Download Military Communications and Information Technology

Military Communications
and Information Technology:
A Trusted Cooperation Enabler
Volume 2
Warsaw 2012
Reviewers:
Prof. Milan Šnajder, LOM Praha, Czech Republic
Prof. Andrzej Dąbrowski, Warsaw University of Technology, Poland
Editor:
Marek Amanowicz
Co-editor:
Peter Lenk
© Copyright by Redakcja Wydawnictw Wojskowej Akademii Technicznej.
Warsaw 2012
ISBN 978-83-62954-31-5
ISBN 978-83-62954-52-0
Publication qualified for printing without editorial alterations made by the MUT
Publishing House.
DTP: Martyna Janus
Cover design: Barbara Chruszczyk
Publisher: Military University of Technology
Press: P.P.H. Remigraf Sp. z o.o., ul. Ratuszowa 11, 03-450 Warszawa
Warsaw 2012
Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Chapter 5
Tactical Communications and Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
SOA over Disadvantaged Grids Experiment and Demonstrator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Frank T. Johnsen, Trude H. Bloebaum, Léon Schenkels and team,
Joanna Śliwa, Przemysław Caban
GUWMANET – Multicast Routing in Underwater Acoustic Networks. . . . . . . . . . . . . . . . . . . . . . . . . 27
Michael Goetz, Ivor Nissen
Network Routing by Artificial Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Michal Turčaník
An Application of Chord Structure in Tactical Ad-hoc Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Jerzy Dołowski, Marek Amanowicz
Revisiting the DARPA’s Idea of a Programmable Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Vladimir Aubrecht, Tomas Koutny
Selection and Investigation of a Civil Wideband Waveform for Potential Military Use . . . . . . . . . . . . 81
Ferdinand Liedtke, Matthias Tschauner, Sarvpreet Singh, Marc Adrat, Markus Antweiler
Experimental Performance Evaluation of the Narrowband VHF Tactical IP Radio
in Test-Bed Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Edward Golan, Adam Kraśniewski, Janusz Romanik, Paweł Skarżyński, Robert Urban
Hybrid Error Detecting and Correcting System Using Hardware Associative Memories. . . . . . . . . . . 107
Ion Tutănescu, Constantin Anton, Laurenţiu Ionescu, Gheorghe Şerban, Alin Mazăre
Concurrent Error Detection Scheme for HaF Hardware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Ewa Idzikowska
Chapter 6
Spectrum Management and Software Defined Radio Techniques. . . . . . . . . . . . . . . . . . . . . . . . . 133
A Realistic Roadmap for the Introduction of Dynamic Spectrum Management
in Military Tactical Radio Communication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Bart Scheers, Austin Mahoney, Hans Åkermark
Dynamic Spectrum Management in Legacy Military Communication Systems . . . . . . . . . . . . . . . . . 151
Marek Suchański, Paweł Kaniewski, Robert Matyszkiel, Piotr Gajewski
Spectrum Issues of NATO Narrowband Waveform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Jan Leduc, Markus Antweiler, Torleiv Maseng
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Legacy Waveforms on Software Defined Radio: Can Hierarchical Modulation
Offer an Added Value to SDR Operators?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Marc Adrat, Tobias Osten, Jan Leduc, Markus Antweiler, Harald Elders-Boll
Data Fusion Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks. . . . . . . . . . . 187
Djamel Teguig, Bart Scheers, Vincent Le Nir
Implementation of an Adaptive OFDMA PHY/MAC on USRP Platforms for
a Cognitive Tactical Radio Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Vincent Le Nir, Bart Scheers
Validation of the ITU 1546 Land-Sea Propagation Model for the 900 MHz Band. . . . . . . . . . . . . . . 215
Krzysztof Bronk, Rafał Niski, Jerzy Żurek, Maciej J. Grzybkowski
Chapter 7
Mobile Ad-hoc and Wireless Sensor Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Algorithms for Channel and Power Allocation in Clustered Ad hoc Networks. . . . . . . . . . . . . . . . . . . 229
Luca Rose, Christophe J. Le Martret, Mérouane Debbah
High Spatial-Reuse Distributed Slot Assignment Protocol for Wireless Ad-hoc Networks. . . . . . . . . . 247
Muhammad Hafeez Chaudhary, Bart Scheers
Hybrid Network Synchronization for MANETs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Harri Saarnisaari, Teemu Vanninen
Application of Dezert-Smarandache Theory for Tactical MANET Security Enhancement. . . . . . . . . 277
Joanna Głowacka, Marek Amanowicz
Mechanisms of Ad-hoc Networks Supporting Network Centric Warfare . . . . . . . . . . . . . . . . . . . . . . . 289
Rafał Bryś, Jacek Pszczółkowski, Mirosław Ruszkowski
Using Network Coding in 6LoWPAN WSNs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
Jarosław Krygier
Testbed Implementation of Energy Aware Wireless Sensor Network. . . . . . . . . . . . . . . . . . . . . . . . . . . 319
Ewa Niewiadomska-Szynkiewicz, Michał Marks, Filip Nabrdalik
An Energy Aware Self-Configured Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
Marcin Wawryszczuk, Marek Amanowicz
Chapter 8
Localization Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
Enhanced Location Tracking for Tactical MANETs Based on Particle Filters
and Additional Information Sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
Peter Ebinger, Arjan Kuijper, Stephen D. Wolthusen
Spatial Localisation of Radio Wave Emission Sources Using SDF Technology. . . . . . . . . . . . . . . . . . . 367
Jan M. Kelner, Piotr Gajewski, Cezary Ziółkowski
On the Effect of Tuner Phase Noise on TDOA Measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
Anders M. Johansson, Patrik Hedström
Aircraft Tracking Using Mobile Devices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
Michał Andrzejewski, Radosław Schoeneich
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
Foreword
Modern military operations are conducted in a complex, multidimensional
and disruptive environment. The challenging political and social environment
of the operations necessitates establishing coalitions, consisting of many different partners of differing levels of trust, e.g. partners from NATO nations, as well
as non-NATO nations and others such as the local government bodies and local
forces. Tight collaboration with these partners and the guarantee that the appropriate information is shared within the community is vital to the mission efficiency.
This also requires understanding of these differences and greater trust as well as
acceptance of the greater risk involved.
Dynamic environmental changes and limitations of the technical infrastructure assets creates additional challenging issues for the effective collaboration
of the coalition partners. The fragile nature of the communications infrastructure, especially at the tactical level, requires robust methods and mechanisms to
deal with long delays, communication failures or disconnections and available
bandwidth limitations.
These all necessitate a better understanding of the environmental conditions
and appropriate procedural actions, as well as strong technological support, to
provide the required levels of interoperability, flexibility, security and trusted collaboration in connecting heterogeneous systems of all parties involved in the action.
Many research efforts aimed at the elaboration and implementation of innovative communications and information technologies for military systems,
enabling trusted information exchange and successful collaboration in disadvantaged environments, have been undertaken world-wide. The latest selected
results of such activities that include novel concepts for military communications and information systems, as well as innovative technological solutions, are
presented in this book.
The book contains the papers originally submitted to the 14th Military Communications and Information Systems Conference (MCC) held on 8–9 October 2012
in Gdansk, Poland. The MCC is an annual event that brings together experts from
research establishments, industry and academia, from around the world, as well
as representatives of the military Communications and Information Systems
community. The conference provides a useful forum for exchanging ideas on the
development and implementation of new technologies and military CIS services.
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Military Communications and Information Technology...
It also creates a unique opportunity to discuss these issues from different points of
view and share experiences amongst European Union and NATO CIS professionals.
The papers included in this book are split into two volumes, each contains
selected issues that correspond to the conference topics, and reflect the technology advances supporting trusted collaboration of all parties involved in joint
operations. The first volume is focused on: Concepts and Solutions for Communications and Information Systems, Communications and Information Technology
for Trusted Information Sharing, Information Technology for Interoperability and
Decision Support Enhancement and Information Assurance & Cyber Defence, while
the latter on the following: Tactical Communications and Networks, Spectrum
Management and Software Defined Radio Techniques, Mobile Ad-hoc & Wireless
Sensor Networks and Localization Techniques.
The editors would like to take this opportunity to express their thanks to the
authors and reviewers for their efforts in the preparation of this book. We trust
that the book will contribute to a better understanding of the challenging issues in
trusted collaboration in modern operations, scientific achievements and available
solutions that mitigate the risk and increase the efficiency of information exchange
in hostile and disruptive environments. We believe that the readers will find the
content of the book both useful and interesting.
Marek Amanowicz
Peter Lenk
Chapter 5
Tactical Communications
and Networks
SOA over Disadvantaged Grids Experiment
and Demonstrator
Frank T. Johnsen2, Trude H. Bloebaum2 , Léon Schenkels and team1, 3,
Joanna Śliwa4, Przemysław Caban4
2
FFI, Norway,
[email protected], [email protected]
3
NC3A, Netherlands, [email protected]
4
Military Communication Institute, Zegrze, Poland,
{j.sliwa, p.caban}@wil.waw.pl
Abstract: The objective of the IST-090 group is to investigate challenges of SOA applications in disadvantaged networks. The group studies possible solutions that can improve the overall efficiency
of information dissemination when facing different disruptions. This paper presents lessons learned
from the real-life experiment and demonstration that was carried out by IST-090 group members
during the MCC 2011 conference. We evaluated several solutions, namely the WS-DDS interface,
the DSProxy, the Mist protocol, and ESBs that were used as SOA solutions enabling efficient information exchange in a disadvantaged environment. The experiment was preceded by separate tests of each
solution. However, in the combined scenario, the aim was to evaluate the interoperability of these
solutions and define a long-term plan for either their application in operations or for further functionality development. The paper gives an overview of the solutions we investigated, presents a rough
model of the network environment used, and discusses the results observed and the lessons learned.
Keywords: component; Web services, publish-subscribe, DDS
I. Introduction
NATO is a strategic organization, and, as such, the vast majority of the communications infrastructure that is used and deployed by NATO is in the strategic
realm of static headquarters and Forward Operating Bases (FOB). These tend to be
served by high speed LAN and WAN communications, with low latency and high
bandwidth. However, under the NATO Network Enabled Capability (NNEC) vision,
the stovepipe systems of the past, offering services and applications to a limited, geographically co-located group of users will shift to a dynamic federation of systems,
which will allow services previously only available within the strategic domain to be
made available in the tactical domain. Likewise, situational awareness information
1
The NC3A team members: Rui Fiske, Marc van Selm, Vincenzo de Sortis, and Aad van der Zanden
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needs to be fed from the tactical domain into the strategic domain to better inform
planning and operational decision making. This necessarily involves increased
transfer of data across tactical links with constrained communications pathways
in both directions. There is no clear solution yet from industry that will support
these requirements and so NATO and the member nations themselves need to devise
suitable mechanisms. The experiment described in this paper is meant to discover
how solutions may be applied in relation to the anticipated use of services in NNEC.
Within the NATO and coalition enterprise, there are a wide range of network
conditions and communications environments. The architects and developers
of most Functional Area Services (FAS), and often those looking at Service-Oriented
Architecture (SOA) in particular, tend to consider only high bandwidth, low latency
local area networks. They find that their applications tend to work well in these
static environments, with low response times, even for large messages. However,
thought also needs to be given to the frontline staff; those connected only between
themselves by Mobile Ad Hoc Networks (MANETs), or by satellite and radio connections back to their FOBs. For these users of systems, how does the NNEC SOA
paradigm deliver the most significant and timely information to them without
the provision of a “LAN to the man”?
The RTO Group, IST-090, has been tasked with looking at the challenges and
potential solutions that can be used for SOA over disadvantaged grids [1]. A number
of NATO member nations were involved in the group, information was shared and
experiments were conducted together, in order to test the various approaches that
had been suggested and developed. A number of IST-090 members were invited
to speak at the Military Communications and Information Systems Conference
(MCC) in Amsterdam in October 2011. As part of the conference, IST-090 was assigned a special area of the conference venue in order to demonstrate the benefits
of the various solutions that had been presented. As part of this, further experiments
were conducted over a dynamic network, provided by Norwegian and NC3A-owned
MANET components. This would provide a real, unreliable network environment
to assess the actual value of the solutions, and allow further experimentation with
the MANETs themselves.
This paper gives a detailed description of the test environment, test plan and test
results that were conducted and observed at the conference. It describes the network,
and various SOA-supporting solutions that were deployed by the group members,
and assesses how each of them provides lessons to consider when designing services and systems to be used over poor communications channels. Many different
potential approaches were identified, many of which could be combined to offer
a range of network-optimization techniques.
The remainder of the paper is organized as follows: In Section II we present
the motivation for pursuing a pervasive SOA in military networks. Related work
is discussed in Section III. Section IV covers our joint experiment – the setup,
the execution, and the lessons learned. Finally, Section V concludes the paper.
Chapter 5: Tactical Communications and Networks
11
II. SOA motivation
SOA, realized by Web services technology, has been identified as the crucial
NNEC enabler [2]. The advantage of SOA is that it provides seamless information
exchange based on different policies and loose coupling of its components. In a military domain it enables making sensitive information resources available in the form
of services, which can be discovered and used by all mission participants that do
not need to be aware of these services in advance.
The most mature technology for implementing SOA, recommended by NATO
and widely applied in the commercial sector, is Web services. Web services are
described by a wide range of standards that deal with different aspects of their
realization, transport, orchestration, semantics, etc. They provide the means to
build a very flexible environment that is able to dynamically link different system
components to each other. These standards are based on the eXtensible Markup
Language (XML) and have been designed to operate in high bandwidth links.
XML gained wide acceptance and became very popular for the reason that it solves
many interoperability problems, is human- and machine-readable and facilitates
the development of frameworks for software integration, independent of the programming language. Nevertheless it undoubtedly adds significant overhead, both
in terms of necessary computation power and consumption of network resources
while being transported. This means that using SOA in tactical networks is challenging and requires optimizations [3], [4]. IST-090 is looking into techniques such
as compression to mitigate some of the challenges [1].
The utilization of Web services and other SOA implementations in a NNEC
environment (e.g., Enterprise Service Buses (ESBs) and the Data Distribution Service
(DDS)) has been addressed in many national and international experiments (e.g., Coalition Warrior Interoperability Demonstration (CWID) [5], [6], DDS demo [7],
ESB experiment [8]). These experiments indicate that the technologies improve
collaboration, interoperation and information sharing in the Federation of Systems
(FoS). DDS is even perceived as real-time technology that can be tailored for the use
in low – bandwidth tactical networks. However the idea of IP-based ubiquitous communications that is able to feed users with data based on the available communications media turns to be difficult to realize at the current stage of available technology
maturity level. In order to achieve efficient information exchange between FoS users,
SOA solutions need to work with different types of information and communication
systems. Service interoperability must be provided among all command levels on
an end-to-end basis. The challenge is therefore to apply SOA in low bandwidth tactical communications systems, which usually cope with high error rates and frequent
disruptions. Such networks are usually referred to as disadvantaged grids.
Previous initiatives have focused on information distribution over disadvantaged grids. In very low bandwidth environments it is considered to be the best
solution to use asynchronous replication based middleware that provides static
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Military Communications and Information Technology...
information distribution between partners that have agreed to use a specific database
format [9]. This solution is however not very convenient in highly dynamic operational scenarios. That is why there has been carried out research on SOA solutions
that provide flexibility and interoperability and are well suited to work in a FoS.
III. Related work
Publish/subscribe, a paradigm for asynchronous communication, has been
identified as particularly useful in MANETs, where it provides decoupling of provider and consumer [10]. In this paper we focus on MANETs, and thus the publish/
subscribe paradigm. The NATO Core Enterprise Services (CES) Working Group [11]
has identified WS-Notification as the standard to use for publish/subscribe in NATO.
Thus, we employ that standard in our experiment in this paper.
Net-Centric Tactical Services (NCTS) provide a gateway and software framework
for tactical users to realize the benefits of information sharing across a SOA environment [12]. It is a software framework which resides in the tactical environment and
supports a set of services and functions to enable communications and messaging
translation, data publishing, data subscription, and tactical device management. This
is in key with the NNEC FS [2], which states that in order to apply SOA throughout
a FoS in various network elements, special devices – so called edge proxies that are to
support information distribution over disadvantaged grids – must be used. The problem is therefore identified and is considered valid for the whole NATO community.
Those edge functionalities are to adapt service traffic to the capabilities of the tactical
networks, sometimes in the form of technology gateways. As a consequence, we also
pursue the gateway and proxy concept in our experiments in this paper.
In [13] we describe a Web services infrastructure experiment performed during
the summer of 2011. This experiment is a precursor to the Norwegian and NC3A parts
of the experiment described in this paper. In 2011 we identified several shortcomings
of the software we were using then, e.g., that the WS-Notification framework we used
on the Norwegian side (WSMG from the University of Indiana) was not suitable for
use in a military environment, since it did not handle disruptions. In the IST-090
experiment described in this paper we have replaced WSMG with Apache ServiceMix in an attempt to leverage a hopefully more mature and stable product. Also,
we now focus mostly on the MANET aspect of communications, whereas the 2011
experiment was more geared towards interconnecting infrastructure.
The SOA over disadvantaged grids experiment and demonstrator discussed
in this paper was linked to the other IST-090 presentations during the MCC 2011
in Amsterdam:
• An overview of the research and experimentation of IST-090 [1]
• Mediation of network load over disadvantantaged grids using Enterprise
Service Bus (ESB) technology [8]
• DDS technology demonstrations related to IST-090 [7]
Chapter 5: Tactical Communications and Networks
13
• Dedicated WS-DDS interface for sharing information between civil and
military domains [14]
• An Evaluation of Web Services Discovery Protocols for the Network-Centric
Battlefield [15]
• An independent evaluation of Web service reach solutions in disadvantaged
grids [16]
• Towards a middleware for tactical military networks – interim solutions
for improving communication for legacy systems [17]
• Semantic Description of Web Service QoS Profiles for Context-aware Web
Service Provision [18]
These papers give an overview of recent IST-090 related work by the member
nations. The remainder of this paper focuses on the aspects of the experiment and
demo performed at MCC 2011.
IV. Experiment
The goal of the experiment was to evaluate the possibility of delivering information from service producers to service consumers in a disadvantaged environment.
The networking environment was set up using equipment provided by the NC3A
and FFI (see Figure 1). Further, technologies studied under the umbrella of IST090 as promising for SOA application on the tactical level were employed: Web
services, ESBs, and DDS. We built a heterogeneous networking infrastructure that
modeled cooperation of users on different levels of command. Information was sent
up and down the echelons of command through the MANET. Thus, we had aspects
of both infrastructure and disadvantaged networks. We evaluated the interoperability of the proposed solutions, as well as their applicability to the scenario that
was developed by the IST 090. For further information regarding the scenario,
please refer to [1]. Moreover such real-life experiments always bring us lessons
learned in terms of implementation correctness and incompatibility of technologies.
A number of potential approaches have been identified by the participants
in this experiment, which may improve communications between service providers and service consumers. This should lead to a concrete set of recommendations
for FAS and Communities of Interest (CoI) when commissioning and developing
their services. Specifically, these approaches are:
• A DDS domain, which is interfaced to a Web services environment through
a gateway (MCI’s contribution).
• DSProxy for delay and disruption tolerant SOAP transport across disadvantaged networks (one of FFI’s contributions).
• Decentralized chat using the experimental Mist protocol, coupled with
a Mist/XMPP gateway for interoperability with infrastructure networks
(one of FFI’s contributions).
• The use of an ESB to optimize the use of bandwidth and manage limited
connectivity (NC3A’s contribution).
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Figure 1. Disadvantaged grid experiment and demonstrator communications network <by NC3A>
Figure 2.SOA infrastructure <by NC3A>
Chapter 5: Tactical Communications and Networks
15
The disadvantaged grid networking infrastructure used was based on equipment provided by NC3A and Norway: Rajant breadcrumb MANET systems using
the 2.4 and 5 GHz ISM bands augmented by a simulated satellite link. The NC3A
provided a complete infrastructure to execute SOA tests and to evaluate the results,
and functioned as the connecting “hub” between Norway and Poland. This infrastructure was built around two ESB instances in charge for the message routing and
transformation (see Figure 2). Here, the interfaces were used as follows:
• SIP3 Engine – This is a processing engine for Friendly Force Tracks in NFFI
V1.3 format that can simulate running tracks, store and filter them. During this test campaign it was used as track store, track simulator and SIP3
interface. The SIP3 Engine is composed of: Track store, Track simulator,
SIP3 message interface (NFFI over SOAP), IP1 message interface (NFFI
over TCP/IP), IP2 message interface (NFFI over UDP), and SIP3/IP1/IP2
interface adapter.
• WSO2 ESB (version 3.0.1) – This is a COTS ESB based on Apache
Synapse. During this test campaign it was used for protocol adaptation
and message routing/conversion. The main services exposed were: a)
SIP3 to KML converter, which was used to visualize on standard KML
enabled map viewer the NFFI V1.3 tracks stored in the SIP3 track store;
b) NVG to KML converter, which was used to visualize on standard
KML enabled map viewer the NVG layer provided by JocWatch; c)
WSNotification (notification consumer) to SIP3 (event sink) which
was used to feed the SIP3 engine with the NFFI tracks published from
the Norwegian pub/sub broker, and d) WS-DDS to SIP3 (event sink),
which was used to feed the SIP3 engine with the NFFI tracks published
from the WS-DDS interface.
• ServiceMix ESB (version 3.5.0) – This is a COTS ESB that offers a WSNotification v1.3 Pub/Sub interface. During this test campaign it was used
to publish messages via the WS-Notification protocol.
• Symbology server – An NC3A service used to graphically represent the units
given their APP6 symbol.
• JOCWatch – The Incident Reporting source systems.
• Google Earth – COTS map viewer used to visualize the tracks produced
and received from the NC3A systems.
Figure 1 shows the network diagram that was used in the experiment and
demonstration.
The experiment was aimed at connecting Web services and DDS domains,
which are technologically different realizations of SOA. Web services support both
publish/subscribe and request/response communications, as opposed to DDS which
has only publish/subscribe support. On the other hand, DDS supports QoS features
and real-time systems, which Web services do not. For a comparison, see Table I.
More details about DDS can be found in [14].
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Table I. Web services and DDS comparison
Property
Standardization
Web services
DDS
W3C, OASIS, WS-I
OMG
Real-time and QoS support
No
Yes
Request/response paradigm
Yes
No
Publish/subscribe paradigm
Yes
Yes
Current standard suitable for disavantaged grids
Enabling support for disadvantaged grids
No
No
Experimental
optimizations
Vendor specific
tactical extensions
Figure 3. Mist as a message ferry <by FFI>
A. Technology components
There is a combination of parameters at hand that will lead to specific use
cases where the following conditions can be tested:
• Different types of information (NFFI, incident reports)
• Different solutions (DDS and DDS/WS gateway, DSProxy, ESB capabilities)
• Different network conditions (mostly making use of the MANET but also
having the possibility to simulate other types of networks).
We evaluated each tested solution’s usability from an end user perspective, and
were able to show the viability of the solutions involved for effectively disseminating the necessary information. It should be noted that DDS was not actually used
in the MANET, it was connected to the MANET using a Web services interoperability gateway. Further information regarding the separate demo components are
discussed briefly below. The complete details of the demo are described in [19].
Chapter 5: Tactical Communications and Networks
1)
17
Mist: The Mist protocol provides robustness and delay tolerance in dynamic
wireless ad-hoc networks. We wanted to use two separate 802.11 ad-hoc networks with one gateway/relay node connected to 100BASE-TX for the Mist/
XMPP experiments. FFI provided the necessary equipment for the mobile
nodes – a mixture of laptops and Sony Ericsson Xperia mobile phones with
WiFi enabled were used. One computer, the server in the national services LAN
where the DSProxy resides, was running the XMPP/Mist gateway software.
Figure 4. NC3A-NOR interconnection featuring ESBs and the DSProxy <by FFI>
The XMPP server could be anywhere in the network, as long as it was reachable by TCP from the XMPP/Mist gateway node from time to time. In this demo
the central server was hosted by the NC3A in the NATO LAN. Messages were
relayed to XMPP when the connection was available and stored for future delivery
when the link was down, using the ”message ferry” principle shown in Figure 3.
The message ferry was a single wireless node (mobile phone) which was used
to carry messages between the two networks. It did not need to be connected
to both ad-hoc networks at the same time, but it did require the use of 802.11
to connect in an ad-hoc manner. For in-depth information on using Mist for
tactical chat, see [20].
2) DSProxy: The use of proxy servers for adapting Web services to disadvantaged grids is a very promising approach. Through the use of proxies one
can utilize unmodified Web services at the client and server machines, and
only the intermediate nodes in the network need to use the proxy software.
This reduces complexity in the development of applications and servers, and
therefore also costs. Norway (FFI) has designed and developed the Delay and
disruption tolerant SOAP Proxy (DSProxy) system.
The DSProxy is a prototype system implementing a range of concepts, ideas and
mechanisms which aim to tackle the challenges associated with utilizing web services
in tactical environments and disadvantaged grids [21]. The DSProxy is a middleware
component enabling delay and disruption tolerant web services for heterogeneous
networks. The Java-based software uses acknowledged standards and is designed
to work with existing COTS Web service clients and services.
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Military Communications and Information Technology...
Functional tests where NC3A provided NFFI data on a WS-Notification interface were shown (see Figure 4). Here, the DSProxy was used to ensure disruption
and delay tolerant SOAP transport across the unstable MANET and highdelay
satellite link, whereas the exchange between the NC3A and NOR was conducted
using ServiceMix to provide WSNotification.
3) DDS/WS Gateway: DDS is a specification of a publish/subscribe middleware for distributed systems created in response to the need to standardize
a data-centric publish/subscribe programming model for distributed systems.
It has been suggested for use on the tactical level [7], but such usage requires
vendor specific extensions. In this paper we focus on interoperability with DDS,
and do not evaluate any tactical extensions. Poland has developed a gateway
to bridge the DDS environment to a Web services environment, focusing on
one specific Web service – blue force tracking using NFFI. This gateway bridging the DDS domain with the rest of the infrastructure is shown in Figure 5.
It enables DDS publishers to deliver NFFI tracks to Web services subscribers
and, in the opposite direction, NFFI tracks published by Web services can be
delivered to subscribers on the DDS side. It should be noted that, when using the DDS/WS Gateway, for every Web service that needs to be bridged to
the DDS domain one needs to implement a special purpose message translator
between the standardized Web services interface and a DDS interface.
B. Results
FFI infrastructure: ServiceMix was used to exchange NFFI tracks between
Norway and NC3A. Tracks from Poland were submitted to the NC3A, which
re-distributed them to Norway. Once the system was up and running everything functioned as expected with WS-Notification, but there were some
initial problems: The version of ServiceMix that was deployed (3.2.3) by FFI
was not fully standards-compliant, leading to some trouble on NC3A’s side.
The problems could be handled by a work-around in the subscription message and some tuning of the WS-Notification client software. This version
of ServiceMix supported only IPv4, meaning that notifications could not be
used over IPv6, even if the communication stack was available. In the future, a newer version of ServiceMix, or another application entirely, should
be considered to provide WS-Notification support.
Mist functioned as it should by delivering chat messages in a disruption tolerant
manner across both IPv4 and IPv6. Also, the message ferry principle functioned
as expected, thereby showcasing one of the strengths of the Mist protocol.
The DSProxy was used to ensure delay and disruption tolerant communication across the „troublesome” networks, i.e., the MANET and the satcom simulator. A bridge that had been developed in-house by FFI was used to interconnect
ServiceMix with the DSProxy, so that notifications could be transported across
1)
Chapter 5: Tactical Communications and Networks
19
the national network to and from the troops. This solution worked as expected
— internally, instabilities were overcome in the network thanks to the DSProxy,
while still retaining external compatibility with NC3A through WSNotification on
the ServiceMix instance. The current implementation of DSProxy has previously
only been used with IPv4, and at this testing event it was not able to run over IPv6.
This is an important finding, and support for IPv6 should be sought for in the future.
Figure 5. WS-DDS is a gateway between the Web services (WS) and Data distribution
service domains <by MCI>
MCI infrastructure: WS-DDS interface enabled the bidirectional exchange
of information between the Polish DDS domain and NC3A’s WS domain.
Additionally, the NFFI tracks from NC3A were sent to the Norwegian domain and back (from FFI through NC3A to POL DDS domain).
Both the WS-DDS interface and the DDS domain emulator operated correctly. Initially there was a problem with interoperability between the WS-DDS
interface and the SIP 3 service. The SIP3 client was created based on the WSDL
1.1.0, whereas SIP3 service was based on WSDL 1.1.9. After recompilation, and
some additional modifications in the structure of the NFFI messages exchanged,
the problem was solved.
The WS-DDS interface version that was tested currently works in the request/
response mode. It would be highly recommended to enhance it with pub-sub functionality based on WSNotification. This will improve the functionality of WS-DDS,
since DDS generally implements a publish/subscribe message exchange pattern.
Furthermore, this will also decrease the time of information transfer and traffic
generated in the WS domain.
3) NC3A infrastructure: The competitive approaches to optimizing traffic using the ESB involve two streams of messages being sent to different services
through the ESB in parallel, a setup further discussed in [8]. One of the services
is of a higher priority than the other, so it is given more resources in the ESB
than the other. The number of successful messages to this service is then
measured. Although the lower priority messages are considered less important,
2)
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Military Communications and Information Technology...
the number of errors in this background stream (3000 messages over 30 secs)
is still recorded to give an impression of the negative impact on this stream.
Two competitive approaches were tested. Prioritization boosts the priority
of the more important message stream, but does not limit the background
messages. Throttling, on the other hand, limits the amount of bandwidth that
a particular service can use, and caps the number of messages that can be
passed to this service.
Table II. ESB Competetive Test Results
Messages
Background
errors
Max
Min
Average
Baseline
32
536
57.3 s
7.7 s
32.6 s
Prioritization
36
601
38.9 s
2.2 s
20.5 s
Throttling
40
2667
54.2 s
3.3 s
29.5 s
Prioritization delivered a small but significant (12.5%) improvement on
the balanced approach, without a massive impact on the background messages.
Although throttling had a more noticeable effect on the higher priority messages
(an improvement of 25%), the effect on the background messages was severe.
The results are shown in Table II. A set of evaluations identifying uncompetitive
approaches was also performed, these results are shown in [19].
It is recommended that a balanced and judicious use of the two approaches
should be considered for use in production. The relative prioritization and throttling parameters will be dependent on the level of service that is offered by the ESB.
C. Lessons learned
The use of SOAP over HTTP provides a number of challenges within an environment where bandwidth is limited. XML is a verbose data format, and the overhead
of the SOAP messaging protocol adds additional bytes to the payload. As was seen
in these experiments, within a MANET environment, where reasonably high
bandwidth is available between the individual nodes on the MANET, this effect
can be negligible.
The use of HTTP can be problematic in case of disruptions due to node
mobility or interference, though. Also, across radio or satellite communications channels, or even a combination of these, then the use of SOAP can cause
timeouts and limit the number of messages that can be delivered. Therefore,
mechanisms need to be defined that can provide any level of improvement
across these network boundaries, and recommendations delivered to the projects
responsible for the delivery of Functional Area and Core Enterprise Services.
Even in simple request-response message exchanges between service providers
Chapter 5: Tactical Communications and Networks
21
and consumers, systems can benefit from the judicious use of disabling HTTP
continue, increasing the number of sockets and lengthening the timeouts on
the client. The suitability of these approaches should also be considered in addition
to mediation through any kind of middleware or proxy. However, middleware
such as ESBs and the DSProxy do help manage poor network communications.
Further refinements, such as switching to other protocols like DDS or JMS,
or even prototypes targeted at unreliable networks like Mist, may offer even
more possibilities. Also, employing compression and other optimization techniques must be considered [1]. Any proposed approach should be considered
on a case-by-case basis, and further research is needed to identify the optimum
approaches for network optimization.
1) Data distribution service: DDS is an emerging middleware that is explicitly
targeted at providing publish/subscribe communications for real-time systems.
It offers fine and extensive control of QoS parameters, including reliability,
bandwidth, delivery deadlines, and resource limits that makes it a candidate
for use in disadvantaged grids. Note, however, that DDS requires vendor
specific extensions to function in such environments [7].
DDS defines a specific API for the messages and subscription handling but
cannot natively interoperate with SOAP Web services. The WS-DDS gateway
is filling this gap by building a SOAP web service interface on the top of the DDS
API so that a standard SOAP data consumer/producer can directly use this interface. The experiment with the WS-DDS gateway was very successful, and future
deployment of DDS could build upon the knowledge gained with this prototype.
A possible option for future experimentation is to leverage an ESB as frontend
for SOAP clients and to configure DDS as a transport protocol (as a substitute for
HTTP). Such a setup could possibly:
• Make use of the DDS strengths for content-centric message handling and
QoS definitions.
• Be a beneficial approach to leveraging COTS middleware.
• Offer the DDS API to a SOAP service.
• Hide the complexity of the DDS protocol and the configuration details
from the SOAP client.
• Offer the possibility to centrally manage QoS parameters for topics and
messages.
• Offer SOAP interfaces that are payload agnostic/independent.
This approach is similar to a widely adopted solution where a SOAP frontend
is using the Java Message Service (JMS) as unified transport protocol. The DDS-ESB
approach must be tested and validated because of the differences between the JMS
and DDS protocols [22]. A possible drawback of having a SOAP payload agnostic
interface is that the content-aware DDS is a strong typed protocol, in order to improve performance. Extensive tests must be made to guarantee that using generic
payloads will not negatively impact DDS performance.
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Military Communications and Information Technology...
Publish/subscribe, DSProxy, and Mist: The use of publish/subscribe mechanisms further offer a range of possible solutions to unreliable network communications. By exchanging messages only in response to clearly-defined
events, the amount of network traffic can be reduced (through the elimination of unnecessary polling), and the effects of intermittent communications
can be lessened. However, particularly in the coalition environment, with
multiple heterogeneous systems, it is important to ensure that any solution
is fully standards-compliant, otherwise there may be interoperability problems,
as were observed with ServiceMix.
It is likely in the future that NATO systems will migrate to an IPv6 infrastructure, and so it is encouraging that IPv6 functions correctly over the MANET.
However, it is important that any applications that are developed or procured
to be used within the NATO enterprise are also able to operate over IPv6.
The version of ServiceMix used by FFI (3.2.3) supported only IPv4, meaning
that notifications could not be used over IPv6, even if the communication stack
was available. It was also an important discovery that the current implementation
of DSProxy was only able to run over IPv4, and FFI will be looking to support
IPv6 in the future.
Mist functioned as expected, and was able to deliver the chat messages in a disruption tolerant manner across the networks. Mist supports both IPv4 and IPv6,
and functioned fully using either protocol. The message ferry principle functioned
adequately as well, showcasing one of the strengths of the Mist protocol.
3) Enterprise service buses: The use of ESBs or other middleware offer a number
of advantages when mediating communications across constrained communications channels. The first and most important of these is that it abstracts
the optimization of the use of bandwidth away from the developers and administrators of the services themselves. To the service consumers and providers,
the network itself should be transparent, and so with the messaging framework
that is used across this network. By using ESBs to manage traffic optimization,
the service consumer calls the ESB using SOAP over HTTP, without having to
consider any other features. Management of the message bus becomes a separate concern, and additional optimization features can be deployed without
the need to redevelop or recompile the applications themselves. In addition
to this benefit, there are a number of features that are supported by ESBs that
can greatly improve performance, and these are discussed in Section IV-B3
above. The use of retries and reliable messaging adds marginal but significant
improvements over a direct call from service consumer to provider. The most
obvious benefit that can be delivered by ESBs is the use of compression for
larger messages, where improvements of over 500 per cent were observed on
a 111 KB message, using only the standard GZIP compression mechanism.
Other XML-specific compression suites promise even higher compression
ratios and therefore even greater improvements.
2)
Chapter 5: Tactical Communications and Networks
23
V. Conclusion
No single solution stood out as the ”magic bullet” to solve all the requirements for high speed connectivity to the edge, but many of them do offer measurable improvements in messaging capability. A number of key success factors were
identified, including the foundation on open standards, ease of management and
configuration, and transparency to the user.
We have shown that the standards are important in delivering interoperability
in heterogeneous FoS environment, but are not sufficient to be used in disadvantaged
grids. The proposed DSProxy standing upon the standard SOAP communication
enables compression of SOAP messages and provides store and forward functionalities if connectivity is lost. The WS-DDS interface creates unique possibility of connecting the WS and DDS architecturally different domains giving the possibility to
exchange messages vertically between the echelons of command. The Mist protocol
is a solution for the XMPP deficiencies enabling it to deliver the chat service on
the tactical level. And finally, the ESBs tuned appropriately give benefits in terms
of compression and priority of messages.
In general, the messaging infrastructure should be optimized for the consumers of services without the need to incorporate proprietary, ad hoc solutions
that will ensure tighter coupling between providers and consumers and therefore
limit the range of potential partners. Where a protocol is not widely understood
in another domain, then gateways should be used to translate from one standard
or protocol to another.
As a suggestion for future experiments, we recommend that the different
potential solutions should be considered individually and collectively to provide
optimized communications across the entire enterprise, from static HQs, with
high speed communications to land forces with intermittent and low quality links
according to a well defined scenario. Each solution should be considered on a caseby-case basis, but once the best solution for the scenario has been identified, then
it should be quick and easy for an administrator to apply, ensuring the best possible
experience for front-line users.
Acknowledgment
The authors would like to thank the organizers of MCC 2011 for making
the experiment and demo possible. Furthermore, we would like to acknowledge
Magnus Skjegstad, who developed the Mist software as part of his ongoing Ph.D.
thesis work, and supported the chat part of the experiment. He also provided us
with Figure 3. Finally, we would like to thank Rolf Rasmussen and Johnny Johnsen
for proofreading.
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Military Communications and Information Technology...
References
[1] P.-P. Meiler et al., “An Overview of the Research and Experimentation of IST-090:
SOA over Disadvantaged Grids,” in proceedings of the MCC 2011, Amsterdam,
Netherlands, October 2011.
[2] M. Booth et al., “NATO Network Enabled Feasibility Study vol. II: Detailed Report
Covering a Strategy and Roadmap for Realizing an NNEC Networking and Information
Infrastructure, version 2.0,” NATO C3 Agency, 2005.
[3] D. Dragolov et al., “Service Oriented Architecture – concept of services,
infrastructure components, implementation practices and military environment
specifics,” in proceedings of the MCC 2009, Prague, Czech republic, September 2009.
[4] F.T. Johnsen, “Pervasive web services discovery and invocation in military networks,”
FFI report 2011/00257, http://rapporter.ffi.no/rapporter/2011/00257.pdf, January 2011.
[5] J. Busch, “An investigation into deploying web services,” TN 1229, NC3A, December 2007.
[6] G. Babakhani et al., “Web Trends And Technologies And NNEC Core Enterprise
Services – Version 2.0,” NC3A Technical Note 1143 (draft), NC3A, The Hague,
Netherlands, December 2006.
[7] I.H. Novo et al., “DDS technology demonstrations related to IST-090,” in proceedings
of MCC 2011, Amsterdam, Netherlands, October 2011.
[8] R. Fiske et al., “Mediation of network load over disadvantaged grids using Enterprise
Service Bus (ESB) technology,” in proceedings of the MCC 2011, Amsterdam,
Netherlands, October 2011.
[9] P. Bartolomasi et al., “NATO Network Enabled Feasibility Study vol. I, ver. 2.0,”
NATO C3 Agency, 2005.
[10] P. Costa et al., “Socially-Aware Routing for Publish-Subscribe in DelayTolerant Mobile Ad Hoc Networks,” IEEE JOURNAL ON SELECTED AREAS
IN COMMUNICATIONS, VOL. 26, NO. 5, JUNE 2008.
[11] Consultation, Command and Control Board (C3B), “CORE ENTERPRISE
SERVICES STANDARDS RECOMMENDATIONS: THE SOA BASELINE PROFILE
VERSION 1.7,” Enclosure 1 to AC/322-N(2011)0205, NATO Unclassified releasable to
EAPC/PFP, 11 November 2011.
[12] S.D. Crane et al., “Bridging the digital divide with net-centric tactical services,”
AFCEA-GMU C4I CENTER SYMPOSIUM ”CRITICAL ISSUES IN C4I”, George
Mason University, Fairfax, Virginia Campus, USA, 20-21 May 2008.
[13] F.T. Johnsen et al., “Towards operational agility using service oriented integration
of prototype and legacy systems,” in proceedings of the 17th ICCRTS, Fairfax, VA,
USA, June 19-21 2012.
[14] P. Caban et al., “Dedicated WS-DDS Interface for Sharing Information Between
Civil and Military Domains,” In proceedings of MCC 2011, Amsterdam, Netherlands,
October 2011.
[15] M. Skjegstad et al., “An Evaluation ofWeb Services Discovery Protocols for
the Network-Centric Battlefield,” in proceedings of the MCC 2011, Amsterdam,
Netherlands, October 2011.
[16] A.H. Landa et al., “An Independent Evaluation of Web Service Reach Solutions
in Disadvantaged Grids,” in proceedings of the MCC 2011, Amsterdam, Netherlands,
October 2011.
Chapter 5: Tactical Communications and Networks
25
[17] C. Barz et al., “Towards a Middleware for Tactical Military Networks Interim Solutions
for Improving Communication for Legacy Systems,” in proceedings of the MCC 2011,
Amsterdam, Netherlands, October 2011.
[18] J. Sliwa et al., “Semantic Description of Web Service QoS Profiles for Context-aware
Web Service Provision,” in proceedings of the MCC 2011, Amsterdam, Netherlands,
October 2011.
[19] P. Caban et al., “SOA OVER DISADVANTAGED GRIDS EXPERIMENT AND
DEMONSTRATOR,” NC3A Reference Document 3342, NATO Unclassified,
The Hague, Netherlands, December 2011.
[20] M. Skjegstad et al., “Distributed Chat in Dynamic Networks,” in proceedings
of the 30th IEEE Military Communications Conference (MILCOM2011), Baltimore,
MA, USA, November 2011.
[21] K. Lund et al., “Robust web services in heterogeneous military networks,” IEEE
Communications Magazine, vol. 48, no. 10, pp. 78-83, October 2010.
[22] Real-Time Innovations Inc., “A Comparison and Mapping of Data Distribution Service
(DDS) and Java Message Service (JMS),” (on-line), http://www.omgwiki.org/dds/sites/
default/files/Comparison of DDS and JMS.pdf, 2006.
GUWMANET – Multicast Routing in Underwater
Acoustic Networks
Michael Goetz1, Ivor Nissen2
1
Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),
53343 Wachtberg, Germany, [email protected]
2
Research Department for Underwater Acoustics and Marine Geophysics (FWG) – WTD 71,
24148 Kiel, Germany, [email protected]
Abstract: Underwater networks move more and more into the focus of the research community,
especially for military purposes. They enable the full integration of underwater components like
submarines or sensor platforms into maritime Network Centric Warfare (NCW). Nevertheless, most
of the scientific work was done in physical layer methods and medium access protocols.
In this paper we introduce a new network protocol called Gossiping in Underwater Acoustic Mobile Ad-hoc Networks (GUWMANET), which realizes medium access and routing functionality
in a cross-layer design. In contrast to other routing approaches for underwater networks, we developed a network protocol from scratch, fitting the special needs of underwater communication,
instead of adopting existing terrestrial protocols.
We use multi-hop strategies to achieve a higher maximum transmission range than that of our
physical layer method. Additionally, multicast addresses are used which allow an unlimited
number of nodes. The routes between the nodes are build up automatically and need no preceding configuration, which allows a full ad-hoc capability including mobile nodes. In combination with the Generic Underwater Application Language (GUWAL), which has a 16 bit header
with the multicast source and destination address, GUWMANET needs only 10 bits additional
overhead. This is realized by using gossiping strategies, where each node itself decides whether
it forwards the data or not.
Keywords: multicast, multi-hop, gossiping, routing, underwater acoustic networks, mobile ad-hoc
networks, emission control, implicit acknowledgment, clustering, bursts
I. Introduction
In the last years, underwater networks received more and more attention
in scientific, industrial and particularly military areas. They enable the full integration of underwater components like submarines, Autonomous Underwater Vehicles
(AUVs), gliders, or bottom sensors into maritime Network Centric Warfare (NCW)
for example in Mine Counter-Measure (MCM) or Anti Submarine Warfare (ASW)
operations. Especially, Underwater Wireless Networks (UWNs) moved into the focus of the research community to enable full flexibility of the platforms without
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Military Communications and Information Technology...
the need for any cables, which are not only expensive but also difficult to handle
and limiting the maneuverability of the moving nodes [1].
The ocean is almost impervious to electro-magnetic waves which makes
them useless for wireless underwater communication over distances greater
than a hundred meters; wireless communication to submerged nodes can only
be realized using sound. The network protocol GUWMANET presented in this
paper, developed by Fraunhofer FKIE and FWG, is designed to establish a robust
mobile ad-hoc UWN working in all-weather and sea conditions. Within this scope
GUWMANET is a possible candidate to fulfill delay tolerant scenarios defined by
the project Robust Acoustic Communications in Underwater Networks (RACUN)
under the European Defense Agency (EDA) [2], [3]. A detailed description of the scenarios follows in Chapter III.
II. Physical layer restrictions
In principle, sound waves can propagate several hundreds of kilometers
in deep waters, nevertheless they underlie natural restrictions which make robust
underwater communication challenging. Due to the fact that the absorption loss
of sound waves increases with frequency, the available bandwidth stays in reciprocal
relation to the maximum transmission range, as shown in Figure 1. In addition,
the weather conditions influence the maximum transmission range, because breaking
waves and rain increase the noise level. To overcome these bandwidth limitations,
we introduce multi-hop strategies in our protocol, which are well-known from
terrestrial Mobile Ad-hoc Networks (MANETs).
Figure 1. Upper bound of the User Data Rate (UDR in bps) over transmission range (km) in deep
and shallow waters and different weather conditions, simplified with homogeneous propagation
conditions. The worse the weather condition, the lower is the expected transmission range
Chapter 5: Tactical Communications and Networks
29
GUWMANET is designed for a physical layer method based on impulse communication, the so called Transient Underwater Acoustic Communication System
(TUWACS) [4]. It sends out short data bursts with a fixed length of 128 bit in 0.3 s
plus 10 bits for a network header. This clarifies that there is no room for much protocol overhead and an underwater network protocol must be as efficient as possible.
Another difference to terrestrial communications is the low and varying sound
propagation speed between 1405 and 1560 meters per second. This does not only
increase the Doppler compensation complexity significantly, but also the signal propagation delay. TUWACS is designed for a maximum internode distance of 10 km.
This distance leads to an optimal frequency band of 3.5−7.5 kHz. A transmission
over a distance of 10 km needs 6−7 s, which must be considered in the protocol
design. A basic assumption of terrestrial protocols is that the transmission time
is much higher than the propagation time. Therefore, terrestrial network protocols
cannot be easily adopted. Instead, a completely new one has to be developed from
scratch to meet the requirements of underwater networks.
III. Scenario
With reference to Figure 2, in the scalable RACUN scenario [5] it is assumed
that an underwater acoustic network is deployed in proximity of a harbor to be
surveilled. All nodes are bottom-mounted and organized in subsequent barriers. These are sets of nodes arranged in a line topology: the first barrier is placed
in front of the harbor, and is composed of the largest number of nodes in order to
ensure the largest sensing coverage along the coast. The distance between nearest
neighbors within a barrier is set to 3 km. The sensing range is assumed to be 2 km.
Every 8 km comes another barrier which can sense movement as well as relay data
towards the cooperating fleet at the sea base. Again, the nodes in the barrier are
arranged in a line topology. While proceeding towards the sea base, the number
of nodes per barrier is reduced from 5 (in the first barrier) to 2 (in the barrier farthest from the shore); in fact, the most important sensing task is carried out near
the coast, whereas the barriers out at sea are key for relaying packets. Nevertheless,
their sensed data are useful to confirm the detections of the first barrier and to give
some further clue about the direction of movement of the boats exiting the harbor.
The network covers a total area of 16 km × 32 km. We recall that the intended
maximum transmission range of a node in our networks amounts to about 10 km,
hence the barriers are typically in range of each other. In this paper, we assume
that two nodes are deployed close to the last barriers: one buoy on the right
side and one ship on the left side. These entities are part of the network, and act
as seaborne sinks.
Although the number of nodes is reduced after each barrier, our network topology features high connectivity, and multiple path alternatives exist. This makes
the network robust against node failures as well as broken links (for example caused
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Military Communications and Information Technology...
by the noise of the boat propellers disturbing the communications). Additionally,
the gateway buoy and the ship stationing at sea are equipped with both acoustic
and radio communication hardware: therefore, as either receives data over acoustic
links, such data is immediately relayed to all other sinks and the cooperating fleet
using radio communications.
Sea base with
cooperating fleet
Last barrier
Ship
Buoy
8km
3km
First Barrier
Surveilled harbor
Figure 2. RACUN harbor surveillance network scenario, with 14 bottom nodes organized in 4 parallel
barriers. Additionally, a ship and a gateway buoy act as sea-borne sinks, as they gather detection information from the network and relay it to the cooperating fleet stationing in the sea base area
The traffic generation pattern in this scenario is inherently event-based, for
example the detection of an intruder by a sensor node. Most of the time there is no
communication in the network. Also the network protocol should work reactively
instead of generating periodically control messages. This saves energy and keeps
the network covert.
IV. Application data
Besides the physical layer restrictions, it is important to know which kind
of data will be transmitted in the network in order to design an appropriate network
protocol. Due to the low bandwidth the application data format must be as short and
efficient as possible. For this propose we specified the so called Generic Underwater
Chapter 5: Tactical Communications and Networks
31
Application Language (GUWAL) [6] which is suitable for any kind of underwater
application. It is based on the following four parcel types:
1. Data Request
2. Data (sensor data, status, GPS position, ...)
3. Command and Control (sleep, move, change mode, ...)
4. Text Message (SMS)
In GUWAL the basic parcel size is 128 bits; but it is also possible to use
variable length if needed. All parcels include an operational header, a checksum
of 16 bits each, and 96 bits payload with variable fields depending on the parcel
type, as shown in Table 1. Among other fields, the header contains a source and
a destination address used for a cross-layer approach. An operational address
is 6 bits long, whereby the first two bits define the type of the node. The following
groups are defined:
1. Gateway Node (buoy/ship with acoustic and radio link)
2. Bottom Node (environment sensor or relay node)
3. Mobile Node (submerged unit: diver, AUV, submarine)
4. Surface and Air Nodes (node without acoustic link like ship, airplane or
operation center via satellite).
Table 1. Format of a GUWAL parcel with source, destination
and priority field as header and a checksum
Position
Length
1-2
2
Parcel Type
Field
3
1
End-to-End Acknowledgment
4
1
Priority Flag
5-10
6
Operational Source Address
11-16
6
Operational Destination Address
17-112
96
Payload
113-128
16
Checksum
The latter four bits are a node identifier to distinguish nodes of the same type,
whereby zero is defined as broadcast to all nodes of the same type. The address
with all bits set to one is reserved for broadcasting to all nodes in the network
regardless of their type. As a result, there are 15 network addresses in groups
1-3 and 14 in group 4, hence 59 in total. In order to allow more than 59 participants in the network, it is envisaged that multiple nodes can share the same
address. For example, it is not mandatory from an operational point of view to
distinguish bottom nodes in the same area, especially if they are only relay nodes.
As a consequence, the network protocol may handle nodes with the same address
as multicast groups. How this is achieved by GUWMANET is explained in detail
in the following chapter.
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Military Communications and Information Technology...
V. Protocol design
In this chapter we introduce our new network protocol GUWMANET. In the first
part, we introduce the Medium Access Control (MAC) and the Automatic Repeat reQuest (ARQ) mechanisms which are used in GUWMANET. Furthermore, the details
of the addressing problem resulting from shared multicast addresses are discussed.
The introduction of a local nickname additional to the GUWAL network address
is motivated. At the end we describe the initialization steps and the routing algorithm,
followed by an extension for improvements based on network-coding strategies.
A. Medium Access Control
The MAC layer manages the access to the acoustic communication channel.
This can be done in a contention-free manner like code, space, frequency or time
division multiple access or randomized, accepting possible collisions in the shared
medium water. We decided to apply a time-based random access method, which
is much more efficient than fixed divisions in networks with event based and bursty
traffic and a physical layer using short impulses, as in underwater scenarios. A detailed survey of MAC mechanisms with regard to underwater acoustic networks
can be found in [7].
In random access methods every node can access the sound channel whenever
it has data to send while regarding specific rules. The methods can be subdivided
into two types, with previous channel reservation or direct data transmission.
In our case channel reservation is inefficient, because the data packets consisting
of 128 bits are already very short and a reservation would take a long time due to
Round Trip Times (RTT) of up to 15 s having internode distances of 10 km. Moreover,
the transmission times are with 0.3 s short compared to the long propagation delay.
Most of the time, nodes wait for incoming data packets instead of transmitting.
This is an important distinction to terrestrial networks.
Another consideration is to apply carrier-sensing before transmitting data.
However, sensing the medium prior to a transmission does not allow drawing
conclusions about the channel state at the receiver side several seconds later when
the signal arrives. Hence, we use a simple ALOHA [8] method without carriersensing in GUWMANET. Nevertheless, we point out that underwater acoustic
communication is half-duplex. It is not possible to transmit data during a reception or vice versa.
B. Automatic Repeat reQuest
Using a random access method like ALOHA means that packet collisions may
occur, even if the transmissions itself are very short. Additionally, bit errors due
to high noise or low signal strength can result in packet losses. Therefore, ARQ
Chapter 5: Tactical Communications and Networks
33
methods have to be used to guarantee a successful reception. Typically, the receiver
node sends an acknowledgment packet back to the transmitter to indicate a correct
packet reception. If an acknowledgment stays out, the source node will automatically repeat the packet after a predefined period of time.
The application language GUWAL already supports end-to-end acknowledgments, which are sent by the final destination node as operational notice of receipt.
This acknowledgment is optional and is requested by setting the acknowledgment bit in the header.
In multi-hop networks it is reasonable to add additional link layer packet
repetition mechanisms, which will detect packet losses at each hop. In wired networks explicit packets have to be sent to inform the transmitter about a successful
reception. In MANETs this can be done in an implicit way without additional
transmissions due to the shared medium in wireless multi-hop networks. The source
node is able to overhear if the next hop forwards the packet and therefore gets a so
called implicit acknowledgment. If the packet will not be forwarded, the source
node will automatically repeat the packet until it overhears a packet forwarding attempt. In consequence, the destination node must also repeat the packet to inform
the previous hop about the successful reception.
In GUWMANET this implicit acknowledgments are used with exponential
backoff timers for packet error control as described later in Section G. Additionally, our network protocol is delay tolerant and supports so called Emission Control
(EMCON), which means that nodes can decide to stay silent for an arbitrary time
period. This is for example important for submarines, which do not want to get
detected due to transmissions. Therefore, it is possible that acknowledgments stay
out, even the transmission was successful. A packet is repeated with exponential
backoff to attempt to deliver the packet successfully, but it cannot be guaranteed
that the packet was received correctly. It is also not possible to make assumptions
like a node is broken even if it did not reply for a longer period. Nevertheless, for
this period where a node stays in EMCON state it is not available for packet forwarding and related routes have to be updated.
C. Addressing
As mentioned earlier, each network address can be used as multicast group
including multiple nodes of the same type. From operational view, it is not mandatory to distinguish nodes inside this group, but for the network protocol it is
mandatory to facilitate forwarding mechanisms. Therefore, we introduce a second
local address of 5 bits in addition to the global 6 bit operational network address,
which is independent from the network address and the node type. In the following
this local address is called nickname.
To allow full ad-hoc capability, the local nicknames are not predefined and
have to be chosen automatically after network deployment. In our MANET exists
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Military Communications and Information Technology...
no master node coordinating the address allocation, therefore each node has to
choose its nickname by itself. These should be unique in the local 2-hop neighborhood to allow all nodes to distinguish between its neighbors. Typically, this
is done making a neighborhood discovery, where the new node requests lists with
neighborhood information from all its neighbors. We propose another algorithm to
reduce the number of transmissions and thereby the energy consumption, because
battery power is a limited resource in underwater scenarios.
The idea is to overhear at first the network traffic for a listen period TL. If during this period communication takes place in the neighborhood, the node becomes
acquainted with its neighbors and learns their nicknames passively. Additionally,
it gets information about its 2-hop neighborhood, because the network header
of GUWMANET includes beside the transmitter’s nickname also the nickname
of the last hop, as described later. After TL the node knows a subset of its 2-hop
neighborhood and chooses randomly a presumably free local nickname. Now
it transmits to its local neighborhood a nickname notification (NN) parcel to indicate
its presence and nickname choice. The chosen nickname is included in the network
header as source address. All other information is included inside a special GUWAL
parcel. GUWAL has reserved a separate data type for such network control parcels
where each network protocol can define up to eight individual control parcels.
The network control parcel NN of GUWMANET includes the predefined
multicast GUWAL address of the node, its unique 16 bit MAC address and a list
with up to 10 local nicknames of its by now known 1-hop neighbors, as shown
in Table 2. Typically, underwater networks are sparse; anyway, multiple NN packets
are sent if there are more than 10 neighbors. The NN parcels are also used for
network initialization described in the next section.
Table 2. Format of a Nickname Notification (NN) parcel included in a GUWAL Data frame
Position
Length
1-2
2
Parcel Type (Data)
Field
3
1
End-to-End Acknowledgment (no)
4
1
Priority Flag (low)
5-10
6
Source Address (own address)
11-16
6
Destination Address (broadcast = 1111112)
17-20
4
Data Type (Network Control)
21-23
3
Network Control Type (NN)
24-42
19
Timestamp
43-92
50
Nicknames of 10x 1-hop Neighbors
93-108
16
MAC (own address)
109-111
3
unused
113-128
16
Checksum
Chapter 5: Tactical Communications and Networks
35
If any of the neighbors receives a NN and has an objection, because it already
has a neighbor with this nickname, it replies with a nickname collision notification (NCN) parcel which has the same fields as the NN parcel. But instead of using the own 16 bit MAC address the one of the discovering node is used which
was included in the corresponding NN parcel. This allows the discovering node to
extend its 2-hop neighborhood list and to choose another free nickname. Before
transmitting a new updated NN parcel the node waits a period TC to collect further
NCN parcels if such were send. In the case a node receives an NN parcel in which
it is not included in the 1-hop neighborhood list, it automatically sends its own
NN parcel to inform the other node about its presence, if it has not already sent
an NCN parcel for that nickname notification.
NCN parcels are repeated with a binary exponential backoff algorithm until
a new NN parcel is received or a limit of Lrep is reached. An exponential backoff
is used to allow fast repetitions to correct simple packet errors at the beginning
and late repetitions to handle (temporally) asymmetric or broken links. NN parcels are not repeated automatically, only after the incoming of NN or NCN parcels
of neighbor nodes.
Although the probability is low, it is possible that nickname collisions stay
undetected. This may occur due to packet losses, asymmetric or temporally broken links during the nickname allocation, or mobile nodes moving to other areas.
Therefore, the network protocol is designed to tolerate double nickname occupancies. This only leads to redundancies during packet forwarding as will be shown
later. If a nickname collision is detected later, the detecting node will try to fix this
by sending an NCN parcel, followed by the same procedure as described above.
D. Initialization
In our network protocol each node among the nickname needs some initial
network control information. This information is exchanged automatically with
an initialization parcel after a node deployment to allow full ad-hoc capability.
This parcel includes for example a 32 bit UNIX timestamp. The timestamp is used
as reference time for a shortened 19 bit timestamp which is used in GUWAL, see
Table 2. It allows date stamping inside a three month operating period with an accuracy of 15 s.
Due to the limited battery power resulting in an operation period of three
month, inaccurate clocks with a clock drift of one second per week and a lack of synchronization it is not necessary to have a longer timestamp with higher accuracy.
Before a node can use this shortened GUWAL timestamp it must know the starting
point regarding to the UNIX timestamp where the GUWAL timestamp is zero.
The initialization parcel is send out as reply to parcels with a timestamp set
to zero, here the NN parcel. After the node received the initialization parcel it is
ready for use.
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Military Communications and Information Technology...
E. Routing
In this section we describe the routing strategies of GUWMANET in detail.
As mentioned before, multi-hop strategies and multicast addresses are used to
fulfill the operational needs of an underwater network. Besides this, an important
design aspect was to have as less overhead as possible due to fixed burst length
of the underlying impulse communication.
The basic idea behind GUWMANET is to leave the decision whether to forward a parcel to the nodes themselves, comparable to the behavior when gossiping.
A node hears a parcel/gossip and decides if there are nodes in the neighborhood
which may be interested in this information. If any node repeats everything we have
simple flooding, but this is very inefficient and wastes a lot of energy. Therefore,
the nodes in our network learn passively if they are on the direct route to the destination. As already mentioned, we need only 10 bits additional network protocol
overhead for this purpose. The 5 bit local nickname of the transmitter and during
route establishments the nickname of the last or next hop.
In the beginning, the nodes have only information about the topology in the local two-hop neighborhood. To get global topology information, each node stores
all incoming packets:
PD×H×T×S
whereby D represents the 128 bits GUWAL data packets, H the network headers,
T the local receive times and S the estimated Signal-to-Noise Ratio (SNR).
After a listen period TL plus an additional random backoff time TBackoff each
node forwards the data packet d, even if it is addressed itself as destination, because
there might exist more nodes in the network with the same operational network
address (multicast). The forwarding node puts its own nickname into the source
address field of the network header. For the last hop field all received parcels with
data content d in M are considered:
Pd: ={(d', h', t', s')  P | d' = d}
In this subset all parcels with a SNR lower than a threshold SNRmin are filtered
out:

Pd : {(d ', h ', t ', s ')  Pd | s '  SNRmin }
As last hop field the own nickname is chosen if the node is the first transmitter or the nickname of the transmitter of the first received parcel m in Pd* or,
if Pd* is empty, the parcel with the highest SNR is chosen:
 p '  Pd* : p "  Pd* : t '  t ", if Pd*  



P 
 p '  Pd : p "  Pd : s '  s ", else



If a node overhears that it was elected as last hop inside a forwarded message
of one of its neighbors, it generates a temporary routing entry with the two 6 bits
Chapter 5: Tactical Communications and Networks
37
source and destination addresses included in the data part and the physical addresses of the last and next hop.
All addressed destination nodes receiving the packet have to reply with
an acknowledgment parcel. With the help of the temporary routing entries
the acknowledgment packet can be routed back to the source node. Each node
that was elected as last hop knows that it is on the direct way back to the source
node and responsible for data forwarding. All nodes being involved in the back
routing process convert the temporary routing entries into permanent ones and
will now forward all data packets with the same GUWAL source and destination address tuple. We emphasize that after this process only the routes from
the source node to the destination nodes are learned but not vice versa, because
there might be more nodes with the same address like the source node which
are not considered yet.
After a route is established, all following data transmissions are sent with the last
hop field set to zero. This indicates that there is a known route and all other nodes
without permanent routing entries should not flood this message again. If the route
gets broken due to node or link failures, the last hop field is reused again as before,
which initiates a new route discovery with flooding.
Mobile nodes like submarines and AUVs possess a special role during the route
discovery process. Due to their mobility, they are bad candidates for static routes.
Therefore, they are only elected as last hops if there exist no alternatives after
an additional waiting period of Tmob. Also, the established routes to or from mobile nodes have a limited lifetime. If a node has not forwarded a message during
a predefined time period Tlife the routing entries are discarded and a new routing
process is started, because it is unlikely that this route still works. Mobile nodes
can be easily identified by their operational address; they have the group number 3
as defined in Chapter IV.
We explain in the next section with a simple example how the above described
route establishment works.
F. Route establishment example
Figure 3 shows an example topology whereby each dot represents a node
and each line a connection between two nodes. The number above each node
represents its local nickname, whereby the apostrophes are only for distinction
in this description. In reality the nicknames 1 and 1’ are equal. The local nicknames
should only be unique in the two-hop neighborhood if possible. In the following,
we explain how the route establishment works; if node 1 with the GUWAL address
A wants to transmit data to node 4’ with the GUWAL address B.
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Military Communications and Information Technology...
Figure 3. Example network topology with local nicknames
First of all, node 1 broadcasts its data packet. Thereby, the source nickname
and the last hop field inside the network header are set to 1:
1 → [1,1] + [A → B: DATA]
In the beginning, no nodes have routing information. Therefore, all neighbors
of node 1 which receive this message forward the data. The parcel itself stays steady,
whereby the source nickname of the network header is replaced by the nickname
of the forwarding node. As last hop the nickname of node 1 is chosen, as shown
in Figure 4.
2 → [2,1] + [A → B: DATA]
In this way, the message is flooded through the network.
Figure 4. Node 2 rebroadcasts the data of node 1. Node 1 overhears that it was chosen as last hop
and creates a temporally routing entry
Figure 5. Temporally routes (see Table 3) used to send back the acknowledgment.
On the way back, the routing entries get persistent
During this flooding process each node overhearing a message with a last hop
nickname equal to its own creates a routing entry in a temporally routing table,
as shown in Table 3.
Also the addressed node 4’ with the GUWAL address rebroadcasts the data parcel, because there might be more nodes with the GUWAL address B in the network:
4’ → [4,3] + [A → B: DATA]
Chapter 5: Tactical Communications and Networks
39
After this, node 4’ sends out an acknowledgment parcel which is forwarded
back to node 1 using the temporally routing tables. During the complete back
forwarding the last hop field is set to zero:
4’ → [4,0] + [B → A: ACK]
Table 3. Temporally routing entries of all nodes
Node
From
To
Next
Node
From
To
Next
1
A
B
2
7
A
B
8
1
A
B
3
8
A
B
6’
1
A
B
4
3’
A
B
4’
1
A
B
5
5’
A
B
7’
1
A
B
6
6’
A
B
2’
1
A
B
7
7’
A
B
1’
2
A
B
3’
4’
A
B
8’
3
A
B
5’
Due to the empty last hop field, the message is not flooded back. Instead,
only node 3’ forwards the acknowledgment back, because it has a temporally
routing entry after overhearing his election as last hop from node 4’ in the previous transmission. This is repeated until the acknowledgement reaches node
1 and a complete persistent route is established. Even, if there is more than one
destination node, all routes are learned simultaneously. Additionally, we point out
that it is necessary to store the data parcel itself in each temporally routing entry,
to distinguish parallel route discoveries. The acknowledgment parcel includes
the 16 bit checksum of the data parcel which allows an association of the ACK
with the temporary routing entry.
As mentioned before, this route is valid for transmissions from A to B only
and not vice versa, because the GUWAL address of A is not necessarily used
as unicast address.
G. Packet loss and broken link handling
In general, the probability of collisions in the underwater channel is very low;
even though flooding creates a lot of redundancy during the route establishment
phase. That is because the transmission time is very low compared to the propagation time; in consequence the channel is idle most of the time and not occupied
with an ongoing transmission. In our underlying impulse communication we have
a transmission time of 0.3 s for a GUWAL parcel whereas the propagation time
can be up to 6−7 s. This is a fundamental difference to terrestrial networks where
the data rate is much higher and the signals propagate with light speed.
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Military Communications and Information Technology...
On the other side, the bit error rate of an underwater channel can be very
high, depending on weather conditions and noise. Therefore, packet losses must be
handled. As mentioned in the previous chapter we use implicit acknowledgments
to indicate successful transmissions from hop to hop. If a packet forwarding from
a neighbor stays out, the message will be repeated. In GUWMANET a message
is retransmitted up to 5 times with an exponential backoff. After this, the link
is declared as broken and the neighbor is removed from the neighborhood list.
If a link is broken during a forwarding process on an established route, the route
has to be updated. As already mentioned, this is done by using the last hop field.
The neighbor nodes will flood the message again, if the last hop field is nonzero.
This results in a new route discovery process to renew the broken route.
Nevertheless, these single routes without redundancy are very vulnerable to
temporally link failures or asymmetric links. To overcome this problem, we introduce in the next section an enhancement of GUWMANET, which adds additional
redundancy along a route if necessary.
H. Corridor as route enhancement
In this section we introduce a modification of GUWMANET to enhance
the packet delivery ratio during bad weather conditions. Rain and breaking waves
on the surface increase the channel noise significantly which leads to a much
higher bit error rate. To overcome this problem, additional redundancy is generated by involving the neighbors along the direct route. We call this enlarged route
corridor, which is illustrated in Figure 6.
Figure 6. Corridor as route enhancement for a higher packet delivery ratio
For this objective, the route discovery process is modified in the following
way. If a data parcel reaches one of its addressed destination nodes it will send
out an acknowledgement as usual. But this time, the last hop is not left empty
anymore. Instead, it is used in acknowledgment parcels as next hop field, wherein the transmitter puts its elected previous hop. This node is intended to forward
the acknowledgment back to the source node. Now, not only this elected next
hop will forward the packet, but also all nodes having this elected node as direct
neighbor. This is like gossiping in the real world; gossip is circulated if you know
your neighbor is interested in it too.
Chapter 5: Tactical Communications and Networks
41
Figure 7 shows an example. Node 4’ has elected node 3’ as next hop, whereby
node 6’ and node 7’ knowing node 3’ and 4’ participate as corridor nodes in the forwarding process. Multiple receptions at node 3’ allow utilization of network-coding
strategies, as described later in Chapter VII. This technique allows restoring messages, even if all single transmissions are error-prone.
Figure 7. Adding next hop during back forwarding of an acknowledgment
To avoid unnecessary transmissions, the additional corridor nodes only forward the data if the node on the direct route did not. For this purpose, we introduce
an additional backoff Tcorridor to the normal backoff Tbackoff combined with the link
quality Lquality which is between 0 and 1:
Tforward = Tbackoff + Tcorridor . (2 − Lquality)
The timer is canceled if the node overhears a transmission of the node on
the direct route or reset if the message was repeated first by another neighbor.
During the back forwarding of the acknowledgment the nodes on direct node
make their persistent routing entries as before. But this time, also the nodes on
the corridor make persistent routing entries, which indicate that they are jointly
responsible to forward data parcels. Note, during data forwarding the next/last
hop is still not used and left empty as before. The packet forwarding will be only
decided with the usage of the persistent routing entries.
VI. Evaluation
Sea trials are costly due to the need of expensive equipment and personal. Therefore, we developed an underwater acoustic emulation system to test GUWMANET
in an environment as close as possible to real hardware. This emulation test bed
uses a real acoustic channel with the same physical layer methods as underwater.
The only difference is, that the communication takes place in air instead of water.
Insulation material is used to model the absorption losses of sound waves, which
allows the modulation of a scenario of 8 km × 30 km. Only the propagation delay
is artificially created by delaying incoming transmissions.
A cluster of 10 computers equipped with microphones and loudspeakers are
used to emulate the network nodes. They are arranged in a topology similar to
the RACUN scenario described in Chapter III with 4 barriers consisting of 9 bottom nodes and an additional mobile node.
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Military Communications and Information Technology...
We implemented GUWMANET inside this emulations system and made
a concept study of the routing mechanisms and first performance analyses. The route
establishment algorithms were tested with 3 hops and multicast addresses. These
emulations are the first step of the evaluation and will be enlarged to additional
simulations and sea trials.
VII. Network-coding
In underwater acoustic networks high bit error rates are common. Therefore,
error detection and correction techniques have to be used to enable reliable communications. Common ways are the use in the form of so called checksums in combination with ARQ and Forward Error Correction (FEC) mechanisms in the physical
layer methods. If a packet error is detected, the simplest way is to drop the packet
and wait/ask for a retransmission. But retransmissions waste valuable energy, occupy the channel and increase the packet delay.
A much more efficient way is to use a posteriori error correction techniques
like clustering of all incoming short 128 bit parcels. The idea behind this is to merge
multiple corrupted parcels and merge them into correct one. In real life gossiping
messages contains lies; the gossip average results in true facts. The network-coding
approach with clustering techniques was proposed in the RACUN project [9] and
is an essential part of gossiping.
VIII. Conclusion
In this paper we introduced a new network called Gossiping in Underwater
Mobile Ad-Hoc Networks (GUWMANET). This protocol is designed from scratch
fitting to the special needs of underwater communication. It is designed to fulfill
the requirements of the scenarios defined in the RACUN project; an European project
for robust underwater communication, supporting stationary as well as bottom nodes.
GUWMANET is based on impulse communication as physical layer method,
which sends out short data bursts with a fixed length of 128 bit. This makes it necessary, that our protocol gets along with only 10 bit additional protocol overhead.
These 10 bits are used for two 5 bit local nicknames, identifying the transmitter
node and the last hop. With the help of these two fields a persistent route can be
found, even if there are multiple destinations. We also defined the Generic Underwater Application Language (GUWAL) to overcome the restrictions of 128 bits for
application data.
At least, we introduced a protocol enhancement using corridors for packet
forwarding. These corridors generate additional redundancy if necessary and enable network-coding strategies to restore error-prone messages. This is done by using clustering algorithms, which can be used to the low message size of 128 bit and
the low number of packet transmissions in underwater networks.
Chapter 5: Tactical Communications and Networks
43
IX. Future work
As mentioned in the evaluation chapter, we already made first concept studies
and analyses of GUWMANET. The next steps are simulations with the simulation
framework DESERT Underwater, an NS-Miracle-based [11], [13] framework to
DEsign, Simulate, Emulate and Realize Test-beds for underwater network protocols [10]
developed by the University of Padova. This framework allows us to enlarge our
studies to higher node numbers as were foreseen in the RACUN scenario. Beside
this, GUWMANET can be compared with other network protocols which are
already implemented in DESERT underwater.
After detailed simulation and emulation studies sea trials are planned to
demonstrate the capability of our network protocol to fulfill the requirements
of the RACUN scenario.
X. Acknowledgment
We gratefully acknowledge the partners of the project Robust Acoustic Communications in Underwater Networks (RACUN) for their helpful discussions and
advices. The RACUN project is part of the EDA UMS program (European Unmanned Maritime Systems for MCM and other naval applications), and is funded
by the Ministries of Defence of the five participating nations Germany, Italy, Netherlands, Norway, Sweden. Partners of this project are: Atlas Elektronik (Germany),
WTD71-FWG (Germany), L-3 Communications ELAC Nautik (Germany), TNO
(The Netherlands), Kongsberg Maritime (Norway), FFI (Norway), FOI (Sweden),
SAAB (Sweden), WASS (Italy) and CETENA (Italy).
References
[1] I.F. Akyildiz, D. Pompili, T. Melodia, “Underwater Acoustic Sensor Networks:
Research Challenges”, Ad Hoc Networks, vol. 3, no. 3, pp. 257-279, May 2005.
[2] European Defense Agency (EDA) Project Arrangement No. B0386, http://www.
racun-project.eu
[3] J. Kalwa, “The RACUN-project: Robust acoustic communications in underwater
networks – An overview”, OCEANS, 2011 IEEE – Spain, pp. 1-6, 6-9 June 2011, DOI:
10.1109/Oceans-Spain.2011.6003495.
[4] I. Nissen, “Alternativ Ansatz zur verratsarmen Unterwasser-kommunikaton durch
Verwendung eines Transienten im Kontext von IFS und JUWEL”, FWG Research Report
IB 2009-3; Research Department for Underwater Acoustics and Marine Geophysics
(FWG) – WTD 71, Kiel, 2009.
[5] M. Goetz, S. Azad, P. Casari, I. Nissen, M. Zorzi, “Jamming-Resistant Multipath Routing for Reliable Intruder Detection in Underwater Networks”, Proceedings
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Military Communications and Information Technology...
of the Sixth ACM Inernational Workshop on Underwater Networks, WUWNet’11,
Seattle, Washington, USA, Dec. 1-2, 2011. DOI: 10.1145/2076569.2076579.
[6] I. Nissen, M. Goetz, “Generic Underwater Application Language (GUWAL) –
A Specification Approach”, Research Report WTD71 – 0161/2010 FB, Kiel, Germany,
20.12.2010.
[7] R. Otnes, A. Asterjadhi, P. Casari, M. Goetz, T. Husøy, I. Nissen, K. Rimstad,
P. van Walree, M. Zorzi, “Underwater Acoustic Networking Techniques”, SpringerBriefs
in Electrical and Computer Engineering, DOI: 10.1007/978-3-642-25224-2_1, 2012.
[8] N. Abramson, “Development of the ALOHANET”. IEEE Transactions on Information
Theory, 31(2):119-123, 1985.
[9] I. Nissen, M. Goetz, T. Wiegmann, T. Schäl, “Clustering of tactical underwater
messages”, Research Report WTD71-0065/2012, Kiel 2012. In Preparation.
[10] R. Masiero, S. Azad, F. Favaro, M. Petrani, G. Toso, F. Guerr, P. Casari, M. Zorzi,
“DESERT Underwater: an NS-Miracle-based framework to DEsign, Simulate, Emulate
and Realize Test-beds for Underwater network protocols”, Oceans 2012, Yeosou, Republic
of Korea, May 2012.
[11] The Network Simulator – ns-2, Last time accessed: March 2012. Available:
http://nsnam.isi.edu/nsnam/index.php/User_Information
[12] The DESERT Underwater libraries – DESERT, Last time accessed: March 2012.
Available: http://nautilus.dei.unipd.it/desert-underwater
[13] The Network Simulator – NS-Miracle, Last time accessed: March 2012. Available:
http://dgt.dei.unipd.it/download
Network Routing by Artificial Neural Network
Michal Turčaník
Department of Informatics, Armed Forces Academy, Liptovský Mikuláš, Slovakia,
[email protected]
Abstract: Author presents a design of the artificial neural network for routing in the sensor network
in this paper. The routing table is replaced by artificial neural network. The main aim is to realize this
operation as fast as possible. Optimized neural network is implemented in the reconfigurable hardware. The paper concludes with possible future research areas.
Keywords: artificial neural network, FPGA, network routing
I. Introduction
Sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data
dissemination protocols have been specially designed for sensor network where
saving energy is an essential design issue. The focus, however, has been given to
the routing protocols which might differ depending on the application and network
architecture.
A new approach has been proposed for the routing problem in the sensor
network in this paper. The routing table is replaced by artificial neural network
in this approach. The main aim is to realize this operation as fast as possible.
In this paper author used offline learning method. Offline training regards to
learning procedure on a general-purpose computing platform before the trained
system is implemented in hardware. The software that was chosen for offline training
is MATLAB. Also was used Xilinx ISE to synthesize of VHDL code and simulate.
This paper is organized as follows: part II introduces the sensor network topology. Part III presents artificial neural network (ANN). Using ANN for network
routing and ANN optimization is presented in part IV. Part V presents the results
and discussion about results, and finally part VI concludes the paper.
II. Sensor network topology
Networking unattended sensor nodes may have profound effect on the efficiency of many military and civil applications such as target field imaging, intru-
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Military Communications and Information Technology...
sion detection, weather monitoring, security and tactical surveillance, distributed
computing, detecting ambient conditions such as temperature, movement, sound,
light, or the presence of certain objects, inventory control, and disaster management.
Deployment of a sensor network in these applications can be in random fashion
(e.g., dropped from an airplane) or can be planted manually (e.g., fire alarm sensors
in a facility). For example, in a disaster management application, a large number
of sensors can be dropped from a helicopter. Networking these sensors can assist
rescue operations by locating survivors, identifying risky areas, and making the rescue team more aware of the overall situation in the disaster area [1].
Figure 1. Sensor network (R – router, S – sensor)
In the past few years, an intensive research that addresses the potential of collaboration among sensors in data gathering and processing and in the coordination
and management of the sensing activity were conducted. However, sensor nodes
are constrained in energy supply and bandwidth. Thus, innovative techniques that
eliminate energy inefficiencies that would shorten the lifetime of the network are
highly required. Such constraints combined with a typical deployment of large
number of sensor nodes pose many challenges to the design and management sensor
networks and necessitate energy-awareness at all layers of the networking protocol
stack. For example, at the network layer, it is highly desirable to find methods for
energy-efficient route discovery and relaying of data from the sensor nodes so that
the lifetime of the network is maximized [2].
III. Artificial neural network
Artificial neural networks have been trained to perform complex functions
in various fields, including pattern recognition, identification, classification, speach,
Chapter 5: Tactical Communications and Networks
47
vision, and control systems [9, 10, 11]. Neural networks are composed of simple
elements operating in parallel. These elements are inspired by biological nervous
systems. As in the nature, the connections between elements largely determine
the network function. A neural network can be trained to perform a particular
function by adjusting the values of the connections (weights) between elements.
Typically, neural networks are adjusted, or trained, so that a particular input leads
to a specific target output. There, the network is adjusted, based on a comparison
of the output and the target, until the network output matches the target. Typically,
many such input/target pairs are needed to train a network [3].
Many variations of the perceptron were created by Rosenblatt [4]. One of the simplest was a single-layer network whose weights and biases could be trained to produce a correct target vector when presented with the corresponding input vector.
The training technique used is called the perceptron learning rule. The perceptron
generated great interest due to its ability to generalize from its training vectors and
learn from initially randomly distributed connections. Perceptrons are especially suited
for simple problems in pattern classification. They are fast and reliable networks for
the problems they can solve. Feedforward networks often have one or more hidden
layers of sigmoid neurons followed by an output layer of linear neurons. Multiple layers of neurons with nonlinear transfer functions allow the network to learn nonlinear
relationships between input and output vectors. The linear output layer is most often
used for function fitting (or nonlinear regression) problems [7].
A multi-layer perceptron (Fig. 2) consists of neurons and synapsies (connections).
Each neuron has an input, activation and output function. Each synapsy between
two neurons has a weight. The units are organized in layers. Three different types
of neurons are distinguished: input neurons, hidden neurons and output neurons.
The input units receive the input data, and the output units provide the output [8].
Figure 2. Multilayer perceptron
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The calculation of the final output values proceeds layer by layer. First, the input
signals are applied to the input layer, and each neuron of the input layer calculates
its output value. Next, these values are propagated to the next layer; until the output
layer is reached.
IV. Using the artificial neural network for network routing
A. Clasical table look-up aproach in the sensor network
The primary role of routers is to forward data towards their final destination.
A router must decide for each incoming packet where to send it next. More exactly,
the forwarding decision consists in finding the address of the next-hop router as well
as the egress interface through which the packet should be sent. This forwarding
information is stored in a forwarding table that the router computes based on the information gathered by routing protocols. To consult the forwarding table, the router
uses the packet’s destination address as a key; this operation is called address lookup.
Specifically, the address lookup operation is a major bottleneck in the forwarding performance of today’s routers. This paper presents a proposal a new method
to realize algorithm for efficient address lookup.
B. Artificial neural network topology
Neural networks map between a data set of numeric inputs and a set of numeric targets. To solve a problem of routing in the mash topology of the sensor
network (Fig. 1) was used artificial neural network. A three layer feed-forward
network with sigmoid hidden neurons and linear output neurons are used to solve
multi-dimensional problems.
Figure 3. The structure of the multilayer perceptron for network routing
Chapter 5: Tactical Communications and Networks
49
The structure of the multilayer perceptron is shown in Fig. 3. The input layer
of the neural network receives input data for routing in the sensor network. Input
data represent address (node ID) of the data packet that should be transferred
through sensor network (Fig. 1). The interface status represents information
about status of all interfaces for single router. All active interfaces can be used
to route data. Data communications using inactive interfaces are rerouted to active interfaces. Some sensor nodes may fail or be blocked due to lack of power,
physical damage, or environmental interference. The failure of sensor nodes
should not affect the overall task of the sensor network. If many nodes fail, routing protocols must accommodate formation of new links and routes to the data
collection base stations.
The number of the input layer neurons corresponds to the input information
for multilayer perceptron. The number of the neurons of the hidden layer is a target
of the optimization. The number of the neurons of the output layer corresponds to
the number of the interfaces of the router of the sensor network.
The value of the output layer´s neurons represents index of the interface that
will be used to send data packet to the destination. If the value of the output neuron
is equal to 1, interface will be used. Otherwise, this interface will not be used. All
layers are fully interconnected and there are not feedback connections between
the hidden and input layers.
C. ANN optimization
To optimize neural network structure must be done analysis of the number
of neurons in hidden layer from point of view of correct classification [5, 6].
To solve this problem three training sets and several models of neural network
were created.
Number of neurons for input and output layer is equal to each other for all
models of neural network. Number of neurons in hidden layer is variable and
it changes in interval from 10 to 100 neurons for training, validation and testing sets.
Training, validation and testing sets were created on the base of topology
of the sensor network shown in Fig. 1. The number of samples for neural network
training depends on router, for which is ANN created. Each sample is represented
by one rule of the routing table. There are two main routing rule groups. One group
of rules represents situation when all interfaces are in active state. The second group
of rules are applied when some interface is inactive and another interface must be
used to forward data packet.
Training set was used during network training to adjust error and it consists
of 70% of samples. Validation set was used to measure network generalization
and it consists of 15% of samples. Training is halted when generalization stops
improving. Testing set have no effect on training phase and provide independent
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measure of network performance during and after training. Testing set consists
of 15% of samples.
The results of Matlab simulation are shown in following tables and graphs.
Table I. Mean squared error for analyzed ann
Number
of hidden neurons
Training
Mean Squared Error
Validation
Testing
10
8.487e-3
2.453e-2
1.526e-1
15
6.954e-2
7.843e-2
9.085e-2
20
7.743e-17
1.081e-16
1.099e-16
Mean squared error (MSE) is the average squared difference between outputs
and targets. Lower values are better. Zero means no error. The lowest value of MSE
is for ANN with 20 neurons in the hidden layer.
Table II. Regression for analyzed ann
Number
of hidden neurons
Training
Regression
Validation
Testing
10
9.690e-1
9.074e-2
4.225e-1
15
7.458e-1
6.926e-1
6.661e-1
20
9.999e-1
9.999e-1
9.999e-1
Regression R values measure the correlation between outputs and target.
An R value of 1 means a close relationship, 0 a random relationship. R values are
again the best for ANN with 20 neurons in the hidden layer.
ANNs with higher number of neurons in the hidden are not shown in the tables, because the value of MSE and R value are worse than ANN with 20 neurons.
The following regression plots display the network outputs with respect to targets
for training, validation, and test sets (Fig. 1, Fig. 2 and Fig. 3). For a perfect fit,
the data should fall along a 45 degree line, where the network outputs are equal
to the targets. For this problem, the fit is reasonably good for all data sets, with
R values in each case of 0.93 or above. The ANN with 20 neurons in the hidden
layer has the best results.
Chapter 5: Tactical Communications and Networks
Figure 4. Regression R for ANN with 10 neurons in the hidden layer
Figure 5. Regression R for ANN with 15 neurons in the hidden layer
51
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Figure 6. Regression R for ANN with 20 neurons in the hidden layer
V. Simulation and synthesis results
VHDL (VHSIC hardware description language) is a hardware description language used in electronic design automation to describe digital and
mixed-signal systems such as field-programmable gate arrays and integrated
circuits [7]. ANN was implemented by VHDL language and synthesized using
Xilinx ISE 13.3.
Weights and biases that are obtained from Matlab simulation after training
phase are floating point. To be synthesis possible, weights and biases of the artificial neural network are expressed using fixed-point numbers (range scale
was multiplied by 1000) in the FPGA-based implementation. There is no difference in results between FPGA-based implementation and simulation.
The Virtex®-5 LXT ML505 is a general purpose FPGA and RocketIO™
GTP development board. Provides feature rich general purpose evaluation
and development platform. Includes on-board memory and industry standard
connectivity interfaces. It delivers a versatile development platform for embedded applications. Virtex®-5 LXT ML505 was used as a platform for realization
of the analyzed ANN.
Chapter 5: Tactical Communications and Networks
53
Table III. Analysis of ann fpga realisation
Delay
FPGA
Number of LUT
Slices
10
142 ns
1098
524
15
149 ns
1527
714
20
152 ns
1822
865
ANN
The results of the practical realisation of the ANN are summarized in previous table. Main parameters to compare were delay, number of slices and number
of 4-inputs LUTs. FPGA (Field Programmable Gate Array) is an integrated circuit containing a matrix of user-programmable logic cells, being able to implement
complex digital circuitry. The elementary programmable logic block in Xilinx FPGAs
is called slice. LUTs (Look-Up Tables) can implement any 4-input boolean function,
used as combinational function generators. They are usually the number of LUTs
and the number of slices (and not the number of registers) that are used to compare
the size of FPGA designs.
Delay for all ANN has almost the same value. The highest number of slices
and 4-inputs LUTs has ANN with neurons in the hidden layer. The lowest requirement has ANN with 10 neurons in the hidden layer. The modern FPGA circuits
(thanks to their size) can accommodate easily requirements of the proposed ANNs.
VI. Conclusion
In this paper, was presented a new method for sensor routing table realisation using neural networks. It was proposed artificial neural network topology
and optimization criterions to solve a problem of routing in the mash topology
of the sensor network. The main advantage of the using ANN for table look-up
process is speed. The size of the look-up table in this approach could not influence
the speed of decision where to send packet next as it is in other methods. Time to get
decision from ANN is always the same. ANN could be trained to change the routes
in case of topology changes that could be predicted in the training phase. Thanks
to this ANN does not have to be retrained in the real environment after topology
changes. Analyzed ANNs were practically realised in hardware platform. Execution
of the training process in the hardware can improve this proposal. Future work
can be oriented to realisation embedded system based on MicroBlaze processor
to train ANN in FPGA architecture. This method can be executed in parallel on
an FPGA chip.
Another interesting issue for routing protocols is the consideration of node
mobility. Most of the current protocols assume that the sensor nodes and the sink
are stationary. However, there might be situations such as battle environments
where the sink and possibly the sensors need to be mobile.
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In such cases, the frequent update of the position of the command node and
the sensor nodes and the propagation of that information through the network
may excessively drain the energy of nodes. New routing algorithms are needed
in order to handle the overhead of mobility and topology changes in such energy
constrained environment.
Other possible future research for routing protocols includes the integration
of sensor networks with wired networks (i.e. Internet). Most of the applications
in security and environmental monitoring require the data collected from the sensor nodes to be transmitted to a server so that further analysis can be done. On
the other hand, the requests from the user should be made to the sink through
Internet. Since the routing requirements of each environment are different, further
research is necessary for handling these kinds of situations.
This work has been supported by the MOD of Slovak Republic grants No. 4/2011
“Partial dynamic reconfiguration of the digital systems in military applications”.
References
[1] J.N. Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks:
A Survey”, IEEE Wireless Communications, vol. 11, no. 6, Dec. 2004, pp. 6-28.
[2] O. Al-Kofahi and A.E. Kamal, “Survivability Strategies in Multihop Wireless
Networks”, IEEE Wireless Communications, vol. 17, no. 5, Oct. 2010, pp. 71-80.
[3] J.L. Elman,”Finding structure in time, Cognitive Science,” vol. 14, 1990, pp. 179-211.
[4] F. Rosenblatt, “Principles of Neurodynamics,” Washington D.C., Spartan Press,
1961.
[5] I. Mokriš and M. Turčaník, ”Contribution to the analysis of multilayer perceptrons
for pattern recognition,” Neural Network World. vol. 10, no. 6 (2000), ISSN 1210-0552,
p. 969-982.
[6] M. Turčaník, I. Mokriš, Possible Approach to Optimization of Neural Network
Structure. Proc. of Conf. SECON 97, Warsaw, pp. 326-335.
[7] D. Pattreson, Artificial Neural networks – Theory and Applications, Prentice Hall,
1996.
[8] S. Melacci, L. Sarti, M. Maggini, M. Bianchini, “A Neural Network Approach to
Similarity Learning,” Lecture Notes in Computer Science, Artificial Neural Networks
in Pattern Recognition, June 30, 2008, pp. 133-136.
[9] S. Ezzati, H.R. Naji, A. Chegini, P. Habibimehr, “Intelligent Firewall on
Reconfigurable Hardware,“ European Journal of Scientific Research. ISSN 1450-216X vol. 47 no. 4 (2010), pp. 509-516.
[10] M. Harakaľ, J. Chmúrny, The Tesseral Processor for Image Processing Based on
Hierarchical Data Structures, Radioengineering, vol. 6, no. 4, 1997, pp. 1-5. ISSN 1210-2512.
[11] M. Kuffová, Simulation of fatigue process. In: Mechanics. – ISSN 1734-8927. – vol. 27,
no. 3 (2008), p. 110-112.
An Application of Chord Structure
in Tactical Ad-hoc Network
Jerzy Dołowski, Marek Amanowicz
Institute of Telecommunications, Faculty of Electronics,
Military University of Technology, Warsaw, Poland,
{jerzy.dolowski,marek.amanowicz}@wat.edu.pl
Abstract: Mobile ad hoc network (MANET) is expected to become a prominent standard in a tactical
environment. However, a tactical network requires a reliable mechanism of discovering resources.
In this paper, we propose using a Peer-to-Peer structure to achieve this goal. The crucial aspects
of the Chord structure’s implementing over MANET are pointed out. A cross-layer communication
is proposed to improve the joining process. The results of simulations show that our solution is able to
work in a fully autonomous way.
Keywords: Peer-to-Peer, Chord, MANET
I. Introduction
Contemporary battlefield requires a deployable command and control system.
A tactical system should cope with an increasing number of services and sensors.
SOA (Services Oriented Architecture) is considered a method of tactical services’
consolidation. An implementation of the SOA system requires a distributed register
which would not be prone to damage in a tactical environment. We noticed that
Peer-to-Peer system could be used for this purpose.
The Peer-to-Peer system is a set of equal entities (nodes) that communicate
with each other to share resources. The nodes form a self-organized overlay network.
Among many Peer-to-Peer systems we have chosen the Chord structure. Attractive
features of Chord include its simplicity, provable correctness and provable performance. However, the routing table of Chord is not based on topological feature.
Thus, we propose modification of Chord in order to improve its distance awareness.
A mobile ad hoc network (MANET) is a multi-hop wireless network operating without infrastructure. All nodes in the network have to cooperate in order to
perform routing. An application of Chord in MANET requires a robust mechanism
of bootstrap. We propose the use of discovering features of MANET.
The rest of the paper is organized as follows: Section 2 presents the related
work, section 3 provides a brief overview of the Chord, whereas in section 4
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we describe, in more details, the crucial aspects of the Chord on top of a MANET
as well as the proposed solutions. We demonstrate the effectiveness of our system
in Section 5 and the paper is concluded in Section 6.
II. Related work
Various Peer-to-Peer systems have been proposed for MANET. Most of them
are unstructured (e.g. [6]). Usually unstructured systems use some forms of flooding
to find resources. Flooding-based look-up is rather inefficient and does not scale
well. This makes them impractical in a tactical network where bandwidth is limited.
Nevertheless, there are approaches to overcome this limitation, for instance [7].
In the structured Peer-to-Peer systems, the topology is strictly controlled. They
use a Distributed Hash Table (DHT) which allows an efficient and deterministic
search for resources. Therefore, in our opinion only structured Peer-to-Peer systems
should be taken into account.
Chord overlay which matches to the underlay network is presented in [8].
This feature is achieved using the minimum-spanning tree in each node. Additionally, the identifier space is divided among the nodes in a non-contiguous way.
Another improvement of Chord over MANET, which is proposed in [5], consists
in forwarding lookup queries via physically adjacent nodes. In order to make it possible, a node has to maintain its physically adjacent neighbors. As a consequence,
it results in generating redundant routing traffic.
A different approach is an integration of the DHT with ad hoc network routing. MAD Pastry [10] is an example of that manner. In this idea, the overlay is built
on top of the AODV protocol and both protocols cooperate strictly.
III. The Chord structure
In the Chord [9], each node has a unique identifier ranging from 0 to 2m−1.
The nodes form a one-dimensional ring according to their identifiers. Each node
maintains a pointer to its successor and predecessor node. A successor of the node
is the first node whose identifier is higher than its own identifier. Moreover, each
node maintains information about (at most) m other neighbors, called Fingers. They
are collected in a Finger Table. The i-th row of this table indicates the successor for
interval [n+2i, n+2i+1), where n is the identifier of the current node.
The resources are mapped to nodes based on their keys (identifiers). In Chord,
a resource is mapped to the first node whose identifier is equal to or follows its key.
An example of a Chord overlay network formed by nine nodes arranged on a circle
is shown in Figure 1.We present also an example of the Finger Table of node n = 24,
and location of two resources.
Chapter 5: Tactical Communications and Networks
57
Figure 1. Example of a Chord ring (m = 5)
A message is forwarded toward a closer node in the Finger Table with the highest identifier value less than or equal to the identifier of the destination node.
As a consequence, the key lookup mechanism in Chord takes O (log N) hops, where
N is the total number of nodes [9].
IV. Adaptation of Chord for MANET
A. The key issues
Considering the introduction of Chord in the MANET, we have to take into
account several key issues. Firstly, a mechanism of bootstrapping is requested.
A new node which intends to join an existing Peer-to-Peer structure has to communicate with at least one node of this structure. Therefore, a discovery method
is mandatory. Secondly, the state machine of the Chord node has to be adjusted
to the ad hoc environment. And thirdly, the routing in Chord is not based on
an underlay network topology. If the node was aware of real distances to others,
it could choose a closer node during a routing process. We suggest implementing
an additional mechanism which will improve nodes’ awareness of distance.
B. A cross-layer mechanism
The MANET manager discovers and maintains routes to other nodes. We propose using these routes by the overlay layer during bootstrapping. The nodes that
could be potentially used for bootstrap are stored in a Bootstrap List.
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We assume that the MANET manager adds every discovered route to the Bootstrap List using a cross-layer communication – Figure 2.
When the node intends to join a structure, it selects a random node from
the Bootstrap List. Next, the node sends a request towards it. In case of no response
this entry will be removed from the list. If the list is empty, the node will form
a one-member ring.
Figure 2. The node
The process of adding entries by the MANET manager is independent from
the Chord procedure.
C. A state machine
In Chord, the node maintains an internal state machine. When the node
has successfully joined a ring or has formed a new one-member ring, it goes
into the READY state. In this state, the node waits for incoming requests of joining, but it does not send these requests itself. The abovementioned behavior
arises from the assumption that an accessibility of the node is invariable during its operation. However, the use of MANET as an underlay network results
in dynamic changes of the topology. Thus, the internal state machine should
be adapted to the nature of the ad hoc network. The proposed procedure is depicted in Figure 3.
We propose to introduce a new state called READY_ALONE. The node goes
into this state when it fails to join any member of the Chord ring. The node permanently tracks changes of its Bootstrap List in this state. Besides this, the node
is ready to receive a request of joining from another node. Furthermore, we also
propose extending the READY state. The node staying in this state should try
Chapter 5: Tactical Communications and Networks
59
to join the newly discovered nodes. It is essential for detecting nodes that are
members of another ring (partition).
Figure 3. The modified Chord procedure
D. An improvement of the distance awareness
The topology of an overlay network does not reflect the underlying (physical)
topology. This may result in deterioration of the system effectiveness. In particular,
the latency of a request may decrease. Therefore, we propose to use Vivaldi algorithm that is one of the Internet Coordinate Systems.
Vivaldi [4] is a distributed technique to estimate the latency between nodes
in the Internet. In Vivaldi, a new node computes its coordinates after collecting
latency information from only a few other nodes. Each time a node makes a request to another node, it measures the network latency to the node. A response
includes the answering node’s current coordinates. The requesting node refines its
coordinates based on the latency measurement and the responding node’s informa-
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tion. As Vivaldi is a decentralized procedure, no fixed infrastructure needs to be
deployed. Furthermore, the node does not require any initial data. These features
align well with the requirements of the Peer-to-Peer system.
Figure 4. The pseudocode to find the successor node of an identifier k
However, in order to take advantage of that algorithm it is necessary to modify
the Chord procedures by extending the Finger Table.
The main idea of this proposal is to prepare a set of nodes which potentially
could be predecessors of an identifier k. Then, the node whose distance is the smallest one is selected.
In the original Chord, a row of the Finger Table contains only one successor.
In our modification, several successors are placed in each row. Moreover, the distance obtained owing to Vivaldi is associated with each successor. The procedure
of finding the successor has to be modified too. The appropriate pseudocode
is shown in Figure 4.
Chapter 5: Tactical Communications and Networks
61
V. Evaluation
We conducted experiments using a computer simulation technique in order
to evaluate the proposed solution. We selected the OMNeT++ [11] simulation
environment and OverSim model [2].
The aim of the evaluation was to determine whether it is possible to effectively
search for an identifier in the Chord structure built on top of MANET as well
as what is the volume of the maintenance traffic.
A. Model
A model of mobile ad hoc network was prepared. We choose the DYMO
(Dynamic MANET On-demand) protocol [3]. The network consisted of a fixed
number of nodes, ranged from 10 to 60. Each node acted as a Chord node. Moreover, the node was able to use the Vivaldi mechanism and extending Finger Table.
This feature was controlled during experiments.
We prepared three scenarios of our simulation investigations. In the first
scenario, the nodes were placed in the simulation area and stayed in their position
motionless during operation (a stationary network). In the second and third scenario, the nodes moved according to the Random Way Point mobility model [1].
In this model, the node chooses a random destination point, and next moves toward it at a constant speed. After reaching its destination, the node stays in it for
a pause time. Then, it begins to move toward the next destination. In our experiments, the current value of the speed was chosen from the ranges: 1…5 m/s, and
1…10 m/s in the second and third scenario, respectively.
B. Tests and performance metrics
Each node that had become a member of a Chord ring started the test application. This application performed the following tests: One-way lookup, KBR lookup,
and DHT lookup.
During the One-way lookup test, the test application sends a message toward
a random identifier. In the KBR lookup test, the application asks the Chord to look
for a node that is responsible for the given identifier.
In order to evaluate an impact of the Vivaldi mechanism on the process
of obtaining a resource we prepared the DHT lookup test. We assumed that there
are copies of the same resource shared in the Chord structure. In this test, the application that looks for the resource is eligible to select one of the nodes which
possess that resource.
We use the following metrics to analyze the protocol performance:
– One-way Delivery Ratio – The total fraction of requests that reach the destination successfully.
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– One-way Latency – The delay between reaching a destination by the message and sending it.
– Lookup Success Ratio – The total fraction of requests for which the originator receives an answer successfully.
– Lookup Success Latency – The delay between receiving answer and sending the request.
– Get Latency – The cumulative delay between receiving a resource and
sending the request in DHT lookup test.
Additionally, the total volume of the sent and received traffic essential for maintenance (i.e. refreshing successor, and predecessor; refreshing entries in the Finger
Table) was written.
C. Results
The results of our evaluation are shown in Figures 5-11. The notation “ps = 1”
means that the Vivaldi mechanism and extending Finger Table were active, whereas “ps = 0” denotes the original Chord structure.
The delivery ratio for One-way lookup test (Figure 5) reflects the general
efficiency of a node to communicate in the overlay network. Increasing the number of nodes diminishes the ability to successfully communicate in the overlay.
We found out that the performance of the wireless network is the primary cause
of that observation. The phenomena that are peculiar to mobile ad hoc network
(especially the hidden station) hinder transmission. The improvement of the underlay network was not our goal.
Figure 5. The one-way delivery ratio
Chapter 5: Tactical Communications and Networks
63
Nonetheless, the obtained results prove that our application of Chord enables
fully autonomous work on top of MANET. Moreover, our modifications bring a slight
increase in the delivery ratios (Figure 5 and Figure 7) as well as some improvement
of the latencies (Figure 6 and Figure 8).
Figure 6. The one-way latency
Figure 7. The lookup success ratio
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Figure 8. The lookup latency
The knowledge about real distances between nodes may also be exploited
to optimize a process of getting resources. Owing to our modification, the node
is given a chance to choose the nearest node which possesses the requesting resource. As shown in the Figure 9, the latency is decreased.
Figure 10 and Figure 11 depict the total sent and received maintenance traffic,
respectively. The underlay network is not overloaded by this kind of traffic. However,
it should be stressed that the frequency of refreshing has considerable impact on
the value of the above-mentioned traffic.
Figure 9. The Get latency
Chapter 5: Tactical Communications and Networks
65
Figure 10. Sent maintenance traffic
Figure 11. Received maintenance traffic
VI. Conclusion
In this paper, we proved that the Chord structure is able to operate on top
of MANET in a fully autonomous manner. The adaptations we proposed resolve
the joining process as well as improve distance awareness of the node. Although
the effectiveness of work is not perfect, the network is not degraded due to the maintenance traffic.
During future work we intend to test our solution in a real ad hoc network.
Therefore, we have started practical implementation of the modified Chord
structure.
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References
[1] N. Aschenbruck, E. Gerhards-Padilla, and P. Martini, “A survey on mobility
models for performance analysis in tactical mobile networks,” in: Proceedings
of Military Communications and Information Systems Conference (MCC 2007),
Sep. 2007, Bonn, Germany, pp. 25-26.
[2] I. Baumgart, B. Heep, and S. Krause, “OverSim: A scalable and flexible overlay
framework for simulation and real network applications,” in: Proceedings
of the International Conference on Peer-to-Peer Computing, Seattle, WA, USA,
Sep. 2009, pp. 87-88.
[3] I. Chakeres, and C. Perkins, “Dynamic MANET On-demand (DYMO) Routing,”
IETF Internet-Draft, draft-ietf-manet-dymo-21, 2011.
[4] F. Dabek, R. Cox, F. Kaashoek, and R. Morris, “Vivaldi: A decentralized network
coordinate system,” in: Proceedings of the ACM SIGCOMM, Portland, Aug. 2004,
pp. 15-26.
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Systems, 2006, pp. 1541-1554.
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Underlying Proactive Routing,” in: Proceedings of Seventh International Conference
on the Mobile Ad-hoc and Sensor Networks, 2011, pp. 248-255.
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[9] I. Stoica, R. Morris, D. Liben-Nowell, D.R. Karger, M.F. Kaashoek, F. Dabek,
and H. Balakrishnan, “Chord: A Scalable Peer-to-Peer Lookup Protocol for
Internet Applications,” IEEE/ACM Transactions on Networking, vol. 11(1), Feb. 2003,
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in: Proceedings of Fourth Annual IEEE International Conference on Pervasive
Computing and Communications Workshops, 2006, pp. 179-183.
[11] OMNeT++ discrete event simulation system, http://www.omnetpp.org
Revisiting the DARPA’s Idea
of a Programmable Network
Vladimir Aubrecht1, Tomas Koutny2
1
Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic,
[email protected]
2
Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic,
[email protected]
Abstract: In 1995, DARPA recognized the shortcomings of IP networks. As a response, the experts
initiated a project that should become the cornerstone of new military communication architecture.
The goal was a network that would be resistant to attack and to operate despite successful attacks,
while allowing a secure sharing of data at all levels of command. The project was named Active
Networks. However, it failed to meet performance and security criteria. We revisited this problem,
identified performance and security bottlenecks so that we present an advance to fast, secure and
scalable active network server – Smart Active Node.
Keywords: programmable network, active networking, smart active node
I. Introduction
In 1995, DARPA recognized the shortcomings of IP networks. As a response
the experts initiated a project that should become the cornerstone of new military
communication architecture. The goal was a network that would be resistant to
attack and to operate despite successful attacks, while allowing a secure sharing
of data at all levels of command. The project was named Active Networks.
Initially, DARPA funded the research on active networks. A number of wellaccepted papers appeared to demonstrate the capability of the active-networking
architecture to fulfill the desired goals. With a time, it has shown that these papers
presented rather a proof of the concept than a real-world usable implementation
of an active networking server. The existing active networking servers failed to
widespread beyond the research activities. As DARPA ceased funding this research,
the research stopped gradually. Active Networks failed to meet performance and
security criteria.
The history of computer networks shows a periodic attempt to benefit from
network programmability. For example, it is the Wormhole approach to loadsharing [1], the paradigm of mobile agents [2] and finally, the active networking.
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Military Communications and Information Technology...
Therefore, we decided to revisit the idea of active networking. We identified performance and security bottlenecks so that we present an advance to fast, secure
and scalable active network server – Smart Active Node.
Our decision was based on a development of load-redistribution method
that runs on a simple active networking server – Grade32 [3, 4]. Grade32 runs
processor-native code, but provides no security. In a short, we added a security to
the Grade32 approach.
The paper is organized as follows. The second section describes the related
work. The third section presents Smart Active Node’s (SAN) architecture and
server’s scalability. The fourth section describes the programming environment
for the capsules and active applications. The fifth section presents the use of Ada’s
rendez-vous to implement process isolation and making calls to the applicationprogramming interface (API). The sixth section describes the implementation
of access rights. The seventh section describes capsule transmission. The eighth
section presents results. The ninth section concludes the paper.
II. Related work
A. Active networks
In active networks, a capsule supersedes the packet. The capsule is a packet associated with a program code. The capsule’s header contains a hash digest
of the program code. This program code can handle the data being transmitted.
A network node can download the program code as needed.
In general, a network protocol is associated with a program code that handles
its packets. In an IP network, the program code has to already run at the network
node, prior the packets are received. Therefore, the services provided are rigid and
has to be standardized first. In an active network, the network node can download
and execute the program code as needed. Therefore, only the program code notation and execution environment have to be standardized. As a result, the network
can learn new protocols and functionality immediately.
In IP networks, the destination node has to already run a program that
can handle the incoming packets of a given protocol. Active Networks do not have
this restriction.
B. Preceding concept
Let us discuss the concept of active networking that precedes SAN. In the bottom,
there is a so-called NodeOS. It is either a standard operating system such as Linux or
Windows, or it is a specifically written operating system such as JanOS [5].
On the top of the NodeOS, there are so-called execution environments. Each
environment handles a particular notation of program code. ANTS [6, 7], BEES [8]
Chapter 5: Tactical Communications and Networks
69
and Magician [9] use Java bytecode. Proof of the concept for load redistribution
in Active Networks, called Grade32 [3, 4], uses native x86 code.
A process running in the execution environment is either an active application, or a capsule. The application injects the capsule into the network. If network’s
implementation allows it, the capsule can replicate itself by injecting another capsule into the network. Grade32 allow the capsule to run an active application at
the node. This approach was necessary for Grade32 to implement process migration
in a distributed environment. It is not found in preceding implementations such
as ANTS and Magician.
Initially, the capsule program code was either pre-installed at the node, or
it was transmitted with the capsule. In later implementations of active networking
servers, each capsule contains a hash digest of capsule’s program code [7]. Using
code distribution protocol, node uses this digest to obtain the program code from
other nodes. Only the capsules of a basic code distribution protocol have to use
standardized code identifiers, in place of the hash digest, to identify particular
requests. Otherwise, it would be impossible for any two nodes to exchange a program code. Active node caches recently used program code at a local repository.
Projects like PLAN [10], RCANE [11], SANE [12] and SNAP [13] focused
on security and performance. In contrast to the general-purpose Java bytecode,
specialized languages appeared. Their goal was simplicity that would limit resource
consumption and shorten a capsule’s execution time.
Reference [14] gives an overview on CSANE active network concept, which
aims for security and scalability. CSANE is based on cluster processing. It builds
on ANTS, JanOS and Linux.
C. Related work
Taking a closer look on the concept of the Google Chrome [15] operating
system, we see a resemblance with the active networks concept. There is a simple,
underlying operating system that provides hard application isolation – sandboxing [16]. API and the definition of web services define the Execution Environment.
Also, it features a security manager that prevents running a malware.
Although the Native Client [17] is not primarily designed for a use in active
networks, there are ideas valuable to a high performance execution environment.
III. Architecture
A. Smart Active Node
For the Smart Active Node, we highlight this:
• SAN runs on top of a standard operating system.
• SAN uses standard Java Development Kit for programming the capsules
and to achieve interoperability across different platforms.
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Military Communications and Information Technology...
• SAN does not execute capsules in a single process. Instead it takes advantage
of process isolation that is provided by the underlying operating system.
• SAN uses Ada’s rendez-vous and Remote Procedure Call to further isolate
a capsule program code from accessing SAN’s internal data structures.
• SAN runs without a virtual machine such as JVM. The capsule program
code is compiled just once to the native instruction set for the target
processor.
• SAN applies security measures when compiling the bytecode to the native
instruction set, thus eliminating this overhead from the capsule runtime.
• To increase performance, SAN benefits from supporting a distributed
memory model.
• For SAN, we adopted a concept of roles for a fine-grain control over execution of different program codes of capsules.
B. Farmer – worker model
The SAN server is a farmer-worker model that allows network-node scalability
using the distributed memory model. Furthermore, the client-server model let us
to use the means of process isolation and resource-limits which are provided by
the underlying operating system. The resources are the processor time, the memory
and network bandwidth.
The farmer manages incoming capsules and assigns them to particular workers for further processing. The SAN server can run on a cluster, i.e. the workers
can be distributed physically. SAN server and SAN worker communicate using
the remote procedure call (RPC) mechanism. Therefore, SAN uses RPC to assign
a capsule to a particular worker.
Existing implementations of RPC, e.g. Open Networking Computing RPC,
rely on the TCP/IP stack. As active networks were supposed to supersede IP, SAN
has a custom RPC implementation. The SAN RPC implementation uses SAN’s
internal networking interfaces instead of relying on the presence of TCP/IP stack.
SAN networking interface can encapsulate an ISO/OSI layer 2 protocol such
as the Ethernet. Furthermore, SAN utilizes a proprietary data serialization to avoid
performance penalties by eliminating unnecessary data conversions.
C. Farmer
In the current implementation, the farmer provides the main subsystems:
• Network subsystem – a communication with other nodes
• Security subsystem – monitoring the networking traffic and capsule execution, while enforcing the security measures
• Repository – caching compiled program codes of capsules
• API subsystem – providing an API to capsules via RPC
Chapter 5: Tactical Communications and Networks
71
As the SAN server development will progress, part of the subsystems will be
moved to the workers. This will reduce an average time a capsule/worker needs to
access a farmer’s subsystem.
Figure 1. SAN’s Famer-Worker Model
D. Worker
In the current implementation, the worker is a sandbox for an executing capsule. It takes an advantage of operating-system provided mechanisms for process
isolation and enforcing limits on processor–time consumption, memory usage,
network bandwidth usage, etc. To the running capsule, the worker provides the API.
IV. Programming environment
A. The server programming language
Initially, we followed some of the well-accepted papers on active networking.
The programming language was Java. Both, server and capsule codes were written
in Java and compiled to bytecode.
While the Java Virtual Machine (JVM) executed the server program code,
we needed to execute the capsule program code separately in order to enforce
the security measures. If we would let the JVM to execute the capsule program
code, we would have no control over its execution.
We needed to control processor-time consumption, memory usage, network
bandwidth usage and method calls to prevent malicious actions as such calling
System.exit.
Java was designed to be architecture neutral. To follow this principle, we
were left with one choice – to interpret the capsule bytecode in a JVM. Because
of the incurred speed penalty, we decided to use C++ for the server development.
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Military Communications and Information Technology...
While the developments costs are higher with C++, we got a robust and efficient platform to execute the capsule program code, with security measures applied.
B. The capsule programming language
While Java is performance-inadequate for the server development, it has benefits for programming the capsules. Using a Java bytecode to C++ cross-compiler,
and a C++ compiler, we compile the bytecode into the target-processor optimized
machine code. In addition, the Java’s portability allows the SAN servers to run
across several processor architectures.
The Java’s simplicity provides an opportunity to analyze the bytecode for a possible security breach just once, to save the processor time later on. For example,
the absence of pointers in Java greatly improves the security.
Per a class, the bytecode to C++ cross-compiler generates the header file with
class’ declaration and C++ file with class’ implementation. The header file includes
header files of other required classes. The Java Runtime Environment is replaced
with a C++ equivalent. In addition to standard packages, the SAN Java development kit (SAN JDK) provides classes to call SAN API. Finally, the capsule program
is linked using the pre-compiled SAN JDK and compiled capsule’s cross-compiled
C++ code. The linker generates a dynamically linked library that is saved for a later
reuse, once another capsule of the same protocol arrives.
Several JDK methods are security-unsafe. For example, letting a capsule to use
reflection would be a security risk. In SAN JDK, such methods are not available at
all. Such a malicious capsule will not even compile. Classes such as java.io.File,
which access the server’s file system, have custom implementation which forbids
the capsule to access the file system of the underlying operating system. The new
operator is overloaded with a custom implementation that forbids memory allocation, if the capsule overcomes a given limit.
To monitor processor-time consumption, a separate thread monitors execution times of capsule. It terminates a capsule, which exceeds the limit.
V. Programming interface
A. SAN programming interface
The capsule program code is compiled into a dynamically linked library.
The library exports a routine. SAN server calls this routine to execute the program
code. The routine takes one parameter. This parameter is an object implementing
a pre-defined interface to access SAN API. Capsule accesses SAN API in an objectoriented manner. The API offers these services:
• Network service – capsule transmission
• Routing service – accessing and updating the routing table
Chapter 5: Tactical Communications and Networks
73
• Storage service – an inter-process communication using the blackboard
concept [18]
• Diagnostic service – for debugging
B. Calling SAN API
Standard Java compiler compiles the capsule program code. At this point, the SAN
API interface is not implemented. Later on, the SAN server compiles the bytecode
to C++ source code. This compiled source code is linked with an object that makes
the actual calls to the SAN API interface. This object is a wrapper for Ada’s rendezvous synchronization mechanism.
The Java synchronization model relies on a monitor construction. The monitor
has its working data and synchronized methods. For an exclusive access, a thread calls
a synchronized method to manipulate with the monitor’s data. The thread continues to
receive processor-time quanta based on its priority. Also, its access rights are not elevated.
With Ada’s rendez-vous, a thread makes an entry call to the server. Then,
the calling thread is suspended until the server’s thread finishes execution of the call.
Only the server thread accesses its data. Furthermore, the server’s thread priority
and access rights are independent on the calling thread.
The SAN server runs in a user-address space. The compiled capsule program
code cannot issue an equivalent of x86 int and syscall instructions to isolate SAN’s
kernel from the running capsule. Therefore, we use Ada’s rendez-vous to implement
secure API calls.
To a capsule programmer, the Java capsule program code makes a call to
a Java interface.
VI. Access rights
A particular networking application may require additional access rights to
complete its task. For example, a routing service requires an access right to modify
the routing table. Similarly, time-synchronization service requires an access right to
set the system time. In SAN, we implement the access rights with a concept of users and roles. In a principle, we follow the Windows NT security model of users,
access rights and privileges.
A role is a set of access rights. For example, the routing-service role includes
all rights required to modify the routing table, but not to e.g. set the system time.
In addition to the access rights, a role has resource-consumption limits defined.
A user has authorization credentials and a set of roles. For the development,
we have defined four roles:
• Administrator – no restriction
• Power User – There is no restriction on capsule’s code execution time.
This is used for the routing service.
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Military Communications and Information Technology...
• Users – The execution time limit is increased against the anonymous users.
• Anonymous – This applies to unknown capsule codes and unauthorized
users. The code being executed has lowest priority and no access right
is granted.
The capsule code can elevate its default access rights. By using different credentials, it can re-authorize to the server.
VII. Transmitting a capsule
A. All-Nodes-Execute flag
In the original concept, the capsule’s code executed on every node visited.
This implied an additional overhead. For example, let us consider a frequently used
protocol such as TCP. The intermediate nodes have no need to modify packets’
payload. The packets need to be forwarded only. Therefore, we eliminate overhead
of the original active-networking concept by introducing All-Nodes-Execution flag.
When this flag is not set, the capsules’ code executes at the destination node only.
As a result, the intermediate nodes do not need to create sandboxes, thus reducing
the associated overhead to increase the node throughput.
With this behavior, we maintain low costs of the TCP protocol, while offering
an additional feature. When the capsule executes at the destination node, it attempts
to deliver its payload. Instead of TCP/UDP ports, we will identify a listening process
with a globally unique ID (GUID). This concept was used successfully in the loadredistribution method [3, 4]. If the listening process migrated to a different node,
it left its new address using the black-board mechanism. Thus, the capsule’s code
could lookup the new address and set it as the new destination address. This concept reduces the overhead of handling the error when the listening process is not
present at the destination node.
SAN provides the black-board concept with the Storage service. It is a keyvalue dictionary. The key is a GUID and the value is a sequence of bytes. Its size
and persistency is given by respective resource limits. With the storage service, we
implement an inter-process communication, when two communicating processes
do not need to execute concurrently.
With the All-Nodes-Execute flag set, the capsule’s code executes at every visited node. We use this flag for a routing protocol. To be specific, we use the AntNet
routing protocol [19] to fill the routing table.
B. Quality of Service and Type of Service
To implement Quality of Service and Type of Service, we experiment with
a concept of alternative routing table. SAN’s capsule has Time-Sensitive flag. With
this flag set, the capsule is routed using the better routes in the sense of AntNet
Chapter 5: Tactical Communications and Networks
75
protocol routing metric. If this flag is not set, the capsule is routed to a worse
route if the better route’s link is saturated. As a result, we prioritize the capsule
transmission while attempting to reduce a capsule elimination due to an insufficient bandwidth.
C. Delivering a payload
When a capsule delivers its payload to a process, the SAN would invoke
a registered callback to handle the delivery. The callback executes as a separate
process and asynchronously to the main process of the networking application.
As a result, the main process does not need to be executing. The callback can handle
the capsule, while requiring fewer resources. Later, the main process can process
the cumulative results, which were produced by the callback on receiving capsules’
payloads. This way, we attempt to support mobile devices, where the power and
memory consumption is critical.
When a capsule successfully delivers its payload, it would either forward itself
to a new destination, or finish its execution.
VIII. Results
A. Testing software
To test the performance of the SAN server, we used a custom implementation of the ping command. First, we implemented the ping command as the SAN
active-networking application. Then, we re-implemented the same application using
WinAPI and TCP/IP protocol. This guaranteed that we tested principally the same
applications – the first one using IP, the second one using the active network.
The testing ping application transmitted data for two minutes. We counted
the total number of transmitted bytes in packet/capsule payloads. The payload size
increased gradually.
The operating system used was 64-bit Windows 7, version 6.1.7601. The server and cross-compiled bytecode were compiled with the Microsoft Optimizing
Compiler, version 17.00.40825.2. The bytecode was cross-compiled to C++ using
a proprietary compiler [20]. The generated images were 64-bit executables and
dynamically loaded libraries.
To measure the time, we used QueryPerformanceCounter and QueryPerformanceFrequency WinAPI functions.
B. Testing network
The network nodes used were connected with 100 Mb/s link speed in a full
duplex, using Ethernet. In the current implementation, SAN uses TCP/IP to trans-
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mit the capsules. This implies that the IP-ping should have less overhead than the active-networking pings.
Two servers ran on Intel64, family 6, model A, stepping 7. We tested the ping
on the localhost and over the network. The localhost machine had 8 GB RAM and
3.4 GHz processor frequency. The other machine had 4 GB RAM and 2.3 GHz
processor frequency.
C. Discussion
Table 1 gives achieved bit rate and latency for a ping over a network, i.e. when
packets and capsules processed through the entire networking stack of the operating system. Table 2 gives the same, but for the localhost when the packets and
capsules are not processed by the lowest level of the networking stack of the operating system.
Table I. Network Ping
Size
[B]
SAN
IP
Bit rate
[Mb/s]
Latency
[µs]
Bit rate
[Mb/s]
Latency
[µs]
64
17.1
643
0.5
945
128
18.3
734
1.0
942
256
24.2
811
2.1
942
512
15.2
923
16.3
239
1024
18.0
1097
18.3
428
2048
20.7
1395
22.6
693
4096
20.1
1804
27.9
1121
8192
21.9
2201
35.2
1777
Table II. Localhost Ping
Size
[B]
SAN
IP
Bit rate
[Mb/s]
Latency
[µs]
Bit rate
[Mb/s]
Latency
[µs]
64
5.1
218
17.4
28
128
5.7
223
33.7
29
256
18.3
232
65.1
30
512
41.9
236
114.9
34
1024
77.1
241
211.2
37
2048
136.8
259
363.4
43
4096
179.2
276
726.7
43
8192
193.5
297
1275.5
49
Chapter 5: Tactical Communications and Networks
77
The tests were not designed to achieve the maximum bit rate possible, but
to give the difference between the IP processing and the programmable network
processing.
Table 3 gives memory usage for the SAN ping over the network. The memory
usage measured was the Peak Working Set, which is the largest working set that
has been observed for the process.
Table III. Memory Usage
Size [B]
Server [MB]
Worker [MB]
64
8.1
5.4
128
8.2
5.4
256
8.9
5.7
512
10.5
6.1
1024
12.0
6.9
2048
16.8
8.5
4096
25.0
11.8
8192
39.3
14.3
Looking at the measured quantities of the IP ping, we can see the effect
of Nagle’s algorithm and that the operating system used a reduced processing for
the localhost communication.
Looking at the latencies of the SAN ping, we see an overhead implied by
the sandbox usage. Looking at the bit rates over the network, we see that SAN capsule
suppresses the Nagle’s algorithm due to the capsule’s size overhead. With a payload size greater than 4096 B, we see that capsule serialization has to be improved.
The localhost results confirm this conclusion. Particularly, Table II shows results
above 200 Mb/s for IP, but less than 200 Mb/s for SAN. As the ping-mechanism
is essentially the same for IP and SAN, the reason for the achieved bitrate difference
must be related to the present implementation of capsule processing.
Looking at the memory usage, we see that it increased with capsule’s payload
size. However, the memory usage was stable. If it was not, the memory usage
would increase at least to 328 MB for the 8192 B payload and at least to 77 MB
for the 64 B payload.
IX. Conclusion
In this paper, we present an advance on the DARPA’s idea of a programmable network. We show that the implementation presented could provide a sufficient
performance, while enforcing the security measures on the code being executed.
We achieved these results by using a different approach than the initial implementations did.
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With the All-Nodes-Execution flag not set, data flows are routed in same manner as in IP networks and no sandboxes are created at intermediate nodes. If we
would consider rigid protocol hashes for frequently used protocols such as TCP
and UDP, it would not be even necessary to create sandboxes at all. SAN network
would behave as IP network in such a case, only the address:port-like data structure
would have greater size than with IP protocol. In addition, the SAN network would
still maintain its programmability for other protocols.
If we would consider protocols at higher ISO/OSI level than TCP and UDP,
programmable protocols could possibly provide an increased efficiency over IPbased protocols. For example, reference [22] presents an improved efficiency with
a multi-cast algorithm.
The node throughput depends on the packet buffer size. The node has to
buffer the packet prior processing it. Regarding larger payloads and the All-NodesExecution flag set, SAN could provide a blocking access to the payload. The capsule’s
execution would be suspended, if the capsule requires such a part of payload that
has not been received yet. Such an optimization technique would further increase
SAN’s throughput, as it processes a capsule ahead of receiving its complete payload.
On the other hand, we still pay with processor time and memory consumption for
the network programmability. Therefore, an improved efficiency of networking
protocols at higher ISO/OSI levels should be given by their design. As demonstrated
with e.g. [22], we consider this as possible.
However, the SAN server is still under development [21]. There are a number of optimization opportunities, which should improve the node’s throughput
furthermore.
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Selection and Investigation of a Civil Wideband
Waveform for Potential Military Use
Ferdinand Liedtke, Matthias Tschauner, Sarvpreet Singh,
Marc Adrat, Markus Antweiler
Communication Systems, Fraunhofer-FKIE, Wachtberg, Germany,
[email protected]
Abstract: This contribution presents the results of an ongoing study1 evaluating civil wireless
communication systems or Waveforms (WFs) for their potential military use. It is a continuation
of the work presented in [1] where several civilian communication systems with their main strengths
and weaknesses have been evaluated. The aim of the past and present activities is to examine which
of these communication systems or WFs have valuable characteristics. In addition, it is analyzed which
WFs can probably be modified and supplemented to fulfill military requirements. The focus is on
Wide Band Waveforms (WBWFs) which could help to fill the capacity gap of the “last tactical mile”.
The studies will contribute to the international activities of finding IP and network capable WBWFs
for military Software Defined Radios.
In this paper several fundamentals of tactical communications are sketched, followed by some remarks
on modern civil WFs already in use or tested by military forces. After agreeing on several significant
assessment criteria, three interesting civil systems are short listed before one of these systems is finally
selected for further investigation. For the selected system, WLAN IEEE 802.11n, the first critical
modules are identified and some ideas for their modification and/or enhancement are presented.
Keywords: Wideband Waveform; Software Defined Radio; Wireless Military Communications;
Tactical Communications
I. Introduction
The international defense forces stride ahead towards further development
of the network enabled capabilities for the network centric warfare. Important elements of this research are the communication capabilities, especially the Software
Defined Radio (SDR) technology. For this technology, appropriate hard- and software
modules and new Waveforms (WFs) are needed. In addition to narrow band WFs,
the particular focus is on the development of new, so-called Wide­band WFs (WBWFs)
with spectral channel bandwidths ≥ 1 MHz for the transmission of payload data
1
This research project was performed under contract with the Federal Office of the Bundeswehr for Information Management and Information Technology, Germany.
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Military Communications and Information Technology...
rates > 1 Mbps (Megabits per second) per channel. Several military driven efforts
are pursued in this area. Beside international activities, e.g. from NATO and from
the consortium on Coalition Wideband Networking Waveform (COALWNW) [2],
several nations are thinking about designing additional WBWFs for national use. Keeping this background in mind, it will be interesting to look at civil used WBWFs, too.
In the preceding work [1], the main strengths and weaknesses of four categories
of modern communication systems have been examined: broadcast, cellular, wireless
access networks and trunked radio systems. The results show that several of those
systems have some military suitable characteristics, but further efforts will be required
to get military usable WBWFs. Thus, it is appropriate to select at first a few or only one
suitable modern communication system for further investigation. For the selection
procedure, many military requirements, both of operational and of technical manner, can be collected, weighted and evaluated in a detailed way. But, it is recognized
that only a few evaluation criteria are significant enough for the ongoing efforts that
a comparatively pragmatic and short selection procedure is possible.
This is accomplished in the second phase of our investigations which are discussed in this contribution. In this context, modern civil communication systems
which are already in use or tested by military forces are searched. The recognition
of the state of military deployment of civil WFs has to be kept in mind while deciding for a suited communication system for further investigation. In the following
text “communication(s)” will be mostly abbreviated by COM(s).
To get a better understanding of the different military COM requirements
concerning bandwidth, data rate, range and mobility, a glimpse on tactical COMs
and the necessity to fill the capacity gap of the “last tactical mile” is presented in Section II. Section III contains some remarks on and examples of modern civil COM
systems already in use or tested by military forces. In Section IV, key requirements
of a WBWF for military use are summarized. In Section V, at first, the extraction
of three potentially suited civil COM systems is described. After having outlined
several important strengths and weaknesses of these systems, finally, the WLAN
system IEEE 802.11n is selected for further investigation (WLAN: Wireless Local
Area Network). In Section VI, the first critical modules of this system are identified and ideas for their modification or enhancement are presented. Section VII
gives a short overview of the initial steps for implementing the new WF with SCA
(Software Communications Architecture) conformity on a simulation platform.
II. Tactical communications and difficulties
for the “last tactical mile”
Generally, the military operations planned and executed at the divisional
level or below are the tactical ones. In modern scenarios, planning and execution
of actions are increasingly accomplished by military subordinated smaller groups,
whereby they have to strive for the superior aims. To solve all the different tasks,
Chapter 5: Tactical Communications and Networks
83
powerful COM equipment is necessary. Important requirements for tactical COM
systems are: IP and networking capability, sufficient transfer capacity, adequate range
and fair mobility, also for relevant parts of the infrastructure. The aim is to fulfill
the necessity for Command, Control (C2) and Communication/Coordination (C3)
on the move. The main subsystems of the tactical COM system are pointed out
in Fig. 1 with blue framed boxes. These are: the trunk network, the Combat Net
Radio (CNR) and the data distribution subsystems [3].
Figure 1. Main elements of a tactical COM system, derived from [3]
The arrangement of these subsystems concerning their range-capacity-mobility
trade-offs is depicted in Fig. 2.
Figure 2. Range-capacity-mobility trade-off, similar to [3]
The three blue framed circles designate the areas related to the trunk network
(bottom left), the CNR (top) and the data distribution (bottom right) subsystems.
The trunk network transfers large amounts of information with high data rates
between the relevant headquarters and between the staff personnel and the control
officers, traditionally down to brigade level. The connections are by satellite or
other longer range wireless or lined connections. In general, the range is large and
the mobility is minor.
The CNR in its traditional form is a complement of the trunk network; it has high
mobility, small values for spectral bandwidth and data rate – traditionally 25 kHz
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Military Communications and Information Technology...
and around 20 kbps – and a medium range. The personnel communication system (PCS) is smaller and, in its traditional versions, less powerful than the CNR.
The main purpose of both these radio types is to transfer voice and low rate data
information, e.g. sensor-to-shooter information. Modern tactical radios offer higher
throughput rates, e.g. 112.5 kbps like the SDR-7200 from Elbit/Tadiran [4].
But, for filling the capacity gap between the trunk network and the CNRs or PCSs
for the transfer of more capacity demanding information, e.g. situational awareness
data, an additional tactical subsystem is necessary, which is the data distribution
subsystem. This system has to transfer more information to and from the acting
teams, and nowadays it is the “bottle neck” of the tactical COM system. This problem of the “last tactical mile” is similar to that of the last mile in civil environments.
Although there exist several special solutions for filling this gap, e.g. the U.S. systems
EPLRS (Enhanced Position Locating and Reporting System) and NTDR (Near Term
Digital Radio), important enhancements can be gained by modern SDRs with different WFs. Modern narrow band WFs will be used for CNR purposes and WBWFs
will serve for (bidirectional) data distribution. For satisfying the different military
requirements, it is necessary that the new WBWFs are scalable.
III. Modern civil wireless COM systems already in use or tested
by military forces
Before going on to the evaluation of civil COM systems concerning their potential military use, a review of the present situation is necessary, i.e. a check, if and
where civil wireless COM technologies are already in use or tested by military forces.
A. Broadcast systems
An interesting wideband system is the terrestrial Digital Video Broadcast system DVB-T/T2. It uses the modern and flexible modulation/multiplex type OFDM
(Orthogonal Frequency Division Modulation/Multiplex). But, in its original form,
it has no backward channel. One known modification is the Universal Multi-Media
Link system from Thales Defense [5]. It is provided with a backward channel so
that bidirectional COM is possible. The system is intended for use by safety forces
in difficult scenarios like disasters.
B. Cellular systems
The well-known commercial cellular systems are eagerly used or tested by
military forces though they seem to have some drawbacks like single points of failure
(base stations) and limited mobility of the infrastructure. The cellular technologies
of the third generation (3G) and beyond, i.e. UMTS, CDMA2000, HSDPA, HSPA
and the 4G/LTE (fourth generation of civil cellular systems/Long Term Evolution)
Chapter 5: Tactical Communications and Networks
85
are in the focus. The literature about the military use mainly concerns activities
from U.K. and U.S. [6]. On one hand, cellular systems are used in less dangerous
scenarios, e.g. in command posts, logistical establishments, airports and separated
areas. On the other hand, they are also used for more robust operations. For those
cases, small and transportable base stations, also airborne ones are available with
the aim to have one’s own infrastructure with non-traditional radio frequencies and
extended range and mobility. The currently used or tested handhelds are commercial
smartphones or small tablet PCs, enlarged by ciphering and personnel identification
circuits. In less peaceful scenarios, these hand­helds are connected to the CNRs by
wire or wireless, and the information transfer takes place through the CNRs. For
future use, so-called sleeve modules are developed which can pick up the handhelds
and provide the power supply, the crypto module and interfaces. For peacekeeping
missions, the hand­helds can also use the civil cellular infrastructure, if available.
So, the military used cellular technology can fulfill particular tasks of the trunk
network, the data distribution subsystem and the CNR, too.
Examples of initiatives from U.K. and U.S. are: the Training and Doctrine Command
– Brigade Combat Team initiative, where smartphones are tested together with the JTRSs
(Joint Tactical Radio Systems) AN/PRC-154 (so-called Rifleman Radio) and 155.
In a DARPA (Defense Advanced Research Project Agency) project, the Harris radio
AN/PRC-117G SDR is tested together with tablet PCs. Connections by WLAN are
provided for the near surrounding. A third example is the Monax system from Lockheed
Martin. The used RF is non-traditional, modules for ciphering are developed and IP
connections up to the end-users are available. The developed mobile base stations can be
integrated into land and aerial vehicles. Some of the used WFs are tolerant against larger
latencies, e.g. for GEO-SATCOM connections. A further U.S. development is FASTCOM,
a pico cell system from Textron/Overwatch for robust missions. The base stations are
mobile. The COM types are voice, data and streaming videos, e.g. from smartphone to
an unmanned aerial vehicle and reverse. A high grade ciphering module will be developed. Up to 100 subscribers can be provided. In the U.K., Roke Manor Research is discussing ideas and concepts concerning the use of civil cellular technology, i.e. 3G/UMTS,
4G/LTE and WiMAX, for military purposes (WiMAX: Wireless interoperability
for Microwave Access). Another U.K. project is Roke’s Battlefield Connect, a femto
cell system for a range up to 40 km with up to 7.2 Mbps for non-demanding IP
connections, VoIP and near real-time video transfer. The handhelds would be usable up to 120 km/hr. The used technology is UMTS/HSDPA, HSPA, or, for the future,
4G/LTE or WiMAX. In the final version, 24 subscribers will be provided in one cell,
each with up to 14.4 Mbps for the downlink and up to 1.9 Mbps for the uplink.
C. Wireless access networks
The relevant access networks dealt with in [1] are the WLAN family IEEE
802.11 and WiMAX IEEE 802.16. WiMAX in its mobile form, i.e. IEEE 802.16e,
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Military Communications and Information Technology...
is mainly a cellular system with base stations and the appropriate infrastructure.
But, to preserve the same classification as in [1], it is dealt with in this section
as access network. The ranges of WLAN and WiMAX in their standardized forms
are very different. WLAN can provide connections in the near surrounding, while
the range of the mobile WiMAX is up to 15 km.
In the safety or military range of application, WiMAX is proposed for high data
rate linking over medium ranges. In Norway, its use for rescue operations is proposed.
WLAN is used for short range connections as it is known from civil applications.
For example it is used in headquarters and command posts. A particular proposal
is its supplemental use in command posts during the set-up and set-down periods,
if the line connections are not yet or no longer established. A further WLAN application is the remote control of platforms like sensors, weapon systems or vehicle
based COM nodes. Examples for remotely controlled COM nodes are: Tactical
Cross Domain Solution from U.S. and C2UK C-Lite from U.K. [6].
D. Trunked radios
The well-known trunked radios TETRAPOL and TETRA are well proven and
broadly used for safety and military forces. The systems have a comparatively good
physical and electronic robustness. But, they are designed only with comparably
small channel bandwidths for low or moderate data rates.
IV. Requirements for a wideband waveform in wireless military
tactical use
The term Waveform (WF) comprises of all components of a COM system
in the seven ISO/OSI layers. The focus of the currently accomplished studies is on
the lower layers, PHY and MAC (Physical and Medium Access Control layers).
The requirements of a WBWF are orientated towards the necessity to fulfill mainly
the tasks of a modern bidirectional data distribution subsystem, i.e. to find a WBWF
with IP and flexible networking capability and with appropriate capacity, range
and mobility. The new WF should be flexible in use, i.e. it should be scalable and
adaptable to different applications and transmission conditions. Certain robustness
against disturbing influences, natural and intentional, is aspired.
A. General requirements
General requirements, which are resulting from the facts mentioned in the sections above, are compiled as follows:
• command, control and coordination (C3), also on the move,
• single points of failure should be avoided as far as possible, hence an adhoc capable system/WF would be advantageous,
Chapter 5: Tactical Communications and Networks
87
• shared situational awareness, thus a payload data rate > 1 Mbps seems
advisable,
• with a range of up to 40 km (as Roke’s Battlefield Connect System) for land
based platforms,
• with multi hop and multi cast capabilities,
• for use within mobile platforms; also with participation of a slow airborne
vehicle; hence, land based: up to 100 km/hr and airborne: up to 400 km/hr,
• secure and IP capable information transport,
• Quality of Service (QoS), from low latency to best effort,
• WF with inherent flexibility and scalability, thus a OFDM WF seems adequate,
• providing a tactical mobile extension of the deployed Protocol Core Network,
• for use within SDRs taking into account SCA conformity,
• with resilience against noise and interference,
• operation within the usual military spectral opportunities (RF bands) and
constraints, and
• COM capability between military and civil forces.
Further require­ments like a certain protection against hostile detection, interception and jamming and the provision for the WF usage in high speed airplanes
or for Radio Based Combat Identification will be desirable. But, the last mentioned
requirements are secondary ones and can be fulfilled later.
These requirements for military use have to be kept in mind while assessing
civil WBWFs. But, it cannot be expected that the civil WFs in their original forms
can fulfill many of the requirements because they have been developed with different, commercially driven goals.
B. Further aspects
Some factors proposed from our side are:
• For the present, half-duplex traffic will be sufficient.
• Multiple Input Multiple Output (MIMO) is not yet necessary, i.e. Single
Input Single Output (SISO) will be sufficient.
• The new WBWF shall be demonstrated on particular simulation and development platforms.
If appropriate transceiver modules will be available later on, the possible
WF characteristics concerning RF, transmitter power, range and mobility will be
exploited.
V. System evaluation and selection of one civil WBWF
for further investigation
As a first step, a pre-election of a few appropriate civil WBWF systems is accomplished. The result is a selection of three modern wireless COM systems with
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Military Communications and Information Technology...
large channel bandwidths. Later on, as a second selection step, one of these systems
is finally selected for further investigation. For the pre-election and the final selection comparable pragmatic evaluation criteria are used.
A. Evaluation and pre-election
For the pre-election, only three significant assessment criteria are employed:
(1) modern wireless WBWF system with
(2) bidirectional information transfer and
(3) easy scalability.
All the newer systems mentioned in Section III are modern WBWF systems,
except the trunked radios. The newer trunk radio versions (TETRA2/3) allow
a maximum channel bandwidth of only 150 kHz. This fact, together with the comparatively low development speed of such radios, will result in the exclusion of this
technology from further analysis for a suitable WBWF.
In the second evaluation criterion, the requirement for bidirectional information transfer is not fulfilled from the broadcast system DVB-T/T2. Additionally,
it has a structure with comparably long frames. Both facts would demand a remarkable work load for modification or replacement of the modules in question.
The consequence is the exclusion of the broadcast systems from further investigations as well.
In the third evaluation criterion, the scalability can be easily fulfilled of OFDM
WFs. Additionally, OFDM has significant advantages relating to robustness within multipath propagation and the flexibility of using its particular frequency channels. So, from the cellular and the wireless access network systems those WBWF
systems with this modulation/multiplex type are the most favored ones for our
further investigations: the newest cellular standard 4G/LTE, the WiMAX IEEE
802.16e and the WLAN IEEE 802.11n.
B. Final selection of one system/WF for further investigation
For accomplishing the final selection, the relevant system characteristics
of the three pre-elected systems are gathered in Table I, whereby the focus is essentially on those system characteristics which are different for the systems. The principally equal characteristics, like the OFDM WBWFs, the bidirectional information
transfer, the use of IP protocols, the multicast capability and the variability of packet
lengths are not picked up in the comparison because they do not contribute to discrimination. The table contents written in red or green colored text will be explained
below. In addition to the technical characteristics noted in Table I, several other
facts concerning the information gathering and the possibilities for modifications
or enhancements are quite relevant for our final system selection. These facts are
collected in Table II below.
Chapter 5: Tactical Communications and Networks
89
Table I. Relevant characteristics of pre-elected COM systems (with focus on those
characteristics which are different between the three systems under consideration)
Characteristics
4G/LTE
Max. channel
20 MHz
bandwidth BWchmax
Max. link data rate
in BWchmax
for SISO operat.
RF bands
DL: 75 Mbps
UL: 18.75 Mbps
WiMAX
802.16e
20 MHz
WLAN
802.11n
20 MHz
(40 MHz)
75 Mbps (150 Mbps)
0.8 and 2.6 GHz
(Europe)
DL: 128 Mbps,
UL: 56 Mbps,
both with FDD
2.3, 2.5 and 3.5 GHz
for mobile operat.
2.4 and 5 GHz
(in U.S. also < 1 GHz)
Bandwidth (data
rate) scalability
Yes;
1.25 … 20 MHz
Yes;
1.25 … 20 MHz
Yes; frequency raster
5, 10 and 20 MHz
Modulation types
QPSK, 16-QAM
and 64-QAM
QPSK, 16-QAM
and 64-QAM
BPSK, QPSK, 16-QAM
and 64-QAM
Multiplex method
DL: TDMA /
OFDMA;
UL: TDMA /
SC-FDMA
No
TDMA/
Scalable OFDMA
(S-OFDMA)
CSMA/CA
No
Yes
Ad-hoc net­work
capabil.
Multi hop relay
capability
No, but handover
capability
Yes
Yes
Transmission of
speech and data
with QoS (priority,
latency, etc.)
Necessity of base
stations
Not yet; the necessary IP multimedia
subsystem is not yet
imple­mented;
Yes
Yes
Yes
In principle:
Yes; the necessary wireless
multi me­dia module is still
included;
No (for the ad-hoc mode);
Real time
capability
Yes;
smallest latency
time 5 ms;
Yes;
smallest latency
time 1 ms
Range
Several kilometers;
under spe­cial
condi­tions up to
100 km
Mobility
Handhelds
usable up tp
350 km/hr
comparatively
autono­mous base
stations;
Several hundred
meters up to 50 km
be­tween fixed stations; up to 15 km
between fixed
and mobile stations
Handhelds
usable up to
120 km/hr
802.16e standard
for mobile
operation;
very expen­sive
infra­structure
expensive infrastructure
Remarks
With restrictions because
of CSMA/CA and relativly
large frame and packet
lengths;
Typically: < 100 m indoor
and ≤ 250 m outside;
with more transmitter
power: several kilometers;
Restricted to walking pace
Eco/sleep mode;
frame aggregation
and block acknoledgment;
mature and broadly used
technology;
cheap in-frastructure
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Military Communications and Information Technology...
Table II. Information gathering and possibilities for modifications and enhancements
Inform. gathering,
possib. for modific.
and enhancements
4G/LTE
WiMAX 802.16e
WLAN 802.11n
Access of source code
and simulation models
Strongly restricted
because of property
rights
Restricted
because
of property rights
PHY: Yes, but
without time sync;
MAC: No
Possibilities for code generation from simulation tools
like MATLAB/SIMULINK
Not available
Only simple PHY
model available
Complete PHY
model available
Possibilities for incremental
modifications and enhancements
Difficult because
of concetanated
PHY and MAC
Difficult because
of concetanated
PHY and MAC
Promising because
PHY and MAC
are separable
Expenditure for modifications and enhancements
Very high
High
Moderate
While the access of system specifications is good for all three systems, the availability
of simulation tools and the possibilities for adaptations and enhancements are different.
As can be acknowledged from Table II, there are clear advan­tages for WLAN 802.11n,
because of (partial) availability of source code and simulation tools and the possibility
to separate the PHY and MAC layers. These advantages alleviate modifications and
replacements of critical modules and are the crucial factors in our decision to select
the WLAN WF for further investigation. For the other two WFs, much work ought
to be invested to reach comparable circumstances. In Table II, the advantageous facts
of WLAN 802.11n are emphasized by using green colored text.
As can be gathered from Table I, WLAN 802.11n has several further advantages
(emphasized with green colored text): the ad-hoc network and multi-hop relay capabilities, the included multi-media module, the independence from base stations,
the eco/sleep mode, the possibility for frame aggregation and block acknowledgment
and the availability of cheap components. The eco/sleep mode can perhaps help
to realize the military required “radio silence”. The frame aggregation and block
acknowledgment reduce the overhead caused by control information.
However, the WLAN WF not only has advantages but also disadvantages which
are emphasized by using red colored text. The main disadvantage is the CSMA/
CA (Carrier Sense Multiple Access/Collision Avoidance) multiplex method which
impedes the required real time information transfer if more than only a few users
are active. Furthermore, CSMA/CA does not efficiently exploit the possible channel
capacity. But, this multiplex method can be replaced as it is discussed in the next
section. The other two emphasized disadvantages, limited range and only pedestrian mobility, are not independent from the multiplex method and can also be
improved concerning the military requirements. Furthermore, the topic of insufficient security has to be worked on in a later study phase.
Chapter 5: Tactical Communications and Networks
91
VI. Ideas for modifications and enhancements of WLAN IEEE
802.11n modules
In this section some critical modules of WLAN 802.11n are discussed and
evaluated for necessary modifications and enhancements. Due to the military
requirements on a data distribution subsystem, WLAN 802.11n (as described
in the specifications) has some disadvantages, e.g. in multiplex method, range and
mobility. In the following subsections, initial ideas for the elimination of these
disadvantages will be presented.
A. Multiplex method
1) Selection of an alternative multiplex method
In WLAN 802.11n, the CSMA/CA multiplex method does not guarantee real
time behaviour and cannot exploit the possible channel capacity due to the fact
that a random based channel access is used. Therefore, multiplex methods with
systematic channel access like Time Division Multiple Access (TDMA), Frequency
Division Multiple Access (FDMA) or Code Division Multiple Access (CDMA)
are better suited. However, CDMA needs an expensive power controller to realize non-discriminatory data transmission between all users, especially mobile
ones. A realisation without fixed base stations would be very demanding. FDMA
is normally reserved for the separation of different user groups. Therefore, at first,
the focus is on TDMA. When replacing the existing multiplex method in WLAN
802.11n, the following requirements need to be preserved or newly fulfilled:
• support of many network nodes,
• IP support and ad-hoc network capability,
• QoS capability, i.e. appropriate latency and priority, based transmission
between nodes, and
• bandwidth efficiency.
As a possible solution, the use of the Unified Slot Assignment Protocol
(USAP) [7, 8] is proposed. USAP is a TDMA based multiplex method with multichannel support. The channel access is managed due to distributed slot assignments, so that random based packet collisions are avoided. With a collision-free
transmission the reliability rises and QoS constraints can be fulfilled more easily.
2) Evaluation of the USAP multiplex method
With the exchange of the multiplex method, an evaluation of the new WBWF
based on the PHY layer scheme of WLAN 802.11n combined with the MAC layer
scheme of USAP is necessary. Therefore, a few equations for estimating the data
rate above the MAC layer are derived.
To estimate the slot time duration Tslot(B) dependent on the packet size B
bytes in TDMA systems, several relevant parameters have to be known. Firstly,
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Military Communications and Information Technology...
an upper bound of the COM distance Dmax that should not be exceeded by any two
node pairs in the existing network is needed. This strict constraint is important to
prevent crossover collisions between neighbouring slots that have been assigned
by the USAP for transmission. Secondly, an additional time period Tadd needs to
be considered which includes time uncertainties on the half-duplex switching
duration, the hard- and software processing delays and for having an additional
spare time. Further, the transmission time for a PHY layer packet of size B needs
to be known. From the WLAN 802.11n specification [9, 10], it is extracted that for
a given channel bandwidth BW, the number of bytes B in the packet and the chosen
modulation and coding schemes expressed in the data bits per OFDM symbol
Ndbps,k is sufficient to calculate the slot duration. The values for Ndbps,k can be
taken from Table III for different modulation and coding schemes, assigned by
the index k. The slot duration Tslot,k(B) for the USAP multiplex method can then
be calculated by:
Tslot,k  B =Tadd +
D max
80 
22+8 B 
. (1)

 6 
c0
BW 
N dbps,k 
The parameter c0 is the velocity of light. The first two terms of (1) stem from
USAP part and the third term is the transmission time for a PHY layer packet
with B bytes corresponding to the WLAN 802.11n specification. Tslot,k is valid
either for a payload or a control time slot. Equation (1) can be transformed such
that the maximum number of transmittable bytes Bk in a single data slot can be
calculated:
 1  BW 

D  

. (2)
Bk = 
Tdataslot  Tadd  max  6  N dbps,k  22
c0  


 8  80 

The next step is the estimation of Tdataslot. USAP has a strict separation of payload and control data. A frame with duration Tframe is divided by mini slots for USAP
control information and by data slots for the payload. The number of available mini
slots in a frame is given by Nbootstrap,slot. Similarly, the number of data slots is given
by the sum of broadcast slots Nbroadcast,slot and reservation/standby slots Nres/stby,slot.
Thus, the duration of a single data slot Tdataslot can be computed by:
Tdataslot =
Tframe  N bootstrap,slot Tslot,k0 (B 
20)
. (3)
N broadcast,slot  N res/stby,slot
For USAP in each mini slot, B = 20 bytes with 16 bytes for USAP control
and 4 bytes for the code redundancy check needs to be transmitted. Tslot,k=0 can be
calculated from (1).
Chapter 5: Tactical Communications and Networks
93
Table III. Number of data bits per symbol dependend on the WLAN 802.11n
modulation and coding schemes [9, 10]
Index k
Modulation
Coding
Ndbps,k
0
BPSK
1/2
26
1
BPSK
3/4
39
2
QPSK
1/2
52
3
QPSK
3/4
78
4
16-QAM
1/2
104
5
16-QAM
3/4
156
6
64-QAM
2/3
208
7
64-QAM
3/4
234
8
64-QAM
5/6
260
The data rate R above the MAC layer can then be computed by:

R
N broadcast,slot  N res/stby,slot
Tframe
 1  Bmean , (4)
whereas ρ is the average slot utilization of the data slot assignment of USAP,
ε is the packet error rate and Bmean is the assumed mean number of bytes in a packet.
As an example, the following parameter values are chosen for an initial estimation of the data rate: BW = 5 MHz, Dmax = 50 km, Tadd = 1 ms, ε = 0.05, ρ = 0.6
and Bmean = Bk=1. Corresponding to [7, 8] the further parameter values are chosen
as: Tframe = 0.125 sec, Nbootstrap,slot = 13, Nbroadcast,slot = 2 and Nres/stby,slot = 8. With
the given parameter values Tdataslot can be calculated from (3) together with (1)
to 10.71 ms. From (2) and Table III Bmean = Bk=1 is calculated to 2873 bytes, and,
together with (4), the data rate R is gained to 1.0235 Mbps. This data rate (above
the MAC layer) can be seen as an initial value which could be adequate for a military
WBWF. It should be emphasized that the corresponding payload data rate will be
smaller and the corresponding link data rate will be remarkably higher.
B. Range
The original WLAN 802.11n system is not specified to reach high ranges.
This disadvantage is part of restriction to the transmission power of at most
100 mW in the 2.4 GHz RF Band, 200 mW in the 5.1 GHz to 5.3 GHz RF band and
1000 mW in the 5.4 GHz to 5.7 GHz RF band (European specification). However,
this disadvantage could be eliminated by use of higher transmit power and by
operating in the military RF bands with lower center frequencies. Due to the fact
that higher transmit power enlarges the total energy consumption of the system,
further improvements of critical modules of the PHY layer, e.g. those for detection,
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synchronization or modulation and coding will be helpful to compensate somewhat
the higher energy consumption.
As a first step, the well-known preamble coding and detection algorithm
from Schmidl & Cox [11] is implemented and analyzed. This algorithm is applicable for WLAN 802.11n. But, in the meantime Minn-Zeng-Bhargava published
a more powerful preamble coding scheme [12], with which better symbol timing
estimations at the receiver side can be achieved. At present, this proposed method
is adapted for use together with the new WBWF.
As a second step, an alternative header coding scheme is analyzed. The WLAN
802.11n uses a BPSK modulation and a convolutional code rate of 1/2 with generator polynomial G = [171 133] for header coding. With this configuration,
the 42 header bits are mapped to two symbols with 48 subcarriers for each symbol.
As an alternative, the use of a QPSK modulation and convolutional coding rate
of 1/4 with generator polynomial G = [171 153 135 127] is considered. Both configurations produce the same total coding rate of 42/96. In an AWGN environment
the new configuration outperforms the classical configuration by around 0.4 dB
as shown in Fig. 3. The use of iteratively based channel coding schemes like turbo
or low density parity check schemes would not result in performance improvements
due to the very short block length of a header with only 42 bits.
Figure 3. Bit Error Rate (BER) and Header Error Rate (HER) dependend on Eb /N0
for two schemes for header modulation and coding; coding rate 42/96
Chapter 5: Tactical Communications and Networks
95
C. Mobility
Another important goal in designing a military WBWF is the mobility capability. WLAN 802.11n is developed only for walking pace. Therefore, some modifications and enhancements, and, also for the simulations, the use of an appropriate
channel model are required. It is decided to use the ITU models pedestrian A and
vehicular B [13] which were officially employed together with the WiMAX development. For taking in account high platform mobility, a channel response with
remarkable values for delay spread, Doppler spread and Doppler shift has to be
provided. Another designing aspect is the choice of the OFDM guard interval
length. It has to be chosen in accordance to the largest expected path delay. WLAN
802.11n uses short guard intervals in terms of 1/4 or 1/8 of a symbol length. For
the case of 1/4 symbol length duration, the resulting time durations are 3.2 µs, 1.6 µs
or 0.8 µs for transmission with those symbol rates which are related to the channel bandwidths of 5, 10 or 20 MHz. But, since here ITU vehicular model B is used,
in which a maximum delay of 20 µs is pre-determined, the guard interval length
of the new WF has to be enlarged to that value.
VII. SCA based implementation of WLAN IEEE 802.11n
In parallel to reviewing the WF, its implementation on a simulation platform,
according to the SCA standards, has to be prepared. Various SCA development
tools are available in the market with which such implementations can be accomplished. For preparing the implementation of a newly designed WBWF, as a first
step, the general SCA based implementation of WLAN 802.11n is accomplished.
A. Modeling – building blocks
The model we propose is depicted in Fig. 4 and contents the following modules. The four green framed boxes on the top are those of the WF in which signal
processing functionality can be added. They are the SCA based resources. The blue
framed boxes below represent the services offered by the SDR platform which a WF
can use. They emulate all the SCA based devices, e.g. the serial port, the sound
card (not shown in Fig. 4) etc. The red framed box in the bottom center represents
the crypto module, which is bypassed in our current simulation environment.
The boxes are individually described as:
• DataReadService is responsible for reading and writing data to and from
the SerialPort Device. It also contains the functionality of using push-totalk, i.e. the communication starts when a particular key is pressed.
• RedPayloadProcessing (RPP) handles the payload and control data on the red
security side.
• BlackPayloadProcessing (BPP) handles the payload and control data on
the black security side.
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• Signal-in-Space (SIS) contains the actual PHY and MAC signal processing code of the waveform. The C++ code of the waveform which is compiled as a static library (e.g.: WLAN signal processing) is integrated within the C++ code of this SCA resource.
• SCA Devices Platform (blue framed boxes at bottom) is a combination
of all the SCA Devices used, see Fig. 4. It includes also further devices like
the sound card etc.
We focus on PHY and MAC which correspond to SIS Resource and BPP
Resource, respectively. But for simplicity, we keep it together in the SIS Resource
in the current implementation. In future, a separation between PHY and MAC will
be accomplished. Also, a subdivision of SIS will be required to run parts of it on
GPP, DSP and FPGA. But currently, everything is kept working on a GPP.
B. Payload workflow
Fig. 4 shows the workflow of the payload data going through the building blocks of the SCA based model of the WF. In the first step, the data to be
transmitted is given to the arrangement using the SerialPort Device. Then it is
read by the DataReadService Resource using the DataRead functionality implemented in it. Then the data is given to the RPP Resource from where it is given
to the SecurityAdapterRed Device. The data from there is given to the crypto
module. From the crypto module it is committed to the SecurityAdapterBlack
Device. The BPP Resource then takes the data from the adapter and passes it on
to the SIS Resource. The actual signal processing of the data takes place in this
component. For the present, the WLAN signal processing functionality is added
into this resource. The processed data are then given to the Transceiver (Trx)
Device which transfers it out to the antenna. In the case of receiving information,
the data flow in the reverse direction.
Figure 4. Building blocks for SCA implementation
Chapter 5: Tactical Communications and Networks
97
C. Separation of red and black security sides and between payload
and control data
the implementation of the WF is done keeping in view the concept of red and
black security separation. This means that the system can be divided into two parts:
• Red side: It handles unencrypted information usually from devices like
keyboard, audio card etc.
• Black side: It handles the information in encrypted form and uses it for
signal processing.
In addition to this concept it is also recommended that there should be a clear
separation between the payload and control information.
• Payload is also called user data. It is that part of the transmitted data which
is the fundamental purpose of the transmission.
• Control information provides the control data the network needs to process
and deliver the user data.
Keeping in view to the above mentioned separations, all the building blocks
on the red side of the system are implemented with a clear payload and control
separation.
D. Defining the interfaces
The interfaces between the SCA based resources and devices, which handles
the payload and control data, have to be defined beforehand.
VIII. Conclusions
After a short glimpse on tactical communications, followed by remarks on
modern civil WFs already in use or tested by military forces, several requirements
on WBWFs for military use are outlined. With the help of only a few significant
assessment criteria, three modern civil WBWF systems are short listed as candidates
for further investigations. In a second assessment step, WLAN IEEE 802.11n is finally
selected, and first critical modules are pointed out for modifications or enhancements. These modules are: the CSMA/CA multiplex method, the preamble coding
and the header coding. The most critical part, the CSMA/CA multiplex method,
could be exchanged by a TDMA based method. Here, USAP is observed closely,
and a first estimate for the expected data rate above the MAC layer is presented.
Furthermore, an enhanced preamble coding with the aim to improve the detectability is considered. Finally, a modulation and coding configuration scheme to
improve the header coding and therefore decrease the header error rate is proposed.
In parallel to reviewing the new WF, its implementation on an appropriate simulation platform with SCA conformity is prepared.
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In future, more critical modules will be investigated, and, if necessary, modified or enhanced. All modules, those taken unchanged from WLAN IEEE 802.11n
and the modified or enhanced ones shall be integrated in appropriate simulation
and development platforms to develop a new military usable WBWF.
References
[1] S. Couturier et al., “Evaluation of wireless civilian communication systems for
military applications”, MCC 2011, Amsterdam, The Netherlands, October 2011.
[2] R. Pengelley, “UK strives for joined-up communic ations networks”, Jane’s International
Defense Review, April 2011.
[3] M.J. Ryan, M.R. Frater, “Tactical communications for the digitized battlefield”,
Artech House, 2002.
[4] “Armada compendium, tactical radios 2010”, Supplement to Armada INTERNATIONAL,
issue no. 3 (volume 34), June/July 2010.
[5] Universal Multi-Media Link, Data Sheet, Thales Defense Solutions and Services, 2009.
[6] R. Pengelley, “Digital delights: Commercial wireless commu­nications on
the battlefield”, Jane’s International Defense Review, September 2011.
[7] C.D. Young, “USAP: A Unifying dynamic distributed multichannel TDMA Slot
Assignment Protocol”, Proc. of IEEE Milcom, vol. 1, October 1996.
[8] C.D. Young, “USAP multiple access: Dynamic resource allocation for mobile multihop
multichannel wireless networking”, Proc. of IEEE Milcom, November 1999.
[9] IEEE Standard for information technology – Telecommunications and information
exchange between systems – Local and metropolitan area networks – Specific
requirements, Part 11: “Wireless LAN Medium Access Control (MAC) and Physical
layer (PHY) specifications”, 2007 (WLAN 802.11a/b/g).
[10] IEEE Standard for information technology – Telecommunications and information
exchange between systems – Local and metropolitan area networks – Specific
requirements, Part 11: “Wireless LAN Medium Access Control (MAC) and Physical
layer (PHY) specifications”, Amendment 5: “Enhancements for higher throughput”,
2009 (WLAN 802.11n).
[11] T.M. Schmidl, D.C. Cox, “Robust frequency and timing synchro­nization for OFDM”,
IEEE Trans. on Com., vol. 45, no. 12, pp. 1613-1621, December 1997.
[12] H. Minn, M. Zeng, V.K. Bhargava, “On timing offset estimation for OFDM systems”,
IEEE Com. Let., vol. 4, no. 7, pp. 242-244, July 2000.
[13] ITU-R M.1225, “Guidelines for evaluations of radio transmission technologies for
IMT-2000”, 1997.
Experimental Performance Evaluation
of the Narrowband VHF Tactical IP Radio
in Test-Bed Environment
Edward Golan, Adam Kraśniewski, Janusz Romanik,
Paweł Skarżyński, Robert Urban
Radiocommunications Department, Military Communication Institute, Zegrze, Poland,
{e.golan, a.krasniewski, j.romanik, p.skarzynski, r.urban}@wil.waw.pl
Abstract: This paper evaluates the efficiency of IP packets transmission in narrowband networks
based on the RRC 9210 tactical radios. Based on measurements performed in the laboratory test-bed
environment, we examined the user throughput for selected length of UDP datagrams under AWGN
channel. We determined BER characteristics vs. channel attenuation. We also found the thresholds
of BER, when the data rate of the radio interface drops automatically as a result of the increased channel
attenuation. We mapped the BER to the data rate of the radio interface. We found that the maximum
throughput offered to the user amounts to 9,1 kbit/s. We also noticed, that the optimum size of UDP
datagrams is equal to 512 bytes. Based on these results, we assessed the efficiency of the RRC 9210
tactical radios in the IP-mode.
Keywords: Tactical radio, tactical VHF communication, BER characteristics, user throughput
I. Introduction
The rapid growth of needs of the C4I systems for the information exchange
enforced specific requirements for the tactical communications systems. As a result,
military solutions within the scope of communications employ broadband technologies, mostly based on the IP protocol. This trend also applies to mobile wireless
systems, which are essential mean of communications in the changing and unpredictable battlefield environment. The effort of researchers and military engineers
is heavily oriented towards IP radio at the tactical level (Tactical Internet) [1,2,3] and
also providing new services, e.g., BFT (Blue Force Tracking) and RFT (Red Force
Tracking). Broadband technology enables to get an increased network throughput,
better reliability and to offer wide range of services. If the network efficiency is high
enough, the set of provided services is almost the same as in the wire network.
Finally, the end user can get full access to the network resources from any place,
using cable connection or wirelessly. This leads to the unification of the interfaces
and applications and thus enables to get high level of interoperability.
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On the contrary, there are narrowband radios that can operate in IP mode.
For example, Polish Land Forces are equipped with tactical VHF radio RRC 9210,
which offers throughput reaching up to 19,2 kbit/s under favorable propagation
conditions [4,5,6,8]. RRC 9210 radios have Ethernet 10/100BaseTX interface.
The latest firmware enables to operate in IP mode.
The advantage of narrowband radios is to get long-range links and stable connections. However, the question about the efficiency of the narrowband IP packets
transmission seems to be justifiable. Until now, this problem was not discussed
widely in the literature. This may result from the fact, that manufacturers do not
provide detailed information of the implemented mechanisms and network performance. Usually, the information is limited to the simple statement, that the Ethernet
interface and IP protocol stack are built-in the radio. From the system designing
point of view it is important to assess properly the network efficiency [7] and to
plan deployment of stations as well as the set of provided services.
In the literature, there are many papers dedicated to the efficiency of the broadband wireless systems. A lot of important issues have been recognized, which may
have strong influence on the efficiency of the IP transmission in narrowband wireless
networks [9,10,11]. They include, e.g., the informational overhead resulting from
the transfer of the data through the complete protocol stack. While transporting
data from the application layer to the physical layer, headers coming from successive layers are added and consequently the size of the transmitted data increases
significantly [8]. In the physical layer the management and control frames such
as RTS, CTS or ACK are exchanged, which also limits the available bandwidth.
Moreover, apart from the user data, additional information is transmitted, e.g., synchronization, link test.
If the TCP transmission is considered, then the size of the packets amounts to
several hundred of bytes. In the physical layer packets are fragmented and typically
are send as a few frames. This process has a significant impact on the transmission
delay, especially in case of errors and retransmissions. In addition, one of the critical parameters of the TCP protocol is Timeout. The value of the Timeout should be
adjusted to the narrowband channel characteristics.
Taking into account issues mentioned above, the detailed test of VHF radio
are strongly required. Such tests will help to assess the efficiency of IP packets
transmission in destructive military environment. The test results will provide
the necessary information about the rules of the radio configuration, the network
capacity, recommended deployment of nodes and set of offered services. Moreover,
it will be possible to determine the typical throughput offered to the user.
The paper is organized as follows. In Section II the narrowband VHF communication was characterized. Section III describes the IP modes of RRC 9210
radio. In Section IV the test scenario and assumptions were presented, while
in Section V test results are discussed. Section VI contains conclusions and Section VII future work.
Chapter 5: Tactical Communications and Networks
101
II. Narrowband VHF communication
Fast development of technologies to support reliable data transmission through
radio channels was caused by increasing demands on services quality offered by
wireless communication systems. It is not easy to realize such expectations because
of transmission channel, which is changeable in a random way. Signal sent from
transmitter can reach receiver by multiple propagation paths at different delays
as a result of reflection from terrain obstructions. It causes random fluctuations
in the received signal’s amplitude and phase briefly called multipath fading, which
results in burst errors in received signal [9]. This phenomenon depends on type
of environment and radio mobility, thus location can have important influence on
signal reception.
Transmission channel of tactical system based on VHF radio is narrowband
(usually 25 kHz) and is characterized by flat fading. Such fading occurs when coherence bandwidth is greater than signal bandwidth and causes signal suppression [10].
VHF transmission is very suitable for short distance terrestrial communication,
only slightly farther than the line of site from the transmitter to the receiver.
The requirements that must be fulfilled by modern military communications
systems indicate, that the development of such systems is focused on broadband
technologies, mostly based on IP protocol. Classical tactical radio networks are
organized with the use of narrowband transceivers operating in the VHF and HF
frequency range. In such case radio communications between multiple correspondents is provided in omni-directional mode and integration with wired systems
is realized by means of Radio Access Points (RAPs).
Implementation of TCP/IP protocol stack in tactical radios caused positive
changes in operation of radio networks. Transmission reliability increases, because
IP radio network can operate despite partial destruction or jamming and does not
need integrating devices such as RAPs.
III. IP modes of RRC 9210 tactical radio
Polish Land Forces are equipped with tactical VHF F@stnet family radios,
which includes the RRC 9210 manpack radios with maximum power of 10W and
vehicle version called RRC 9310AP with output power increased to 50W. These
radios have Ethernet 10/100BaseTX interface and may operate IP mode if the latest
firmware is used. From these reasons RRC 9210 manpack radios were selected to
evaluate the performance of IP packets transmission in VHF channel.
RRC 9210 tactical radio offers two IP modes:
• IP-MUX (Simultaneous Voice & Data over IP mode).
• IP PAS (data packet over IP mode).
IP-MUX mode replaced older MUX mode, which was designed to transmit/
broadcast voice and IP data on the same radio channel. It offers simplex data
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transmission or transmission in TDMA triggering mode with the data rate up
to 4,8 kbit/s. Synchronization of hopping is made by station, which play NCS
role in the network. Though, core synchronization type assumes using GPS.
Switching of the station to IP-MUX mode is initialized by an operator. Addition
as well as removal of SUB stations can be commanded at any time via NCS station. The automatic selection of routing tracks is provided in the latest version
of the firmware. To provide interoperability with previously delivered radios,
a firmware update is necessary.
IP PAS mode was designed only for data transmission with the data rate up to
19,2 kbit/s. The hopping synchronization is triggered in distracted mode without
NCS station (there is no primary station in the network). Retransmission of data
can be done with maximum 5 hops, Fig. 1. In that case, one radio serves also as relaying node. Such functionalities provide significant extension of network coverage,
although, it costs notable increase of the transmission delay. Therefore, this mode
is dedicated to non-real time services only.
Figure 1. RRC 9210 in IP PAS mode
Figure 2. Test-bed configuration
Chapter 5: Tactical Communications and Networks
103
Network topology to which radio station belongs is updated automatically.
As a result each station “knows” its surroundings. If there is lack of direct connection, then the most optimum neighboring node is selected automatically to play
a role of a retransmitting station.
IV. Test scenario and assumptions
The aim of the tests was to assess the efficiency of IP packet transmission of RRC
9210 radio in IP mode. The test-bed consisted of two radio terminals configured
in a point-to-point connection, which is shown in Fig. 2. The first step in the test
was to select one of two IP modes of the radio. After preliminary test the IP-PAS
mode was chosen, as it allocates all of the available bandwidth for data transmission
only. Therefore, this mode allows to measure the available bandwidth in a simple
point-to-point connection, Fig. 2.
Figure 3. BER vs. channel attenuation
All tests were performed in laboratory environment, which guarantee repeatability and stability of experiments. AWGN channel was used as a simulator
of transmission channel. It does not generate burst errors, typical for real VHF
channel, but only errors distributed at a uniform rate. However, to determine BER
characteristics, such test-bed configuration was acceptable. In the next step of tests,
the model of VHF channel with multipath fading will be used.
The channel attenuation was changed from 130 dB to 140 dB with the step
of 1 dB. The output power was set to +27 dBm in each radio. Thus, the signal level
on the receiver site was equal to:
• –103 dBm – maximum signal level.
• –113 dBm – the level of sensitivity.
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Figure 4. User throughput
Radio was configured in the IP-PAS mode with adaptive data rate and correction code. End-points were the terminals with specialized software to generate
and analyze an IP traffic.
Bit error rate (BER) parameter was chosen as a channel quality metric. Therefore, BER was measured for each value of attenuation level. Fig. 3 presents BER
characteristics vs. channel attenuation.
Next step of experiments was to determine the available bandwidth vs. the channel quality. To measure the maximum user throughput, UDP protocol was used,
as it does not need acknowledgements and retransmissions [11]. If TCP protocol
were chosen, measured throughput would not be easy to analyze, because of additional acknowledgements in transport layer.
To sum up the scenario, the quality of VHF channel was changed by selection
of the attenuation level. The source terminal generated UDP datagrams of 512 Bytes
size to achieve the traffic of 12 kbit/s.
V. Test results
Fig. 4 shows the user throughput measured for different BER levels. For each
period of 20 seconds the throughput was measured and then the average value
was calculated. After each 10-minute period of measurement, the level of BER
was changed by selection of the channel attenuation level. In Fig. 4 'n' denotes
the number of consecutive 20-second periods. The maximum user data throughput
amounts to 9,10 kbit/s on average. In that case, RRC 9210 operates with maximum
data rate. Because the channel is of high quality (BER from 4,02*10-7 to 8,45*10-6)
Chapter 5: Tactical Communications and Networks
105
the redundancy of correction codes is low. When the BER achieve the level of 8,18*10-5,
then the user data throughput drops to 3,59 kbit/s. This is a result of applying
of correction codes with higher redundancy. When the channel quality is worsen
again and BER achieves the level of 1,88 *10-2, then the user throughput drops to
1,49 kbit/s. In such a situation, radios detect high level of errors and apply stronger
correction code. Further increase of the channel attenuation makes that the transmission is impossible and finally connection between two radios is simply lost.
VI. Summary
In this paper the efficiency of IP packets transmission in narrowband networks
based on the RRC 9210 tactical radios was evaluated. Based on measurements
performed in the laboratory test-bed environment, the user throughput was determined under AWGN channel. The maximum throughput offered to the user
amounts to 9,10 kbit/s in point-to-point connection. The minimum throughput
amounts roughly 1,5 kbit/s. The thresholds of BER when the data rate of the radio
interface drops automatically were determined. These BER levels allow to estimate
the network throughput in a given propagation conditions.
These results will be helpful to assess the channel quality in a real environment
and to predict user throughput. Finally it will enable to define the set of offered
services. Results of experiments presented in this paper allow to presume, that
the network will not be efficient for real time services. Only non-real time services
will be offered, like chat, e-mail, short message transfer or FTP. However, the range
of provided services in a real environment needs to be confirmed by additional
experiments.
VII. Future work
For future work it would be interesting to examine the efficiency of the IP
transmission in real environment. Based on the test results, we are going to investigate the network capacity, the set of offered services and also the rules of device
configuration for IP-mode.
Furthermore, we would like to devote attention to the aspect of the relaying
and test the efficiency of the network, when nodes operate on large area.
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References
[1] Collective work edited by M. Amanowicz, “Zaawansowane metody i techniki
tworzenia świadomości sytuacyjnej w działaniach sieciocentrycznych” (Advanced
methods and techniques for creating situational awareness in the network centric
activities).
[2] B. Marczuk, “Radiostacje szerokopasmowe” (Broadband radio stations), The Land
Forces Review 03/2006.
[3] L. Stypik, “Cyfrowe wsparcie pola walki – przyszłość czy rzeczywistość” (Digital
battlefield support – the future or reality), The Land Forces Review 10/2010.
[4] M. Gruszka, “Taktyczny Internet w praktyce” (Tactical Internet in practice), The Land
Forces Review 01/2011.
[5] E. Golan, A. Kraśniewski, J. Romanik and P. Skarżyński, “Ocena potrzeb
i możliwości wykorzystania szerokopasmowych radiostacji osobistych i pokładowych
na szczeblu taktycznym” (Assessment of needs and possibilities of using the broadband
on-board and personal radio stations at the tactical level), Journal of KONBIN, Safety
and reliability systems, Warsaw 2011.
[6] D. Pawłocki, “ W stronę transmisji danych z protokołem IP” (Towards a data
transmissions including the IP protocol) The New Military Technology 09/2011.
[7] J. Dudczyk, “Optymalny dobór parametrów radiostacji szerokopasmowej żołnierza”
(Optimal selection of parameters of the soldier’s broadband radio station) NTV
no. 9/2011.
[8] J. Romanik, P. Gajewski and J. Jarmakiewicz, A Resource Management Strategy
to Support VoIP across Ad hoc IEEE 802.11 Networks, ThinkMind Digital Library,
Proceedings of The Fourth International Conference on Communication Theory,
Reliability and Quality of Service, April 17-22, 2011, Budapest, Hungary, pp. 15-21,
ISBN 978-1-61208-005-5.
[9] R. Urban, “Metoda określania średniej długości paczek błędów w kanale UKF w oparciu
o analizę zmian wartości BER” (The method of determining mean length of error
bursts in VHF channel based on fluctuation analysis), Biuletyn WAT, Warszawa 2007,
vol. LVI, s. 433-440.
[10] B. Sklar, “Digital Communications: Fundamentals and Applications” (2nd Edition),
Prentice-Hall, Upper Saddle River, New Jersey 2004.
[11] W. Wysota and J. Wytrębowicz, “End to End QoS Measurements of TCP
Connections”, PPAM’07 Proceedings of the 7th international conference on Parallel
processing and applied mathematics, Springer-Verlag Berlin, Heidelberg 2008.
Hybrid Error Detecting and Correcting System
Using Hardware Associative Memories
Ion Tutănescu, Constantin Anton, Laurenţiu Ionescu,
Gheorghe Şerban, Alin Mazăre
Faculty of Electronics, Communications and Computers – University of Pitesti, Romania,
{gheorghe.serban, constantin.anton, laurentiu.ionescu, ion.tutanescu, alin.mazare}@upit.ro
Abstract: This paper presents a solution of design and implementation of a hardware error
correction and detection system using associative memories. This type of memory allows search
of a stored binary value, having as an input data a partial (or modified) amount of this value.
This property can be used in communication, for detection and correction of errors. In our
experiments the encoder just associate message with corresponded word code, which is sent
to communication channel. The decoder can associate back the word code received from communication channel with the message, even if the received word code has errors. This is due
to associative memory recognition ability. Experimental results obtained were compared with
performances of other hardware systems.
Keywords: associative memories, error detecting and correcting codes, Field Programmable Gates
Array (FPGA), Hybrid Automatic Request (H-ARQ)
I. Introduction
Errors’ correction and detection is a very important feature in modern
communications. There are several methods for error correction and detection.
BCH codes (Bose, Chaudhuri, and Hocquenghem) are widely used in communication networks, computer networks, satellite communication, magnetic and
optic storage systems.
In this paper we present a hybrid ARQ (Automatic Request) and FEC (Forward
Error Correction) solution using hardware associative memory.
BCH codes operate over finite fields or Galois fields. BCH codes can be defined
by two parameters that are: length of code words, n, and the number of errors to
be corrected, t.
The BCH codes are a class of cyclic codes whose generator polynomial is a product of distinct minimal polynomials corresponding to
α, α2, …, α2t,
where   GF  2m  is a root of the primitive polynomial g(x)[1].
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An irreducible polynomial g(x) of degree m is said to be a primitive if only
if it divides polynomial form of degree n,
for no n less than 2mm 1 . In fact,
every binary primitive polynomial g(x) of degree m is a factor of x 2 1 1 [2].
In our application we present two Hybrid ARQ solutions. First use a polynomial of 10 degree, which can be used to correct 2 erroneous bits and detect 5,
in a word code of 20 bits size for 10 bits size message:
g ( x )  x10  x 7  x 6  x 4  x 2 1 (1)
This polynomial is used to generate word codes with 5 Hamming distance.
Secondary, we use a polynomial which generates word codes with 10 Hamming distance, which can correct 4 erroneous bits from 5-bits size messages and
20 bits size word code. This solution has a very high correction capacity (from
5 bits message can correct 4 erroneous bits) and can detect all errors from message:
g ( x )  x15  x14  x13  x12  x10  x 8  x 5  x 4 1 (2)
Field-Programmable Gate Arrays (FPGAs) have become one of the key digital circuit implementation media over the last decade [3]. One bit patterns will
produce operational circuits and can be used in many areas like the communication systems. Our hardware scheme is based on polynomial generator for errors
detection and correction.
FPGA circuits represent a compromise between circuits with microprocessor and ASIC circuits (Application Specific Integrated Circuits) [4]. First, they
present flexibility in programming, called here reconfiguration, which is a feature
for microprocessors.
Even if FPGA cannot be programmable while operation, they can be configured
anytime is needed, having a structure based on RAM programmable machines, as we
see in Figure 1. On the other hand, they allow parallel structures implementation,
with response time less than a system with microprocessor.
Figure 1. Communication system with hardware errors’ detection and correction
The system proposed in this paper is based on the use of reconfigurable FPGA circuits for hardware implementation of error detection and correction algorithms.
Chapter 5: Tactical Communications and Networks
109
Our new design system can be integrated inside a multilayer protocol communication. Thus, hardware module takes the tasks from others components
of communication system (for example from the computers).
In the next section we present the hardware circuits for encoder and decoder.
The first presented solution is a simple binary decoder structure while the second
is based on a hardware linear associative memory.
Section 3 present experimental results obtained after implementations for
both H-ARQ solutions. The implementation is done on a FPGA circuit.
II. ENCoder and decoder
We designed the encoder and decoder using dedicated binary circuits. Thus,
the encoder will be attached as a physical device to any system which transmits
dates to a communication channel while the decoder will be attached to the system
which receives the dates.
In this section we describe the operation of the two systems and our design
method proposed for them.
A. Encoder
The operation of encoder is illustrated in Figure 2.
Figure 2. Encoder operation flowchart
According to the first solution, the message is received from the emitter system.
The message can be received serially (Ethernet or USB) or in parallel if the emitter use a protocol defined by itself. The experiments were performed using 5-bits
size message words (thus can be encoded letters from Latin alphabets) and 10-bits
message words (which can contain extended ASCII charset).
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Each message has associated a word code. To 10 bits-size message word we
have a Hamming distance 5, which means that we can correct 2 erroneous bits and
can detect 5 erroneous bits. We generate word codes for all 10-bits size combinations. Thus we have 210 locations in memories to encoder and decoder.
In fact, the communication encoder is designed as a simple binary decoder.
The messages are generated in a linear increasing manner and word code is obtained
by multiplying message polynomial with generator matrix (see Figure 3).
The word codes are equidistant in Hamming space (distance is 5). This orthogonally property is required in order to have the same error correction and
detection capacity to each received word code.
Figure 3. Association between message and BCH word code to the encoder
The second solution uses 5-bits size message, which are encoded with 20-bits
size word code. The Hamming distance between word codes is 10 so we have a correction capacity of 4 erroneous bits.
The encoder is designed in the same way as in previous solution and the number
of location is 25. Thus, in both cases, we have a very reduced hardware structure
for encoder.
B. Decoder
Decoder, on the other hand, has a more complex structure. This is because
it also has the error detection and correction function. The decoder can match
received words with expected word codes but, in some cases, mistakenly received
word is not found in any of the stored ones.
Error correction would actually identify the correct word code. This involves
finding the closest word in memory. Once identified, it will determine what message is received.
Tasks to be performed at the receiver are presented in the following chart
(see Figure 4).
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Figure 4. Decoder operation flowchart
First, the word code is taken from the communication channel. In our
experiments the word code is of 20 bits size, for both solutions. This word code
is then compared with all stored words. It is a binary comparison. Finally, we
find out which are the erroneous bits. Each memory cell counts the erroneous bits and we can determine minimal value from all locations. The location
which contains an word code with minimum erroneous bits is associated with
the correct message.
The associative memory has a more complex structure that the encoder (see
Figure 5). Thus, each location consists in a binary comparing circuit, as it is illustrated in Figure 4, and in a bits counter (compressor). This compressor count
“1” bits from the comparator output. There’s also a register in which are stored
word code and message.
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Figure 5. Structure of location in associative memory to the decoder
The message with erroneous bits will be transmitted as a 10 or 15 bits size
data pair (5 or 10 bits message and 5 bits number of erroneous bits – from 20 bits
of received word code). All these data pairs, at each location separately, will be
compared to determine the minimum. For this operation we use a combinational
network which gets the minimal value of number of erroneous bits, as is illustrated
in Figure 6.
Figure 6. Minimum computation circuit
The data pairs from the associative memory are applied to left side (x axis).
To y axis we find the data pairs with a minimal value of erroneous bits, which travel
to bottom side of circuit.
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113
The minimum cell (Min), which enters in the structure of the circuit, compares
the number of erroneous bits from two data pairs and selects only the data pair
with the minimum number of erroneous bits.
III. Experminental results
Since it’s a hardware system used to detect and correct errors in communication, items that are considered for performance analysis of this system are given
by response time, area occupied on silicon surface and communication speed.
In our experiments, we tested two different solutions. The first solution encodes
large message package (10 bits) and can correct a small number of erroneous bits
(2 errors). The second solution encodes a smaller message and has a greater error
correction capacity. Implementation was performed to a FPGA Xilinx Spartan 3
family, a low cost family circuit (3-20$ per chip inside the family). A complete
communication system will integrate both encoder and decoder on the same chip.
We will analyze them separately.
In our analysis we take in account different members of Spartan 3 FPGA family. The logical cell (Look Up Table, LUT) and the logical-routing cell (Slice) are
the same structure for all members inside the family. The differences come from
the number of logical and routing cells integrated on chip.
For example, XC3S50 FPGA has 1728 logic cells and 768 logical-routing cells,
while XC3S1000 has 17280 logic cells and 7680 logic – routing cells. Of course,
the price reflects this difference. For a minimum cost we implement the systems
to the smallest and cheapest circuits inside family.
Thus, the communication encoder consists in a simple binary decoder. Its
structure is very simple, in terms of hardware, occupying approximately 1% of all
chip resources for a XC3S50 chip. So we use, for encoder of both solution XC3S50
circuit (3 $/chip).
Table I. Results from encoder and decoder implementation – synthesis report
Method
Circuit
H-ARQ
w = 20,
m = 10
Encoder
Used slices 10 – 1.3% capacity of XC3S50
Used LUTs 17 – 1.0% capacity of XC3S50
10.926 nsa
Decoder
Used slices 6550 – 84% capacity of XC3S1000
Used LUTs 11494 – 66% capacity of XC3S1000
24.048 nsa
Encoder
Used slices 9 – 1.17% capacity of XC3S50
Used LUTs 15 – 0.87% capacity of XC3S50
9.280 nsb
Decoder
Used slices 466 – 60.68% capacity of XC3S50
Used LUTs 812 – 47% capacity of XC3S50
21.573 nsb
H-ARQ
w = 20,
m = 5
a) for 10 bits of message
b) for 5 bits message
Area on silicon (XC3S)
Response time
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The circuit simplicity implies a very small response time of 9.2 ns for each
5-bits size message or 10.9 ns for each 10-bits size message (see Table I). So, when we
use 5 bits communication means communication at 1 Gbps for encoder. The same
speed is for 10 bits message.
The decoder, more complex than the encoder, still has a simple structure
consisting only of combinational circuits arranged in an array. Comparison operation between the input data into the associative memory (word code received from
the communication channel) and the word code stored is performed in parallel
for all locations.
Therefore, circuit’s complexity is increased by the number of locations. To
10 bits message solution we have a number of 1024 different location. This number
is reflected in capacity of 84% allocated from XC3S1000 logical resources. The decoder implemented for second solution, with 5 bits per message but highest error
correction capacity can be implemented in the smallest XC3S50.
On the other hand, the response time for both decoders is approximately
the same (24 ns – 10 bits, 22 ns – 5 bits). This happens because all the cells respond
in parallel (this value doesn’t take in account the response time of minimum circuit).
We have a communication speed for 10 bits message packets (encoded in 20 bits
word code) of 416 Mbps and for 5 bits message (encoded in 20 bits word code)
the speed is 227 Mbps. First solution makes correction for 2 erroneous bits and
the second solution makes correction for 4 erroneous bits.
Because of coding it is a reduction in speed of communication with 1/4 (5-bits
message is encoded with 15-bits word code) or ½ (5-bit size message is encoded
with 20-bits word code). For example, if communication is to 1 Gbps the real communication speed is 333 Mbps. However, in our approach, this reduction is not
presented because of parallel computation of entire message word.
Our system can be integrated in Ethernet communications, as additional
services added to physical layer (see Figure 7).
Figure 7. Hardware correction layer services by using our BCH encoder-decoder
Interposition of this circuit in Ethernet communication system will automatically corrected 2-4 error bit locally. Thus, it provides error correction services for
Chapter 5: Tactical Communications and Networks
115
protocols in the highest layer. The TCP services will be significantly relieved of task
that, in other circumstances, would have their back. To increase the communication speed, we can use a “secured” UDP communication, because we have already,
from physical layer, error correction services.
Compared with other structures, based on hardware implementation of error
correction circuit, performance has improved in this version (see Table II, where
SR-ARQ means Selective Repeat Automatic Repeat Request).
Table II. Comparison with other hardware implemented error correction circuits
Method
Correction (t) and detection
(f) capability
Communi-cation speed
Parallel computing, classic
t = 1 erroneous bit
f = 0
149 Mbps
Hardware BCH SR-ARQ
t = 3 erroneous bits
f = 7 erroneous bits
87 Mbps
Associative memory ARQ
(w = 20, m = 10)
t = 2 erroneous bits
f = 5 erroneous bits
416 Mbps
Associative memory A­­­RQ
(w = 20, m = 5)
t = 4 erroneous bits
f = 10 erroneous bits
227 Mbps
Thus, we have a parallel computing circuit which corrects 1 erroneous bit, with
no detection, presented in [5]. Because of parallel computing structure we have
here a 149 Mbps communication speed. Another circuit uses a hardware hybrid
BCH SR-ARQ error correction and detection algorithms [6].
IV. Conclusions
Our solution presents a modern method concerning implementation of error
correcting codes with associative memories. Associative memories allow a very easy
design for error correcting codes and obtain good performances, both in detection
and correction. Also, they provide a very high response speed because of parallel
processing.
Our method, presented in this paper, consist in storing a set of word codes
and the message associated with them. The correction is achieved by comparing
the received word code with all stored word codes and by selecting the nearest word
code, in terms of Hamming distance. Also this method allows us detection of errors while the correction is performed, increasing computing speed. Our method
improves the capacity of communication channel in Ethernet communication.
In future works we will implement in FPGA error correcting codes with
a higher power correction and detection. We will test these new error correcting
systems in order to measure their throughput and to decide their capacity to work
in real conditions.
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References
[1] M.Y. Rhee, “Error Correcting Coding Theory”, McGraw-Hill, Singapore, 1989.
[2] S. Lin, and D.J. Costello Jr., “Error Control Coding”, Prentice-Hall, New Jersey, 1983.
[3] J. Rose, S.D. Brown, R.J. Francis, “Field Programmable Gate Arrays”, Kluwer Academic
Publishers, 1992.
[4] T. Fogarty, J. Miller, and P. Thompson, “Evolving digital logic circuits on Xilinx
6000 family FPGAs,” in Soft Computing in Engineering Design and Manufacturing,
P. Chawdhry, R. Roy, and R. Pant (eds.), Springer: Berlin, 1998, pp. 299-305.
[5] L. Ionescu, C. Anton, I. Tutănescu, A. Mazăre, G. Şerban, “Hardware
implementation of BCH Error Correcting Codes on FPGA”, International Journal
of Intelligent Computing Research (IJICR), vol. 1, Issue 3, ISSN 2042-4655, Published
by Infonomics Society, p.148-153, September 2010.
[6] G. Şerban, C. Anton, L. Ionescu, I. Tutănescu, A. Mazăre, “Implementation
of a 64-bit hybrid SR-ARQ algorithm on FPGA”, Proceeding of International Conference
on Applied Electronics, ISBN 978-80-7043-987-6, ISSN 1803-7232, IEEE Catalog
Number CFP1169A-PRT, p. 337-340, Pilsen, 7-8 September 2011.
Concurrent Error Detection Scheme
for HaF Hardware
Ewa Idzikowska
Poznań University of Technology,
pl. M. Skłodowskiej-Curie 5, 60-965 Poznań, Poland,
[email protected]
Abstract: HaF (Hash Function) is a dedicated cryptographic hash function considered for verification
of the integrity of data. It is suitable for both software and hardware implementation. HaF has an iterative structure. This implies that even a single transient error at any stage of the computation
of hash value results in a large number of errors in the final hash value. Hence, detection of errors
becomes a key design issue. In the hardware design of cryptographic algorithms, concurrent error
detection (CED) techniques have been proposed not only to protect the encryption and decryption
process from random faults but also from the intentionally injected faults by some attackers. In this
paper, we show the propagation of errors in the VHDL model of HaF-256 and then we propose and
analyse some error detection schemes. In proposed CED scheme all the components are protected
and all single and multiple, transient and permanent bit flip faults will be detected.
Keywords: hash function, HaF, S-box, concurrent error detection, hardware redundancy, time redundancy, DWC
I. Introduction
A hash function H is a transformation that takes an input m and returns
a fixed-size string, which is called the hash value h (that is, h = H(m)). Hash functions with just this property have a variety of general computational uses, but
when employed in cryptography, the hash functions are usually chosen to have
some additional properties, e.g. H(m) must be relatively easy to compute for any
given m, one-way and collision-free [11]. Very important role of a cryptographic
hash function is in the provision of message integrity checks, digital signatures,
password storage and verification etc.
Hash functions are computationally complex, and in order to satisfy the high
throughput requirements of many applications, they are often implemented by
means of VLSI (Very Large Scale Integration) devices. The high complexity of such
imple­mentations raises concerns regarding their reliability. There is a need to develop methodologies and techniques for designing robust cryptographic systems,
and to protect them against both accidental faults and intentional intrusions and
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attacks, in particular those based on the malicious injection of faults into the device for the purpose of extracting the secret information [3] and [4]. If an attacker
deliberately generates a glitch attack, causing a flip-flop state to change or corrupt
data values when they are transferred from one digest operation to another, even
a single fault can result in multiple errors in the hash value computed. The severity
of the problem necessitates detection of errors a key design issue. A digest round
consists of several operations. Errors can creep in at any of these operations and
can affect one or several bits at any of the operations in a digest round. As HaF
is considered for use in security services, concurrent error detection is very important. This necessitates an analysis on the propagation of error from the point
of origin to the output.
CED has certain associated penalties such as hardware cost and the performance degradation due to interaction between the circuit and the detection logic,
which need to be considered while designing the error detection circuit. The design goal of the CED is to achieve 100% error detection with minimal penalty.
CED techniques involve redundancy in the form of hardware, time or information. A CED circuit based on hardware redundancy can for example duplicate
the complete circuit. It means that hardware overhead is more than 100%. In time
redundancy, the same hardware is used to perform both the normal computation
and re-computation using the same input data [9]. The advantage of this technique
is that it uses minimum hardware. The drawbacks of this technique are that it entails ≥100% time overhead and it can only detect transient faults. In information
redundancy technique, data are appended with additional bits and a coding scheme
is used to detect errors. Coding techniques marginally increase the hardware as well
as performance overhead. Combinations of the above techniques are also employed
to minimize the overhead for CED [1] and [5].
In this paper the propagation of errors in HaF-256 is studied. We take into
consideration single, transient as well as permanent faults injected at different
stages of hash value computation. It is found that even a single error injected resulted in half the bits of hash value being in error and the errors are spread across
the computed hash value. Next we focused on CED techniques and proposed error
detection schemes to protect basic operations such as multiplication mod (2n+1),
addition modulo 2, addition modulo 2n There is also presented schemes to protect
S function, step function and round function of HaF-256. The proposed approaches
are tested and the results are presented.
This paper is organized as follows. Sec. II presents family HaF of hash functions. There is shown a method of one block processing, round function, operations
of step function. In Sec. III error analysis is carried out to understand the effect
of an error injected into the hash computation circuit. Error detection schemes,
possible faults and faults models are described in Sec. IV. Simulation results are
presented in Sec. V. An error detection scheme for the HaF circuit is proposed
in Sec. VI. Concluding remarks are in Sec. VII.
Chapter 5: Tactical Communications and Networks
119
II. Family HaF of hash functions
The family HaF is formed of three hash functions: HaF-256, HaF-512 and
HaF-1024, producing hash values of the length equal to 256, 512 and 1024 bits,
respectively. The general model for HaF is presented on Fig. 1 [2].
The original message m has to be formatted before hash value computation
begins. After formatting the message m we have the message M. This massage
is divided on blocks M0, M1, …, Mk–1. Each block Mi is processed with the salt s by
the iterative compression function φ. After processing all blocks we receive the hash
value h(m) = Hk as the result.
The length of formatted message M should be a multiple of 16n bits. It means,
that the length of the input block equals 16n bits, where n is a parameter depending of the hash value we want to obtain. The parameter n equals 16, 32 and 64 bits
for HaF-256, HaF-512 and HaF-1024 respectively. We consider HaF-256 function
in this article. It means, that n = 16.
Figure 1. Model for hash function HaF [2]
The block Mi is processed in two rounds. The method of one block processing
is depicted on Fig. 2. Mi, Hi and s are inputs for compression function. The parameter
n indicates the length of the working variable Ar, rÎ{0, 1, …, 15}.
The round function (Fig. 3) has two inputs Ni, Hi, (H0 = IV is an initial value)
and two outputs Ni*, Hi*. Before processing in round #1, the block Mi is modified,
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Ni = MiÅ s, where s is a salt, |s| = 16n (Fig. 2). In the round #l (lÎ{1, 2}) four least
significant bits of Ni indicate the number of bits the string Ni is rotated to the left
(<<lsb4(Ni)) and added (mod 2 of respective bits) to Hi. Next the block Hi Å (Ni<<
lsb4(Ni)) is divided into 16 subblocks of equal length: A0, A1, …, A15. They are
processed by a step function and after processing they are concatenated giving Hi*.
The output Ni* = Ni << lsb4(Ni). Before processing in the round #2 the blocks are
permuted: Ni = Hi* and Hi = Ni*.
Figure 2. Method of one block processing [2]
Figure 3. Round function [2]
Each round consists of 16 steps. The step #j is indicated by the integer jÎ{0,
1,…,15}. Operations creating a function Fj are performed in each step (Fig. 4.).
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121
Figure 4. Step function Fj [2]
In the Fig. 4. the following notations are used:
a ⊙ b
– multiplication mod (2n+1) of n-bit non-zero integers a and b,
v << t–
t-bit left rotation of a string v, |v| = 16n,
|v|
– the length in bits of a string v,
v Å w
–bitwise XOR of strings v and w, |v| = |w|,
v w
–addition mod 2n of integers represented (in base 2) by strings
v and w,
p1(x) Ä p2(x)–multiplication of polynomials p1 and p2 modulo an irreducible
polynomial R(x),
Ar
– working variable, r = 0,1,…,15,
c
– masking constant,
α0, α2, α3, α5 –the polynomials,
Sj (n)
– substitution function; it consists of four S-boxes S0, S1, S2 and S3,
each of dimension 16×16, working in such a way that for n = 16,
Sj(16) = S(j) mod 4.
After processing in two rounds the value Hi*of chaining variable is splitted
into 16 subblocks A0, A1, …, A15 of equal lengths and each of them is modified
by adding (mod 2n) to it the respective input subblocks of Hi being inputs to
the round #1. Next, all subblocks A0, A1, …, A15 are concatenated giving Hi+1 = A0
|| A1 || … || A15 (Fig. 2.).
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III. Error propagation
Error propagation analysis is carried out to understand the effect of an error
injected into the hash computation circuit. Error was injected in:
• the inputs of a round function,
• the inputs of step function.
Experiments were conducted by injecting a single bit flip error in one of the inputs of round function or step function and obtaining the number of erroneous bits at the output of the same block. The errors were introduced at different bits
randomly in every step and the number of bits that were in error was computed.
As an example we show the propagation of error injected in first step of round #1
in subblock A13. Instead value F9DD is the faulty value F95D. Output values A0,
A1, …, A15 after processing each of 16 steps of round function, are shown in Table I.
The faulty values are depicted by bold, italic font.
One faulty bit in the step #0 of round function causes about 43% faulty bits
in the last, #15 step. Faulty bit in the input of round #1 of round function causes
49% faulty bits in the output of round #2.
This analysis helps us in choosing suitable error detection schemes.
Table I. Error propagation in round function of HaF-256
Faulty free input value A0, A1, …, A15; A13 = F9DD
076A3663D2541F389E94CA4E1A759C92ED3282E55F31FC1514A6F9DDBA029ABE
Faulty input value A0, A1, …, A15; A13 = F95D
076A3663D2541F389E94CA4E1A759C92ED3282E55F31FC1514A6F95DBA029ABE
Step
Faulty outputs
0
3663D2541F389E94CA4E1A759C92ED3282E598AFFC1514A6F95DBA029ABEDFA8
1
D2541F389E94CA4E1A759C92ED3282E598AF0AFE14A6F95DBA029ABEDFA8C321
2
1F389E94CA4E1A759C92ED3282E598AF0AFE530AF95DBA029ABEDFA8C3210741
3
9E94CA4E1A759C92ED3282E598AF0AFE530AAEFCBA029ABEDFA8C3210741A47E
4
CA4E1A759C92ED3282E598AF0AFE530AAEFC015D9ABEDFA8C3210741A47E1059
5
1A759C92ED3282E598AF0AFE530AAEFC015D5F4DDFA8C3210741A47E105943F0
6
9C92ED3282E598AF0AFE530AAEFC015D5F4DD46FC3210741A47E105943F0D7D2
7
ED3282E598AF0AFE530AAEFC015D5F4DD46F90E10741A47E105943F0D7D2C084
8
82E598AF0AFE530AAEFC015D5F4DD46F90E1A083A47E105943F0D7D2C084A3FF
9
98AF0AFE530AAEFC015D5F4DD46F90E1A0833F52105943F0D7D2C084A3FF9977
10
0AFE530AAEFC015D5F4DD46F90E1A0833F522C8843F0D7D2C084A3FF9977CA78
11
530AAEFC015D5F4DD46F90E1A0833F522C88F821D7D2C084A3FF9977CA785AAE
12
AEFC015D5F4DD46F90E1A0833F522C88F821E96BC084A3FF9977CA785AAE875B
13
015D5F4DD46F90E1A0833F522C88F821E96B4260A3FF9977CA785AAE875B8AF7
14
5F4DD46F90E1A0833F522C88F821E96B4260FFD19977CA785AAE875B8AF7D530
15
D46F90E1A0833F522C88F821E96B4260FFD1BBCCCA785AAE875B8AF7D5307F1C
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IV. Error detection schemes
Fault detection is achieved in a circuit by including redundancy in one form
or the other. The selection of a suitable scheme depends on the type of errors to
be covered, error models and the performance degradation acceptable in terms
of hardware overhead and delay. The schemes based only on time redundancy are not
capable of handling permanent faults. The simplest form of error detection scheme
which can detect transient as well as permanent faults with hardware redundancy
is the DWC scheme. The hardware overhead in this case is little >100% with very
little additional delay, these can handle unrestricted error models.
The earliest error detecting scheme is the parity bit scheme. It is a well-known
fact that parity codes can detect all single bit errors and all errors with multiple
odd erroneous bits. To improve the error detection capability and detect also even
erroneous bits, multiple parity bits are required [7] and [8]. In HaF-256 first of all
DWC scheme will be used.
A. Faults models
In our considerations we use a fault model wherein either transient or permanent faults are induced randomly into the device. We consider single and multiple
faults. Faults are modelled as an 16-bit error vector E = {e15,...,ei,...,e1,e0}, where
eiÎ{0,1} and ei = 1 indicates that bit i is faulty. The number of ones in this vector
is equal to the number of inserted faults. Fault simulations were performed for two
kinds of fault models. In one model a fault flips the bit, and in the other model
(stuck-at-0/1) the bit takes a constant value 0 or 1.
Let X = {x15,...,x1,x0} be an error-free vector of bits.
Vector Xe = {xe15,...,xe1,xe0} is an erroneous vector [6] and [7]:
• xei = xi Å ei – if the fault flips the bit,
• xei = xi + ei – for stuck-at-1 fault,
• xei = xi × ēi – for stuck-at-0 fault,
where: Å – xor, + – or, × – and.
B. Error detection in basic operations
To protect basic operations such as multiplication mod (2n+1) (⊙), addition
modulo 2 (Å) and addition modulo 2n (⊞) in step function of HaF-256 (Fig. 3) we
used DWC (Duplication With Comparison) scheme as it shown in Fig. 5.
Step function Fj with DWC scheme elements for one of addition modulo
2n (⊞) operation is presented in Fig. 6. In this figure gray boxes are an extra elements to detect errors in A1⊞A6 operation. The outputs of the addition block and
the duplicated addition block are compared (box errcheck) and if are different,
an error signal is generated.
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Figure 5. Duplication With Comparison scheme
Figure 6. Step function Fj with DWC for one of addition modulo 2n (⊞) operation
Experiments were conducted by injection errors into the inputs of an operation block and observe if the errors are detected. Capability of single and multiple, transient and permanent fault detection using this scheme of fault detection
is presented in Sec. V.
C. Error detection in S-boxes
S-box is a substitution function and is used to obscure the relationship between the plaintext and the ciphertext. It is an important element of cryptographic
Chapter 5: Tactical Communications and Networks
125
algorithm and it should posses some properties, which make linear and differential
cryptanalysis as difficult as possible.
In each step of round function of HaF a substitution Sj(n) depending on
n and j is used. It consists of four S-boxes S0, S1, S2 and S3 each of dimension 16×16
working in such a way that:
• for n = 16, Sj(16) = S(j) mod 4
• for n = 32, Sj(32) = S(j) mod 4 || S(j+1) mod 4,
• for n = 64, Sj(64) = S(j) mod 4 || S(j+1) mod 4 || S(j+2) mod 4(x) || S(j+3) mod 4.
HaF S-box has been generated using the multiplicative inverse procedure
similar to AES (Advanced Encryption Standard) with a randomly chosen primitive
polynomial defining the Galois field. Nonlinearity of this S-box is 32510 and its
nonlinear degree is 15. Sixteen Boolean functions that constitute this S-box have
nonlinearities equal to 32510 or 32512 and are all of degree 15 [2].
A 16×16 S-box can be stored as a table containing 65536 values indexed by
an input of the S-box function, i.e., x1, x2, …, x16. The table stores S-box outputs
(16 bits: f1(x1, x2, …, x16), f2(x1, x2, …, x16), …, f16(x1, x2, …, x16)).
Concurrent Error Detection schemes for S-box are presented in [9] and [10].
There are three different approaches to error detection. Two of these methods
– parity based Concurrent Error Detection approach and DWC scheme – are
the methods with hardware redundancy [10], the third one is time redundancy
method and use involutional time redundancy CED to protect the S-boxes core
of function HaF [9].
Capabilities of detection single and multiple, transient and permanent fault
using these schemes are presented in Sec. V.
D. Error detection in step function
The round function (Fig. 3.) consists of 16 steps. The step #j is indicated by
the integer j Î {0, 1, …, 15} and is shown in Fig. 4. To protect this function we used
DWC (Duplication With Comparison) scheme as it shown in Fig. 5.
Round function with DWC scheme elements for step function Fj is presented
in Fig. 7. In this figure, gray box is an extra step function block. The outputs
of these two blocks are compared (box errcheck) and if are different, an error
signal is generated.
Experiments were conducted by injection errors inside or into inputs of the step
function block and observation if the errors are detected. Capability of single and
multiple, transient and permanent faults detection using this scheme of fault detection is presented in the Sec. V.
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Figure 7. Round function with CED elements for step function Fj protection
E. Error detection in round function
The message block Mi is processed in two rounds. The method of one block
processing is depicted in Fig. 2. Round function with DWC is presented in Fig. 8.
The gray box is an extra Round function block. The outputs of Round #1 and Round
#1b are compared (box errcheck) and if are different, an error signal is generated.
Capability of single and multiple, transient and permanent faults detection
using this DWC scheme is presented in Sec. V.
V. Simulation results
In order to measure the detection capability of the proposed in Sec. IV error
detection schemas we used VHDL (Very High Speed Integrated Circuits Hardware
Description Language) hardware description language and the VHDL simulator,
Active-HDL by Aldec. In this section we provide simulation results related to
the fault coverage of the proposed approaches. We present simulation results on
Chapter 5: Tactical Communications and Networks
127
the vulnerability of these schemes for fault models from Section IV.A. The faults
were injected into inputs of an basic operation blocks or also inside the other blocks,
and observed if the error is detected. We consider random faults, in the sense that
the faulty value is assumed to be random and uniformly distributed. The VHDL
model of the HaF-256 function has been modified by injected faults. The output
signals have been compared to correct signals. In this way, the obtained fault coverage gives a measure of the error detection capability.
Figure 8. DWC for round function
A. CED in basic operations
In this experiment we focused on transient and permanent, single and multiple
stuck-at faults and bit flips faults. Errors were injected in the input of an operation block
and observe if the error is detected. The obtained faults coverage for addition modulo
2n operation (⊞) is shown in Fig. 9 for permanent faults and in Fig. 10 for transient
faults. Single permanent stuck-at-0/1 faults are detected by proposed CED in 57.9%,
transient faults in 57.2%. All single bit flip faults, permanent and transient, are detected.
Fig. 9. and Fig. 10. show also dependence of error detection probability on
the number of injected faults. Not only single, but also multiple bit flip faults are
detected always.
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Military Communications and Information Technology...
Percentage of permanent stuck-0/1 error detection for other operations is presented in Table II. DWC scheme detects all bit flip errors.
Figure 9. Probability of permanent error detection using DWC for addition modulo 2n operation (⊞)
Figure 10. Probability of transient error detection using DWC for addition modulo 2n operation (⊞)
Chapter 5: Tactical Communications and Networks
129
Table II. Probability of permanent error detection for operations of step function of HaF-256
Stuck-at-0/1
Number of errors
1
2
3
4
5
a ⊙ b
57.2
74.9
88.1
93.1
96.6
vÅw
61.1
82.1
85.4
93.3
96.5
v⊞w
57.9
80.3
90.3
95.3
96.8
p1(x) Ä p2(x)
60.9
78.2
85.8
93.2
96.1
B. CED in S-boxes
In this paper Concurrent Error Detection schemes for S-boxes of the HaF
algotithm was tested for transient and permanent, single and multiple stuck-at error
and bit flips errors. The obtained detection percentage for differnet CED schemes
for transient faults is shown in Table III. and for permanent faults in Table IV.
The best detection percentage of errors is for DWC scheme, first for all for
single stuck-at-0/1 errors. A difference between DWC and parity bit scheme reaches
more than 25% however this difference is much smaller for multiple errors. There
is no difference between detection percentage for these three error detection schemas for bit flip errors — all are detected.
A study of concurrent error detection possibilities of these three CED schemas shows, that involutional time redundancy CED and also parity bit based scheme
is a good choice. The hardware overhead is very small and detection percentage for
bit flip errors is practically the same as for DWC.
Table III. Probability of transient error detection in S-box
Fault type
No. of errors
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
Parity bits
54.5
79.4
83.5
93.1
95.2
94.5
100
100
100
100
DWC
72.6
86.6
92.9
96.7
99.4
100
100
100
100
100
Involution
65.1
81.8
91.5
91.9
95.1
99.9
100
100
100
100
Table IV. Probability of permanent error detection in S-box
Fault type
No. of errors
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
Parity bits
56.3
82.3
85.5
94.9
97.0
100
100
100
100
100
DWC
75.8
87.7
95.1
98.3
99.7
100
100
100
100
100
Involution
69.2
85.9
95.7
95.9
98.9
100
100
100
100
100
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Military Communications and Information Technology...
CED in step function
In this experiment we focused on transient and permanent, single and
multiple stuck-at faults and bit flip faults. Errors were injected in the input
of step function block or inside the block and observed if the error is detected.
Detection percentage of stuck-at-0/1 and bit flip errors, is shown in Table V. for
transient errors and in Table VI. for permanent errors. Using this CED method
we can detect all bit flip faults. The worse case is for single stuck-at faults. Only
63.6% transient faults and 69.4% permanent faults is detected. Multiple stuck-at
faults are detected in 80-95%.
Table V. Probability of transient error detection in step function
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
63.6
79.6
84.3
90.3
95.6
100
100
100
100
100
Table VI. Probability of permanent error detection in step function
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
69.4
82.8
85.3
93.1
95.9
100
100
100
100
100
C. CED in round function
CED in round function was tested also for stuck-at faults and for bit flip faults.
There was considerate single and multiple faults, transient and permanent faults.
The faults was inserted inside the function block or into the inputs. Probability
of errors detection is presented in Table VII (transient error) and in Table VIII
(permanent error). The obtained detection percentage for bit flip faults, single and
multiple, is 100%.
Table VII. Probability of transient error detection in round function
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
66.5
82.6
85.3
92.1
96.5
100
100
100
100
100
Table VIII. Probability of Permanent error detection in round function
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
70.6
83.9
86.7
94.2
96.7
100
100
100
100
100
Chapter 5: Tactical Communications and Networks
131
VI. CED for hash function HaF
After analyzing the propagation of errors in HaF and the probability of error
detection for elementary function of HaF using different CED schemes, to protect
HaF circuit we used DWC scheme for step function protection and DWC for two
basic operations – addition modulo 2 (Å) and addition modulo 2n (⊞). Hardware
redundancy for step function DWC protection is smaller than redundancy for
round function protection, and probabilities of error detection are comparable.
We take into consideration transient and permanent, single and multiple
stuck-at faults and bit flip faults. Errors were injected into the input of HaF or inside
this function and than we observed if the errors are detected. Detection percentage
is presented in Table IX. and Table X. All, transient and permanent, bit flip faults
are detected. Multiple stuck-at faults are detected in 79-95%. Single stuck-at fault
are detected at approximately 60-65%.
Table IX. Probability of transient error detection in HaF
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
58.6
78.7
84.1
89.4
94.1
100
100
100
100
100
Table X. Probability of permanent error detection in HaF
Fault type
No. of errors
DWC
Stuck-at-0/1
Bit flip fault
1
2
3
4
5
1
2
3
4
5
65.4
81.9
85.0
91.9
95.5
100
100
100
100
100
VII. Concluding remarks
Fault attacks are becoming a serious threat to hardware implementations
of cryptographic systems. Proper countermeasures must be adopted to foil them.
In this paper first the propagation of errors in HaF-256 is studied. We take
into consideration single, transient as well as permanent faults injected at different stages of hash value computation. It is found that even a single error injected
resulted in half the bits of hash value being in error and the errors are spread across
the computed hash value.
We proposed also error detection schemes for basic operations and elementary functions of hash function HaF and finally for HaF-256 circuit. In order to
measure the fault detection capability of these schemas we used VHDL models and
the VHDL simulator, Active-HDL by Aldec. We presented simulation results on
the vulnerability of these schemes for single and multiple faults and for transient
and permanent faults. These methods can provide high coverage for multiplebit errors, which are the most common fault attacks. The coverage depends heavily
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Military Communications and Information Technology...
also on the fault model. Simulation experiments conducted on a large number
of test cases show that our schemes have 100% fault coverage in the case of bit flip
errors. These solution can be useful for concurrent checking cryptographic chips,
especially designed for platforms with limited resources
Acknowledgment
This research was partially supported by the Polish Ministry of Education and
Science as a 2010-2013 research project.
References
[1] G. Bertoni, L. Breveglieri, I. Koren, P. Maistri, V. Piuri, “Error Analysis and
Detection Procedures for a Hardware Implementation of the Advanced Encryption
Standard”, IEEE Transactions on Computers, vol. 52, no. 4, pp. 492-505, 2003.
[2] T. Bilski, K. Bucholc, A. Grocholewska-Czuryło, J. Stokłosa, „Hash Function
Concept”, Report no. 596, Poznań University of Technology, Poznań, 2011.
[3] D. Boneh, R. DeMillo, R. Lipton, “On the Importance of Eliminating Errors
in Cryptographic Computations”, Journal of Cryptology, vol. 14, pp. 101-119, 2001.
[4] D. Boneh, R. DeMillo, R. Lipton, On the importance of checking cryptographic
protocols for faults. Proceedings of Eurocrypt, pp. 37-51. Springer-Verlag LNCS 1233,
1997.
[5] L. Breveglieri, I. Koren, P. Maistri, “An Operation-Centered Approach to Fault
Detection in Symmetric Cryptography Ciphers”, IEEE Transactions on Computers,
vol. 56, no. 5, 635-649, 2007.
[6] E. Idzikowska, K. Bucholc, “Concurrent Error Detection in S-boxes”, International
Journal of Computer Science & Applications, vol. 4, no. 1, pp. 27-32, 2007.
[7] E. Idzikowska, K. Bucholc, “Error detection schemes for CED in block ciphers”,
Proceedings of the 5th IEEE/IFIP International Conference on Embedded and
Ubiquitous Computing, pp. 22-27. Shanghai 2008.
[8] E. Idzikowska, “CED for S-boxes of symmetric block ciphers”, Pomiary, Automatyka,
Kontrola, vol. 56, no. 10, pp. 1179-1183 2010.
[9] E. Idzikowska, “CED for involutional functions of PP-1 cipher”, Proceedings of the 5th
International Conference on Future Information Technology. Busan 2010.
[10] E. Idzikowska, “Errors detection in S-boxes of hash function HaF-256”, Borzemski L.,
Grzech A., Świątek J., Wilimowska Z. (eds.), Information Systems Architecture and
Technology – Web Information Systems Engineering, Knowledge Discovery and Hybrid
Computing, Wroclaw University of Technology Press, Wrocław, 2011, 231-240.
[11] J. Stokłosa, T. Bilski, T. Pankowski, “Bezpieczeństwo danych w systemach informa­
tycznych”, PWN, Warszawa-Poznań 2001.
Chapter 6
Spectrum Management
and Software Defined Radio Techniques
A Realistic Roadmap for the Introduction
of Dynamic Spectrum Management in Military
Tactical Radio Communication
Bart Scheers1, Austin Mahoney2, Hans Åkermark2
1
CISS Department, Royal Military Academy (RMA), Brussels, Belgium,
[email protected]
2
Communications Development, Security and Defense Solutions, Saab AB, Linköping, Sweden,
{austin.mahoney, hans.akermark}@saabgroup.com
Abstract: Cognitive radio (CR) technology has to date not been adopted by the military even though
more than a decade has passed since its inception. This paper describes how cognitive radio and
associated dynamic spectrum management (DSM) procedures can be introduced in military tactical radio communication. CR and DSM address key challenges that face future military tactical
radio communication and their successful introduction can reduce or even overcome current
spectrum scarcity and deployment difficulty. A DSM roadmap is introduced where military users
develop trust in, and experience with, these novel technologies in manageable steps. The first step
in this DSM roadmap involves the introduction of a military band dedicated for CRs and subsequent steps gradually increase the spectrum that may be utilized for military cognitive operation.
A high-level vision of how existing military spectrum management procedures will change in the future with the introduction of DSM is also presented resulting in a significant reduction in the workload
of spectrum management personnel.
Keywords: cognitive radio; dynamic spectrum management; military tactical communication;
roadmap
I. Introduction
Military tactical networks are being required to support a greater number
of services than ever before. In addition, the bandwidth requirements associated with many of the new services are also rapidly increasing. The combination
of these two factors means that we are nearing a time when there will be insufficient
bandwidth to support the services required for future military operations. Today’s
military operations are also typically undertaken by multiple nations cooperating
in a coalition force. The spectrum and frequency planning activities associated with
This research work was carried out in the frame of the CORASMA – EDA Project B-0781-IAP4-GC.
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Military Communications and Information Technology...
the deployment of a large multi-national coalition force are extremely complex and
unacceptably long, and can even delay the start of an operation [1].
Both of the aforementioned problems, spectrum scarcity and deployment burden, are to a large degree consequences of the centralized and static nature of current
spectrum management [1]. Dynamic spectrum management and cognitive radios
seek out and use a part of the electromagnetic spectrum in ways that are not predictable, so that it is not generally known which set of frequencies that a radio will use at
any given time”. The DSM process may be seen as a harmonization of, and dynamic
interaction between, both a human element in the form of spectrum regulators and
spectrum planners or managers and an autonomous element in the form of one or
more cognitive radio networks. DSM represents a fundamental change from existing
spectrum management procedures in the way that spectrum is allocated and used for
both civilian and military domains.
This paper aims to address two particular aspects of DSM, first:
• how a new and unproven technology such as DSM can be introduced
in military tactical radio communication, and second
• how current spectrum management procedures would be affected by such
a change.
To address these two aspects, this paper starts in section 2 with an overview
of current spectrum management procedures in the military tactical domain and
their inadequacy for future military operations. Section 3 highlights the key challenges for a proposed roadmap describing the gradual and systematic extension
of current spectrum management procedures that is presented in section 4. Section 5 relates the proposed roadmap to future spectrum management procedures
to clarify possible DSM practices in a future setting where cognitive enabled radios
will be the norm in military tactical radio communication. This paper ends in section 6 with major conclusions.
II. Current NATO spectrum management procedures
at the operational level
The worldwide use of the electromagnetic spectrum is regulated by the International Telecommunication Union (ITU). The ITU sets out the global radio
regulations in the form of a global Frequency Allocation Table (FAT) from which
each nation defines its own FAT covering many aspects of radio regulation, such
as the generic type of radio service that is permitted within each frequency band
and whether the band is allocated for military, civil or shared use. In what follows,
existing non-cognitive military spectrum management procedures are summarized
for coalition operations as defined in ACP 190(C) [2]. ACP 190(C) defines three
main phases to each operation: Planning, Deployment (also known as Implementation) and Recovery. The description provided is structured according to these
three phases involving the elements introduced in Figure 1.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
Combined Task
Force Commander
(CTFC)
137
Host Nation
Administration
Lead Nation
Combined Spectrum
Management Cell
(CSMC)
National Spectrum
Manager
National Spectrum
Manager
Component Spectrum
Manager (CSM)
National Spectrum
Manager
National
Element
National
Element
National
Element
- Networks
- Radios
- Networks
- Radios
- Networks
- Radios
Figure 1. Elements of a coalition deployment and their relationships
A. Planning phase
The primary purpose of the planning phase is to produce the Battlespace
Spectrum Management Plan (BSMP). The BSMP is used to inform the coalition
force entities of issues relating to the management and planned usage of the spectrum during the operation by providing a mapping between all radio and network
systems and frequencies.
In the planning phase, the Combined Task Force Commander (CTFC) assumes overall command of the forthcoming operation as part of a mandate from
a higher authority specifying the conditions under which the coalition force will
operate. The CTFC establishes a Combined Spectrum Management Cell (CSMC)
responsible for coordinating the spectrum requirement of the force, acquiring
the necessary spectrum and assigning frequencies for the systems used by the forces
within the operational area. The CSMC will normally delegate the authority to manage frequency allotments to one or more Component Spectrum Managers (CSMs),
where maritime, land, air, logistics and special forces are typical components. Each
component may be composed of multiple National Elements (NEs).
When a coalition is formed, a single nation is given the responsibility to act
as the Lead Nation. The Lead Nation is responsible for providing and sustaining
frequencies for the force through the spectrum management process and providing
technical support to the CSMC. Each nation within the coalition force is obliged to
set up its own spectrum management cell with a National Spectrum Manager (NSM)
to coordinate spectrum processes for its national forces. Each NSM will identify
all radio equipment to be deployed by the NEs, including equipment parameters
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Military Communications and Information Technology...
and operational (geographical) areas, and provide this information to the CSMC.
The CSMC combines the spectrum requirements, operational areas and radio
equipment characteristics provided by the individual NSMs into the Electronic
Order of Battle (EOB). The EOB expresses the overall spectrum requirement for
the operation.
Depending on the tools available to the CSMC, it should be possible to model
the overall spectrum requirement for the operation that is detailed in the EOB (i.e.
frequency planning). This modeling activity makes use of the topographical data
associated with the operational area to identify where frequency re-use is possible.
It also takes into account any equipment frequency constraints, frequency hopping systems, airborne emitters, preferred frequency allotments and any protected
frequencies. The CSMC uses the output of the modeling activity and the EOB to
acquire the necessary spectrum to satisfy the operation requirement. Depending
on whether or not the operation is conducted with the support of the host nation,
the CSMC either establishes contact with the host nation spectrum administrators and submits a frequency request detailing the spectrum requirements or uses
electronic surveillance to select the most favorable spectrum without regard for
existing local users.
When the available spectrum has either been allocated by the host nation or
identified through observation, the CSMC (and CSMs) will produce the frequency
assignment tables for all radio equipment associated with the coalition force and
incorporate them into the BSMP. These tables include any constraints on the use
the frequencies including transmit power, antenna height and location.
B. Deployment phase
In the deployment phase, each NE implements the BSMP as received from
the CSMC via their CSM prior to deployment. Interference may be encountered at
any time due to host nation emitters, conflicting allied systems or enemy jamming.
Each NE is responsible for investigating the interference that is encountered to try to
determine the source and, if the source is local, endeavor to reduce the interference
or eliminate it using appropriate action. If local action is impractical or unsuccessful, the interference and resultant loss of capability is reported to the CSM using
a specified interference report format and procedure. Each CSM is responsible for
the real-time control and management of the spectrum within its area of operation
(i.e. the spectrum used by its NEs). The CSM responsibilities include the resolution
of any frequency conflicts between its NEs and other interference issues by making
appropriate frequency assignment changes or modifying allotments if other efforts
to alleviate the interference are ineffective. The CSM will send any spectrum changes
to the appropriate NEs where they are implemented.
Whilst the CSMC may delegate responsibility for real-time spectrum management to the CSMs, it retains overall control and will become involved where
Chapter 6: Spectrum Management and Software Defined Radio Techniques
139
coordination between components is required. The CSMC’s responsibilities include
maintaining a close relationship with the host nation so that all interference reports associated with host nation emitters that are received from CSMs are sent to
the host nation administration to be resolved. The CSMC will also act to resolve
spectrum conflicts between components and other interference issues by making
appropriate frequency assignment changes or modifying allotments if other efforts
to alleviate the interference are ineffective.
C. Recovery phase
The recovery phase is a period of transition where each individual NE is responsible for informing the CSM of the time and date when the element will stop
using their frequency assignments and handing back frequencies to the CSM for
reassignment to another unit. Each CSM is responsible for reviewing and consolidating the spectrum in use by the force component, identifying any NE changes
that need to be incorporated into a new BSMP. The CSMC is responsible for reviewing and consolidating the spectrum in use by the force as a whole, incorporate
any changes into a new BSMP to meet the requirements of the new force which
is passed on to an incoming force or the (newly established) civil administration.
III. Key challenges for the introduction of DSM in military
tactical environments
The previous section shows that today’s military spectrum management procedures are, in the main, centralized and static. For example, the frequency bands
allocated for military use within the relevant FAT are typically not modified on
a regular basis. The frequency planning procedures that are applied within these
military bands are also performed to meet the particular operational requirements
of an operation including factors such as size, scope and composition of the deployed
force. To a large extent, frequency management activities are typically performed
prior to the operation in the planning phase and are often time consuming and
complex especially for large coalition operations. Hence, once the assignments are
made, they are generally fixed for the duration of the operation. Cognitive radio
and DSM technologies offer the potential to significantly enhance the operational
effectiveness and management of military tactical radio communications.
However, these new technologies are relatively complex and represent a paradigm shift in capability and operational procedure for military spectrum managers
and end-users. Several hurdles must be overcome if cognitive radio technology is to
be adopted by the military as the technology is currently not in use even though more
than a decade has passed since its inception. Firstly, as with any novel technology,
there is general mistrust in its capability and military users tend to be conservative
and risk averse towards new and unproven technology. Secondly, if the cognitive
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Military Communications and Information Technology...
radio concept is adopted, it will be introduced into service in a gradual fashion
where CRs must be able to safely coexist with existing legacy radio equipment and
existing procedures for the foreseeable future. These issues call for a carefully managed and incremental introduction of CR technology over time.
Summarizing, the extension of current spectrum management procedures
requires that:
• DSM is introduced in a step-wise fashion gradually increasing military users’ experience with and understanding of CR technology and equipment,
and where
• DSM procedures co-exist with legacy equipment and current spectrum
management procedures taking on a gradually increasing role as more CR
technology is coming into service.
Following these two principles, the introduction of CR and DSM in military tactical radio communication should be regarded as a evolution and not
a revolution enabling a more flexible and efficient use of the spectrum in future
military operations, and to ease the burden of the pre-mission frequency planning procedures.
IV. A relastic roadmap for the introduction of dynamic
spectrum management
This section proposes a realistic DSM roadmap that identifies how cognitive radio technology may be introduced into the military communications
domain in discrete steps although no specific timeframe is proposed. This DSM
roadmap starts from the present day, with no cognitive radios, and with each increment takes a step further into the future with an increasing exposure to DSM
in terms of number of devices, spectrum access complexity and freedom in operating
spectrum. The DSM roadmap is designed such that military users may develop trust
in and experience with the technology in an incremental fashion, and limit risk to
existing operational capabilities.
A. The first step – a dedicated band for CR systems
As mentioned previously, it is imperative that this first step involves minimal
risk to existing legacy operations and allows military end-users the opportunity to
build trust in and evaluate CR technology. We propose the introduction of a band
exclusively dedicated to CRs within the military allocation. All cognitive devices
would operate within this band and all existing legacy systems would operate outside
this dedicated band that is governed by current spectrum management procedures.
Initially, we would expect only a limited number of CRs within a coalition force and
the bandwidth requirement for this CR-only band would be small in comparison
to that required to support the legacy systems of the coalition.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
Civilian
Bands
141
Military
Bands
Today's spectrum
management procedures
Step 1: A dedicated band
for CR cystems
Step 2: Opportunistic military
use of limited civilian bands
Step 3: Coexistence of CR
with military legacy systems
in military bands
Step 4: Flexible use of military
bands and wide scale opportunistic
use of civilian bands
Time
Military bands
(legacy radio only)
Dedicated CRonly band
Civilian bands
(no military users)
CR as secondary users
(civilian bands)
CR as secondary users
(military bands)
Mostly CR (military
bands, flexible use)
Figure 2. DSM roadmap representation
1) General considerations
We propose that the CR-only band would be managed under a managed
commons DSA model1 where all CR users would be considered equal and no
traditional spectrum management procedures, such as channel assignment, would
be required leading to a reduced pre-operation preparation time. Separate CRNs2
would be able to coexist within the same band by autonomously and cooperatively or non-cooperatively selecting different operating channels within the band
(i.e. channels with the least interference). In the managed commons model case,
all CR users/networks share spectrum using an agreed management protocol
that encapsulates technology agnostic spectrum access rules. This CR-only band
may be utilized by military users at all levels of the military hierarchy. However,
it may be best to initially target the technology at the lower priority echelons and
for particular applications (see below). If CRs demonstrate success at this lower
level, they are more likely to gain acceptance and be more widely adopted across
all layers of the military hierarchy.
1
A DSA model is here used as classification of a high-level Dynamic Spectrum Access (DSA) technique following the terms introduced in [3]-[5].
2
A CRN is a network where a number of cognitive radios interoperate.
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Military Communications and Information Technology...
The choice of a dedicated band for CR systems as a first step in the road-map
has some important advantages.
a) No risk for interference with legacy systems
Military end-users are generally conservative and risk averse. The use of a cognitive system in a shared-use spectrum access mode that could degrade a legacy
communication system would not be acceptable. The use of a dedicated band for
cognitive systems eliminates this risk.
b) Relaxation of system requirements
From a technical point of view, a dedicated band based on the managed commons model will relax system requirements for CR systems. The primary reason for
this statement is that the cognitive band will lack primary users, placing less severe
constraints on CR sensing and decision making functions to gather and process
spectrum information to adapt its behavior. Cognitive systems would thus only
need to address interference from other cognitive systems and jammers, which
can be expected to be less time-critical compared to more challenging channel
evacuation requirements.
c) Familiarity
The concept of a license free band such as the current 2.4 GHz ISM band,
is not new for most military end-users and the introduction of a license free military
band for CR is likely to be regarded not as a revolution, but as a case where military
procedures catch up to the less restrictive procedures used in the civilian world.
d) Compatibility with current NATO spectrum management procedures
In NATO, the civil/military spectrum Capability Panel 3 (CAP3) is responsible
for the harmonization of radio frequency use among NATO allies. CAP3 is placed
under the new C3B sub-structure (Consultation, Command and Control Board).
The military part of the CAP3 panel manages the harmonized military spectrum
by allocating bands to applications or services, such as wide-band land systems or
satellite broadcast services. As such, the proposed first step would be compatible
with established administrative spectrum management procedures where CAP3
would allocate one or multiple bands exclusively for CR systems. However, NATO
currently considers CR as a technology rather than as a service or application.
CAP3 has therefore refrained from making the proposed allocation necessitating
further negotiations and lobbying to overcome this hurdle.
e) Incentives for research and development
One of the main obstacles in the technological development of military CR
is the deadlock created by the general lack of confidence in the technology. As long
as there is no clear sign from the military end-user showing an interest in the tech-
Chapter 6: Spectrum Management and Software Defined Radio Techniques
143
nology and thus establishing a potential market, the military communications
industry will be hesitant to invest in the development of military CR systems. On
the other hand, as long as there are no military off-the-shelf CR products, which
can prove the concept and clearly demonstrate key CR benefits, the military enduser will remain skeptical towards the technology. The creation of a dedicated
band for CR systems can end this deadlock and be an incentive for the industry to
start investing and developing products, comparable to what happened in the civil
2.4 GHz ISM band.
f) Extensibility
As mentioned previously, an important motivation for this first step is gaining trust. Once the concept of CR proves to work in this dedicated band, the band
can be easily extended and integrate more complex spectrum access models with
civilian and/or military primary users.
In summary, in light of the advantages described above, the introduction and
use of a dedicated band for CR is by far the most appropriate first step in the roadmap towards the introduction of cognitive radio in the military. If this first step
proves to be successful, the following steps can be implemented to overcome
spectrum scarcity and alleviate current deployment burdens.
2) Spectrum suitability, rules and limitations
Spectrum is a scarce resource, both civilian and military, and the introduction of a CR-only band will require sacrifices. In our opinion, the most
suitable option is to define multiple smaller bands within different spectrum
regions and use them as dedicated CR-bands. It is obvious that the NATOharmonized bands are the most appropriate. From a technical point of view,
and taking into account the possible applications, we think that at least a band
of 5 to 10 MHz should be defined in the NATO harmonized UHF band I
(225-400 MHz). Other possible candidate frequency bands are the military UHF band II
(790-960 MHz), the military UHF band III (1350-2690 MHz) and the SHF band
(4400-5000 MHz), where cognitive systems for short range wireless networks
could be envisaged.
An in-depth study of the rules and limitations for the use of this dedicated
band for CR systems is out of the scope of the paper. We will therefore only comment on three aspects of such rules and limitations:
• The types of applications that are allowed and are forbidden to use this
band together should be defined. Some example of systems that could be
allowed are wideband land systems, line-of-sight microwave links and tactical wireless local area networks whereas pulsed systems like surveillance
radars and broadcast stations are likely to be excluded.
• The technical elements of a set of restrictions need to be studied and
standardized in a future STANAG. Some possible restrictions are the use
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of a spectrum mask, a radiated power bound per application and the duty
cycle of the systems.
• The need for a pre-defined channelization in the dedicated band should
also be studied and if necessary the choice of the channel bandwidth.
B. The second step – opportunistic military use of limited civilian bands
A long-term driving factor is the increasing pressure felt by governments and
regulatory authorities to reallocate spectrum traditionally reserved for military
use to support civilian wireless services. In the event that spectrum allocated for
military operations diminishes on this basis, allowing the military to operate in civilian bands as non-interfering secondary users offers a way to increase the total
military spectrum pool.3
In this second step, we propose to extend the military CR footprint by allowing
military CR systems to operate as secondary users within limited civilian bands
(taking advantage of white spaces in time and space). The terrestrial television
and radio UHF/VHF bands are likely first candidates due to the ideal propagation
characteristics for tactical radio systems, the relatively stable channel availability
and the maturity of the commercial IEEE 802.22 standard which may be leveraged
for military implementations.
Within these television and radio bands, the military CR systems would operate on a non-interfering secondary user basis under a spectrum overlay model.
Military CRNs may or may not cooperate with each other and/or with the primary
civilian users regarding spectrum access. As noted in step 1, the necessary avoidance
of primary user transmissions under the overlay model represents an additional complexity over and above operations under a managed commons model (as in step 1).
From a military perspective, a significant advantage of this step in the DSM roadmap
is that whilst the military users will have acquired more operating spectrum, no interference risk is introduced for existing military operations using legacy systems.4
Military CRs would be operating within both the dedicated CR-only band (as peers)
and within limited civilian bands (as secondary users). All legacy systems would
be operating in remaining military allocations as today. This isolation in frequency
maintains the CR and legacy system coexistence assurance provided in step 1. This
step also allows military users to evaluate the more complex overlay spectrum access
technique with no additional risk to military legacy operations.
3
Here it is assumed that the military agree to be subservient to civil users (i.e. act as secondary users). This
may be the case in training or humanitarian relief operations. In more aggressive operations (such as Defense
of National Territory), military forces are unlikely to operate under this premise. They will more likely secure
spectrum on an exclusive-use basis either through negotiation with the host nation or by force.
4
There may be an interference risk to civilian primary users. Of course, the level of acceptability of risk depends on the importance of civilian services being supported in the specific band and the context of the military operation. The risk may be deemed acceptable for TV and broadcast radio bands but not so for civilian air traffic control bands.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
145
C. The third step – coexistence of CR with military legacy systems
in military bands
In this third step we propose to build on the experience developed in the second
step where military CRs were allowed to operate as secondary users within limited
civilian bands. We propose that military CR systems would also be allowed to coexist with legacy systems in limited military bands. The legacy systems would be
designated as primary users. The CRs would operate on a non-interfering secondary
user basis. This will allow military CR systems to take advantage of white spaces
in time and space left fallow by military legacy systems.
The proposed third step differs from the previous two steps in the proposed
DSM roadmap in that CRs would be allowed to coexist in the same spectrum
as legacy systems and thus represents the first step where there is an increase in risk
of interference by CRs to existing military operations. However, with the experience of the previous steps in the roadmap and only allowing a limited spectrum
overlap (between military legacy and CR systems), this risk may be easily managed.
D. The fourth step – flexible use of military bands and wide scale
opportunistic use of civil bands5
The previous steps have identified how CR and DSM technology may be
evaluated and implemented in an incremental process. A fundamental component
of these steps is the coexistence with existing civilian and military legacy systems
as described in steps 2 and 3, respectively. For this final step we assume that most
military legacy systems have been retired from service and that cognitive radios are
the norm (i.e. a far future setting).6 We also assume that military acquisition agencies and end-users have accepted, and are comfortable with, the new technology.
These assumptions enable the exciting possibility for a significantly more flexible
and effective use of spectrum within the military domain.
In this final step we propose that military spectrum will be managed under
a highly flexible and dynamically variable combination of the uncontrolled commons, managed commons, dynamic exclusive-use and overlay DSA model types
controlled using DSM policies. We also propose the continual and extensive use
of civil spectrum bands on a secondary use basis. A CRN will be provided DSM
policies which identify where in spectrum it will be allowed to operate and the associated operating conditions, which it must adhere to. Such allocations may include
multiple bands each managed under a different DSA model type. The DSM strategy implemented by the CRN management system will autonomously determine
5
The first three steps may not be accompanied by the proposed fourth step as it is entirely possible that
the third step is sufficient to overcome challenges with spectrum scarcity and deployment difficulty.
6
Some portions of the spectrum may always need to be reserved for fixed frequency operations (e.g. GPS and
some safety critical applications) and for any remaining legacy radios. However, this may still be managed
under the new DSM paradigm with the exclusive-use DSA model type.
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the most favorable operating location within the allowed spectrum given the local
time-variable environmental conditions.
CRNs operating within bands designated under either of the commons types
or the overlay model type (as secondary users) will not require complex traditional
frequency planning and should realize more efficient and effective use of the spectrum. However, systems operating under these conditions will not benefit from
the preferential (and often sole) spectrum access rights enjoyed under existing
military processes, which may have a detrimental impact on existing levels of quality
of service (QoS) that need to be managed by planning efforts as well as more dynamic
cognitive monitoring and control loops to better reflect the needs of an ongoing
operation. These future spectrum management procedures are clarified in the following section.
V. Future dynamic spectrum management procedures
in the military tactical domain
The introduction of CR technology and DSM will involve the modification
of existing military spectrum management practices. It is anticipated that these
changes will simplify and shorten the spectrum planning activity required prior to
an operation. This section addresses these procedural changes using the same three
phases of operation as in the current procedure with an emphasis on the planning
phase.7
A. The DSM planning phase
In a CRN future, CRs and CRNs will not, in general, need to be assigned
specific operating frequencies. CRs will instead be allowed to dynamically and
autonomously select the best available operating spectrum within specific bands
according to certain rules. This means that future DSM planning processes will
change from current spectrum management procedures as described in the following starting with an overview followed by a detailed example.
1) Overview of DSM planning considerations
CRs will not, in general, be assigned specific operating frequencies which
is fundamental difference from current spectrum planning that result in the compilation of the BSMP that includes the (static) frequency assignments for all radio and
network equipment. CRs will instead be allowed to dynamically and autonomously
select the best available operating frequency channel within specific bands accord7
In the proposed roadmap, CR systems and legacy radio systems may coexist. It is proposed that legacy radio
systems will continue to be managed using the principles described in section 2 whereas future CR systems
managed as described in this section. The overall management procedure will therefore be a blend between
static and dynamic processes.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
147
ing to certain rules formalized by the DSM policies.8 This means that the contents
of the BSMP will fundamentally change in the future and include DSM polices
rather than today’s mapping between all radio and network systems to frequencies.
The EOB can also be expected to undergo changes making DSM policy generation,
refinement and distribution the key activities to be undertaken during planning
phase of future operations.
A future DSM hierarchy would in the planning phase define and refine the DSM
policies for the underlying CRNs in preparation for deployment. Each decision
making entity in the hierarchy is expected to use a computer-based DSM policy
generation and management tool for this purpose. The host nation administration
will define the highest level DSM policies. These mainly specify the frequency
allocations and conditions of their use, which may be used by the CR systems
of the coalition force in the forthcoming operation. These DSM policies may be
updated and refined by each lower layer in the SM hierarchy prior to being downloaded into the policy repositories in the individual CRN management. DSM policy
refinements made by the individual NESMs and CSMs are reported back up the SM
hierarchy to the CSMC. These feedback paths ensure that the CSMC is completely
aware of how the spectrum will be utilized during the mission. The CSMC would
here not be permitted to change the DSA model type of a band unless the band
has been allocated to it by the host nation on an exclusive-use basis. This is true
for all decision making entities in the SM hierarchy, which must respect the DSA
model decisions made by the hierarchical level immediately above it.
The CSMC will update the DSM policies provided to it by the host nation to
reflect the decisions made. Where host nation allocations are split into sub-bands,
the DSM policy associated with each sub-band inherits the access conditions
of the parent allocation, together with any additional or replacement conditions
specified by the CSMC. The relevant DSM policies are then distributed to the CSMs.
Note that the particular decisions made by the CSMC depend on the requirements
of the individual national elements supplied to it by the NSMs.
2) A DSM planning example
As an example to illustrate the DSM planning phase, we consider a military
operation involving two battle groups, BG A and BG B, from two different nations.
The host nation administration provides the military force with two frequency
bands, one in the VHF band from 47-50 MHz and one in the UHF I band from
318-328 MHz, both on an exclusive use basis. The administration also allows the military force to make use of the civilian terrestrial television band (470-830 MHz) on
a secondary use basis. Figure 3 illustrates the allocations defined by the spectrum
management hierarchy (line 1).
8
DSM policies are here used as a mechanism to guide, control and bound the autonomous behavior of CRs.
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Military Communications and Information Technology...
The CSMC decides how the host nation allocation should be used by the military force on a high level (line 2 in Figure 3). Whilst the focus here is for the BG A operation, the CSMC must also consider the spectrum needs for BG B, that is operating
in another region of the same area of responsibility.
VHF
47
MHz
UHF I
50
MHz
Military Use
318
MHz
UHF II
328
MHz
470
MHz
830
MHz
Terrestrial TV
Military Use
1
Host Nation
2
CSMC
Terrestrial TV
3
NESM A
Terrestrial TV
Coalition Waveform
Band Allocation Key
Dynamic
Exclusive Use
Company Waveforms
Civil Overlay
(Military as SU)
Uncontrolled
Commons
Military Overlay
(PU)
Military Overlay
(SU)
Figure 3. Spectrum plan as output from DSM planning processes at different levels in the spectrum
management hierarchy. Primary and secondary users are designated as PU and SU, respectively
The CSMC assigns primary status to NESM A for BG A communications
for the 48-49 MHz and 318-320.5 MHz bands. These preferential assignments are
intended for the higher priority waveforms associated with BG A. The CSMC also
assigns primary status to NESM B for BG B communications for the 49-50 MHz
and 320.5-323 MHz bands. We assume that a coalition waveform is used for communication between elements in the two battle groups does not support DSM
and is assigned exclusive-use access for the 47-48 MHz band. The CSMC assigns
the 323-328 MHz VHF band as uncontrolled commons spectrum to be shared by
all BG A and BG B cognitive systems (intended for squad or platoon level waveforms). The terrestrial television band is assigned to be used equally by all systems
in the military force on a secondary basis.
Line 3 in Figure 3 illustrates the planning decisions made by NESM A as the next
level below the CSMC in the SM hierarchy (the decisions made by NESM B are
not shown as they are of no further interest in this example). NESM A decides that
it has three different company waveforms that are high priority and are thus given
primary status in separate bands as shown. Underlying squad and platoon level
waveforms are not provided dedicated allocations – they are required to find their
own operating spectrum.
B. The DSM deployment phase
At the beginning of the deployment phase, all CRs should have downloaded
the appropriate DSM policies and be able to initiate communication activities
within their possible allocations, under the specific conditions defined in the planning process. During deployment all CRNs will regularly send performance and
Chapter 6: Spectrum Management and Software Defined Radio Techniques
149
spectrum status reports back up to the relevant NESM provided resources are allocated for this purpose. The NESMs will send aggregate status reports to the relevant
CSM, status reports that are further aggregated to the CSMC. These status reports
allow spectrum managers at all levels of the SM hierarchy to continuously evaluate spectrum usage and network performance within their allocations throughout
an operation. Spectrum managers at all levels will have the ability to update and
disseminate DSM policies at any time to ensure the coalition force communication
needs are continuously met.
Each CRN will be responsible for dynamically and autonomously selecting
the best available operating frequency within its specific allocations defined by
the active DSM policies and be transparent to the end-user. With this capability,
many interference issues may be handled directly by the CRNs themselves through
the execution of the selected DSM strategy and spectrum mobility, with no involvement of any spectrum management personnel. Each NESM, in conjunction with
a network manager, will be responsible for monitoring the status reports provided
by their CRN management systems. During deployment, an NESM may make act
to optimize the performance of the underlying CRNs by updating the active DSM
policies associated with any allocations provided to it on an exclusive-use basis
by the relevant CSM.
C. The DSM recovery phase
During the recovery phase, spectrum managers at all levels of the SM hierarchy
will be responsible for reporting to their immediate superior manager that their
units no longer require their spectrum allocations. The associated DSM polices
may be modified to better reflect the needs of the remainder of the coalition force.
The CSMC will be responsible for consolidating the master DSM policy database such that it may be used by an incoming / replacement force.
VI. Conclusions
Military DSM is and requires a fundamental shift in capability and spectrum
management procedures. This paper has attempted to clarify the need for an incremental approach for implementing DSM into the military domain. We have outlined
a DSM roadmap for this purpose. Through this roadmap we have described how
novel CR technology with non-cognitive legacy radio systems can co-exist over
the transitionary period towards an all CR future. This roadmap has also addressed
how military users may develop trust in, and experience with, this novel technology
in manageable steps. The first step in this DSM roadmap involves the introduction
of a military band dedicated for CRs. It has been proposed that this band is managed
under the managed commons models using multiple smaller bands within different
NATO-harmonized spectrum regions.
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We have also presented a high-level vision of how existing military spectrum
management procedures will change in the future with the introduction of DSM
using ACP 190(C) as a baseline reference. With the application of secondary use
and uncontrolled use of spectrum, we foresee a significant reduction in the workload of spectrum management personnel. We have focused on both the important
planning and deployment phases of military operations. The decisions made by
the spectrum management entities, both before and during an operation, will
principally revolve around dynamically creating, updating, activating, deactivating
and deleting DSM policies.
References
[1] XG Working Group, The XG Vision Request for Comments, v. 2.0, available through
http://www.ir.bbn.com/~ramanath/pdf/rfc_vision.pdf
[2] Combined Communications and Electronics Board, Guide to Spectrum Management
in Military Operations, ACP 190(C), september 2007, available through http://jcs.
dtic.mil/j6/cceb/acps/acp190/ACP190C.pdf
[3] M. Buddhikot, “Dynamic spectrum access: models, taxonomy and challenges”, IEEE
International Symposium on New Frontiers in Dynamic Spectrum Access Networks,
2007, pp. 649-663.
[4] Q. Zhao, A. Swami, “A survey of dynamic spectrum access: signal processing and
networking perspectives”, Proceedings from the IEEE International Conference on
Acoustics, Speech and Signal Processing, 2007, pp. 1349-1352.
[5] Q. Zhao, B.M. Sadler, “A survey of dynamic spectrum access: signal processing,
networking and regulatory policy”, IEEE Signal Processing Magazine, vol. 24, May 2007,
pp. 79-89.
Dynamic Spectrum Management
in Legacy Military Communication Systems
Marek Suchański1, Paweł Kaniewski1, Robert Matyszkiel1,
Piotr Gajewski2
1
Military Communication Institute, Zegrze, Poland,
{m.suchanski, p.kaniewski, r.matyszkiel}@wil.waw.pl
2
Military University of Technology, Warsaw, Poland
[email protected]
Abstract: Increasing demands are being placed on dynamic spectrum access as a result of overload
of the frequency spectrum. In this paper we present a concept of frequency broker as a crucial element
of coordinated spectrum management system for battlefield communication network.
Critical problem in such system is automation of the collision-free frequency planning and management. In this paper, the solution of this problem basing on the graph theory is described. The new
model of electromagnetic waves attenuation prediction is also presented.
Keywords: dynamic spectrum management, frequency broker, radio wave attenuation, frequency
assignment, frequency planning
I. Introduction
The rapid development of the technical systems that use wireless technologies
causes increasing of the spectrum shortage problem. The devices that are “dependent”
on the spectrum appear – apart from the classic uses such as e.g. radio broadcasting – also as more and more complex systems such as the cellular telephony, WLAN
802.11, 802.16 (WiMAX) wireless networks, the GPS systems as well as simple
radio-devices e.g. wireless telephones and devices for home RTV equipment control,
garage gates control etc. The saturation of such type of devices grows causing overload
of the frequency spectrum and thus increase of the level of interference. An illustration
of this phenomenon can be a forecast of use of the spectrum fragment intended for
broadband mobile systems that has been constructed by the USA national regulator
(FCC – Federal Communication Commission) that foresees occurrence of significant
spectrum deficiency for these systems already in 2014 year (see Figure 1) [1].
The work is supported by Polish Ministry of Science and Higher Education funds under the project
No. OR 00018712.
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The spectrum static management and methods that have been used so far,
lead to drastically low effectiveness of spectrum usage. It was proved in many
measurements that were made in various parts of the world [2]. It was found, on
the basis of such experiments, that within the frequency below 3 GHz, an average
level of the spectrum use does not exceed 10% [3].
Such observations and experiments have caused growing of common awareness of a need of the spectrum use rationalization, among others, by application
of more efficient management methods.
Figure 1. Forecast of increasing use of spectrum intended for broadband mobile systems
That is why at the end of XX century appeared a concept of a dynamic access
to the spectrum. The most advanced form of which is so called opportunistic access.
This concept will find its embodiment in future solutions of the cognitive radio.
A spectrum coordinated management system can be used to rationalize
the spectrum efficiency of the communication systems that are built on the basis
of currently operated radio equipment. In some cases to solve this problem, it is
possible to use the frequency broker that operates in a quasi-real time. Such a possibility exists in the communication systems that are based on radio stations susceptible to radio data remote control. In polish army, VHF radio stations of the family
of PR4G and HF type Harris RF-5800H have such possibilities.
This article describes a concept of the spectrum coordinated management
system in which the critical problem is to find an effective algorithm to provide
and to modify the collision-free frequency plans. The presented below algorithm
is based on the graph theory using a specific models to predict an electromagnetic
waves attenuation of the path between a transmitter and a receiver.
II. Frequency broker concept
The main element of the spectrum coordinated management system is the frequency broker. The tasks of the frequency broker are generation and distribution
Chapter 6: Spectrum Management and Software Defined Radio Techniques
153
of radio data for radio networks that are covered by such broker and that ensure
a collision-free operation.
To correctly generate the radio data that ensures a collision-free operation
of the radio networks, the frequency broker must obtain the data that defines a set
of accessible frequencies. Under Polish conditions the unit responsible for the frequency management in armed forces is the Military Office of Frequency Management – NARFA PL. To define the organizational structure of the radio communication system, the frequency broker should cooperate with automated command
systems from which it receives information about location and type of all radio
equipment that is grouped in the radio network. Additionally, to currently verify
the received frequency set, a close cooperation with the electronic warfare systems
is recommended. Such cooperation enables an assessment of the electromagnetic
environment real state in defined nodes of the radio communication system.
Figure 2 shows a concept of the frequency broker usage that takes into account a hierarchic command system from an operational level to a subunit level.
Figure 2. Concept of broker use in military forces
Starting from the tactical level, the frequency broker is connected with the command system section that is responsible for the communication (G6, J6). It exchanges
data to execute frequency assessment procedures. The radio data from the broker
is sent to a properly level of the radio networks that confirm their acceptance or
generate demand for spectrum resources in a reverse channel. The intermediate
level broker is connected with a higher or lower level broker to distribute data
about allocations of the spectrum resources to a lower level and data collection
about electromagnetic situation in the area of the lower level broker responsibility.
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Figure 3 shows a system diagram that takes into account structural connections
between the NAFRA PL and local brokers and a block diagram of the frequency
broker that illustrates its modular structure.
The basic broker element is a planning module that enables generation of radio
data on the basis of jamming measures and criteria defined earlier. The accepted
measures and criteria of the jamming result directly from the radio equipment type
that is used in the radio communication system. For correct assessment of a interfering signal and the level of a usable signal it is necessary to use reliable waves
attenuation models and hence in the further part of the article is presented a radio
waves attenuation model in an urban area (GUPL) complete with test results that
verify this model.
Figure 3. Broker architecture and its functional connections
The collision-free radio plans is generated by broker taking into account a reliable waves attenuation model and some earlier defined data such as: a set of accessible frequencies, a structure of radio communication system, jamming measures
and criteria, radio stations operating mode and parameters. To solve this problem
an optimization method is used. An example of the greedy algorithm used in our
broker is presented below.
III. Concept of frequency planning
The problem of frequency assignment, even in a simplest model is a NP-hard
problem (it is a generalized problem of a graph colouring) and that is why a working
out of a polynomial algorithm that finds an optimal solution is impossible at the present state of knowledge. The best known algorithms that bring optimal solutions
of the NP-hard problems work in an exponential time what means that in practice
they are suitably only to solve not too complex cases of very limited input data [5].
Chapter 6: Spectrum Management and Software Defined Radio Techniques
155
The data sizes in practical use exceed considerably the ones suitable for solution in an exponential time and therefore the worked out algorithms should find
a possibly best solution in the time assumed in advance what means a necessity
of selection of one of suboptimal methods used in NP-hard problem solutions. Hence
for the considered applications, the following types of algorithms can be helpful:
• approximation and heuristic algorithms (e.g. a greedy algorithm);
• branch & cut type algorithms that reduce the exponential complexity of indepth searching by suitably early elimination of ineffective searching paths;
• artificial intelligence algorithms: genetic algorithms, ant colony algorithms,
tabu search, simulated annealing.
It seems that the simple and small time-consuming method to solve frequency
assignment question is the greedy algorithm. The greedy algorithm executes always
operation that is the most advantageous operation at a given moment. Thus, it selects a locally optimal solution expecting that it leads to a globally optimal solution.
A separation matrix is an input element for the frequency assignment algorithm.
The separation matrix elements determine a smallest possible distance expressed
in the frequency field that ensures a collision-free operation of two radio networks.
For co-site locations (the distance between two correspondents of two different radio networks is within 1,5-10 m), the radio station producers define
separation as the percentages that determine the distance between the upper frequency of the lower band and the lower frequency of the upper band that ensures
the networks compatibility.
The formalized expression of such a state when two radio networks operating in the subbands Fmin1 – Fmax1 and Fmin2 – Fmax2 (Fmin2 > Fmax1) do not interfere
themselves has the form:
B * Fmin 2   Fmin 2  Fmax1 
Where “B” is a coefficient of a co-site separation and in case of the above
mentioned radio station of the PR4G family is 0,09.
To determine the separation coefficients for other networks that are not cosite networks, it is assumed that the usable signal “S” reduced by the protection
coefficient “O” must be greater than the interfering signal “Z”.
S  Z O
On the basis of literature and experimental tests the separation of 10 dB
has been assumed.
After correct construction of the separation matrix it is possible to approach
frequency assignment for the defined radio networks. The input data for such
an algorithm is:
• a set of accessible frequencies for use in the frequency assignment process;
• defined networks complete with the type of used radio stations (an operation
frequency range, a transmitter power, a receiver selectivity characteristics);
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• defined criteria of the co-site separation;
• operation modes for particular radio networks (a FH frequency hopping
mode, an operation mode at the DFF fixed frequency);
• a defined protective coefficient;
• a separation matrix;
Moreover, if a given radio network is operating using of the FH mode, the minimum and maximum number of frequency subbands should be defined for a given
radio network as well as hops in the defined frequency subband.
Figure 4. Algorithm frequency assignment used in frequency broker
Figure 4 presents a functional diagram of the frequencies assignment algorithm that uses the greedy algorithm. The defined structure of the radio communication system can be treated as a full graph in which the nodes are the vertexes
whereas the graph edges are determined on the basis of the values contained
in a separation matrix. The task of the algorithm of the frequency assignment
is to solve the travelling salesman problem, it means finding the Hamilton path
of minimum weights sum. The algorithm sums all edge weights in every node and
sorts the nodes depending on the obtained sum of the edge weights. In the first
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157
step, a frequency is assigned to the node of the highest sum of the edge weights that
is treated as the graph starting node. Searching of next “unvisited” graph vertexes
is done by selection of a lowest weight edge.
After all vertexes are visited the total sum of the edge weights of the visited
vertexes is saved.
In the described algorithm every node is the starting node from which searching the Hamilton path is started.
The result of the algorithm operation is a path of the edge weights smallest
sum of the visited vertexes.
In every case it is checked if the number of used frequencies does not exceed
the set of the accessible frequencies. In a special case, when the algorithm can not
determine any path that meets the above mentioned criterion, the assignment
of radio frequencies is not possible.
After the solution is selected that is the best solution (it requires the smallest
number of accessible frequencies to allocate them to defined radio networks), there
is carried out a selection of the radio frequencies that exist in the set of accessible
frequencies and that are not used and a trial to assign successive frequency subbands to co-site networks.
IV. Prediction of electromagnetic waves attenuation
To determine an usable signal level and an interfering signal level, waves
attenuation prediction models are used in the planning module. A detailed
analysis of such models leads to the conclusion that there is lack of suitable models that serve for prediction of radio waves attenuation in urban environments
in which more and more military operations are carried out for the frequencies
within 30-88 MHz and 225-400 MHz most often used by army and for the geometry of the systems that are characterized by low antennas hanging above ground
(1-3 m) (see Table I). [1]
Among the items specified in the Table I, the first three items (Okumura,
Hata, COTS 231) are the most popular empiric models in the engineering practice
that are used for prediction of waves attenuation in urban environments. Unfortunately, these models are useful only for highly hanged antennas because they are
constructed for designing of conventional cellular systems in which the base station
antennas are mounted at high altitudes. Other items presented in the Table 1 show
the analysis results for frequencies other than for above mentioned Okumura, Hata
and COST 231 models, even when they refer to parts of the military frequency
ranges also always regards the situation when one of antennas is mounted at high
altitude. A confirmation of such an observation are also results of the problem
thorough analysis included in [2]. Wishing to fill the existing gap, the authors
of this publication proposed an adaptation of the model given by Rappaport [3]
that was constructed for the frequencies of 900 and 1800 MHz. Thus, they made
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Military Communications and Information Technology...
a linear extrapolation of the model coefficients and carried out its initial experimental
verification. According to that model called the General Urban Path Loss (GUPL)
the attenuation of waves for the frequency range of 30-88 MHz that propagate
in an urban environment is expressed by the following formula:
L[dB ] 10lg(
 
d
) 10n lg( )  d  FAF (1)
4 d o
do
Where:

do – reference distance in a close zone (m) d o 
(when the antenna
2
largest dimension < λ, do @ 30 meters);
λ
– wave length (m);
d
– distance between transmitter and receiver (m);
β
– power component which indicates that received power decays with distance
at a rate of 10β dB/decade;
n
– path loss exponent;
α
– attenuation constant (dB/m);
FAF – floors attenuation coefficient (dB)
Table I
Frequency
(MHz)
Distance
(km)
HT
(m)
Y. Okumura
15-1920
1-100
30-1000
Author
HR
(m)
M. Hata
150-1500
≥1
30-200
1-10
COST 231
800-2000
0.02-5
4-50
1-3
H. Xia
900, 1900
0.001-2
3.2, 8.7, 13.4
1.6
V. Erceg
1956
0.01-0.5
3.3, 6.6
1.5
D. Har
900, 1900
0.06-2
3.2, 8.7, 13.4
1.6
1890
0.02-0.18
4
1.7
3350, 8450, 15750
0.02-0.5
4
2.7
A. Kanatas
H. Masui
Y. Oda
457-15450
T. Rao
200, 400, 450
N. Blaunstein
W. Young
≥ 20
0.5-10.5
≥ 20
3
7
2-3
0.108-16.3
138
2
902-928
150, 450, 800, 3700
In Table II are presented values of β, n and α coefficients for the following
scenarios:
Scenario 1: outdoor RF propagation in an urban canyon;
Scenario 2: indoor propagation (same building, same/multiple floor(s));
Scenario 3: indoor-to-indoor propagation (between two different buildings, same/
multiple floor(s));
Chapter 6: Spectrum Management and Software Defined Radio Techniques
159
Scenario 4: indoor-to-outdoor propagation;
Scenario 5: outdoor-in-indoor propagation.
Table II
Power component
β
Path loss
exponent
n
Attenuation
constant
α (dB/m)
1
2,2
1,8
0,06
2
2,63
1,5
0,65
3
5 (if number of penetrated floors = 0),
4 (if number of penetrated floors > 0)
2
0,65
4
3,6
4
0
5
3,6
4
0
Scenario
The analysis of the GUPL model carried out by us shown a necessity of making corrects of FAF values given in [2] – in particular for higher than 4 number
of floors. That is why experiments were carried out. The results of these experiments
are discussed in [4] and the obtained corrected values of the FAF coefficient are
presented in the Table III and Table IV.
Table III
Recommended FAF
Number of floors
30 MHz
49 MHz
87,5 MHz
1
1,9
3,44
1,76
2
5,85
7,67
5,5
3
9,1
15,7
10,26
4
15,46
18,75
14,15
5
21,25
25,15
19,8
Table IV
Recommended FAF
Number of floors
230 MHz
320 MHz
1
6,4
7,95
2
11,8
10,75
3
14,5
15
4
21,9
18,15
5
26,6
20,1
These experiments enabled at the same time verify positively the GUPL model
usability for signal attenuation prediction in urban areas – both inside buildings
and in street “canyons”.
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Military Communications and Information Technology...
V. Summary
The problems related to the frequency assignment were subject to wide analyses as regards use in the cellular telephony network planning. However, the results
of these analyses are not useful for the frequency assignment in military radio
networks what results from a different specificity of these systems.
In commercial systems, the aim of frequency assignment is to minimize
the number of used frequencies, what is motivated by the concession costs. In the battlefield radio networks it is desired to use maximum number of frequencies, what
is motivated by the system resistance to intentional interference.
In the presented article a way of solution of the problem of frequency assignment
to radio networks has been discussed using the greedy algorithm. The greedy algorithm
selects locally optimal solution assuming that such a strategy will lead to a globally
optimal solution. It should be noted that it is one of possible solutions of the travelling
salesman problem. Another way of optimal frequency assignment is use of the SAT
solvers. The SAT-solver is a program that for a given formula written in a form CNF
(Conjunction Normal Form) finds if there exists such a sentence variables substitution for which the formula is true. If such a substitution exists the program returns
it as a result. At the moment, in the Military Communication Institute is conducted
work on use of the SAT solvers in the frequency assignment.
References
[1] http://blogs.uco.edu/graduate/files/2012/02/chart_wireless_data_2.gif
[2] J. Andrusenko, R.J. Miller, J.A. Abrahamson, N.M. Merheb Emanuelli, R.S.
Pattay and R.M. Shuford, “VHF General Urban Path Loss Model for Short Range
Ground-to-Ground Communications” IEEE Transaction on Antennas and Propagation,
vol. 56, No. 10, 2008.
[3] T.S. Rappaport, Wireless Communication: Principle and Practice, Upper Saddle
River, NJ: Prentice Hall PTR, 2002 (second edition).
[4] P. Gajewski, M. Suchański, P. Kaniewski, R. Matyszkiel, „Prediction of VHF and
UHF Wave Attenuation In Urban Environment” 19th International Conference on
Microwave,Radar and Wireless Communications MIKON-2012, May 21-23.
[5] T.C. Cormen, C.E. Leiserson, R. Rivest, “Introduction to Algorithms”, Massachusets
Institute of Technology, Thirteenth priinting, 1994.
Spectrum Issues of NATO Narrowband Waveform
Jan Leduc1, Markus Antweiler1, Torleiv Maseng2
1
Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,
Wachtberg, Germany, [email protected]
2
Forsvarets Forskningsinstitutt FFI, Kjeller, Norway, [email protected]
Abstract: In order to fulfill most of the military operational requirements, two kinds of waveforms are needed, a wideband networking waveform, e.g. the Coalition Wideband Networking
waveform (COALWNW) with high data rates, enabling network centric warfare and a Narrow
Band Waveform (NBWF) covering long ranges and enabling the parallel transmission of data and
voice. As a complement to COALWNW, the NATO Line of Sight Communications Capability
Team is developing a new NBWF. Legacy versions of the NBWF exist as national waveforms only,
operating historically in the VHF tactical communications band ranging from 30 MHz to 88 MHz
occupying a channel bandwidth of 25 kHz. The new NBWF is expected to operate in the same
frequency range, employing the same channelization; furthermore, the NBWF should allow operation in the tactical UHF band as well. The current waveform proposal is based on Continuous
Phase Modulation (CPM), which is widely used in mobile communications, due to the constant
envelope property of the modulation scheme.
In this paper, we first introduce principles of the newly defined NBWF. Furthermore, we want to
point out some unapparent spectrum usage issues of the current proposal.
Keywords: Narrowband Waveform; Bandwidth; Continuous Phase Modulation; Spectral Efficiency
I. Introduction
In the NATO Technical Note 1246 [1], it was identified that two types of waveforms are needed to fulfill all operational requirements. The first kind of waveform
is a wideband networking waveform enabling high data rates for advanced network
enabled capabilities. This kind of waveform is addressed in the COALWNW project in order to achieve interoperability. The second kind of waveform, a narrow
band waveform, enabling long range transmission employing the VHF 25 kHz
channelization is currently defined at NATO within the NATO Line of Sight Communications Capability Team. The focus of this paper is on the NBWF. The NBWF
will be mainly used in Combat Net Radios (CNR) for interoperability on the lower command levels. Currently there is no secure and networking capable CNR
This research project was performed under contract with the Federal Office of the Bundeswehr for Information
Management and Information Technology, Germany.
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Military Communications and Information Technology...
waveform available as a NATO Standard, since legacy versions of the NBWF are
available as national waveforms only.
There exist several solutions for the NBWF at present, but the one described
in [2] is a CPM employing a scheme with joint iterative demodulation and decoding of convolutional codes. We appreciate, that the lead development was done
at the Communications Research Centre (CRC), Canada [3], [4].
The waveform is described by a variety of modes for a 25 kHz channelization.
These modes are mainly distinguished by the provided data rates. The three lower
rate modes, namely NR (R refers to robust), N1 and N2, offering 10 kbps, 20 kbps
and 31.5 kbps respectively, can be considered as long range modes, while the modes
N3 and N4, offering 64 kbps and 96 kbps respectively can be considered as high
throughput modes. It is worth to mention that this CPM-waveform uses a very interesting approach to realize higher data rates, which will be described subsequently.
There are two possibilities for increasing the data rate. The first one is to increase
the amount of information within one symbol duration by transmitting e.g. 2 Bit/
Symbol instead of 1 Bit/Symbol, which is the common way of increasing the data
rate. The CPM waveform follows another, the second possible approach. While keeping the amount of information constant during one symbol duration, the symbol
duration itself gets reduced, thus the symbol rate increases. When the symbol rate
increases, it is known that the bandwidth occupation of the system increases as well,
which is not desired, since the use of a 25 kHz channel is a mandatory requirement (Requirement 2.07.03, 2.07.04 and 2.07.08 in [5]). Thus, the maximum phase
change within a symbol, h the modulation index, needs to be reduced to compensate
the spectral growth. This technique is very ambitiously applied in the modes N3
and N4, because the modulation indices become very small.
In this paper, the modulation parameters of this particular waveform are
investigated to see, whether the chosen approach for generating higher data rates
is appropriate for CNR NBWF or not.
The paper is organized as follows: In Section II, we give some more insights
on the physical layer of the CPM NBWF as defined in [2]. In Section III, we describe the simulation environment. Simulations results are presented in Section IV.
In Section V, the results and possible consequences are summarized.
II. CPM NBWF
CPM is a digital phase modulation of the same family as the Continuous-Phase
Frequency-Shift Keying (CPFSK) used in many legacy VHF tactical and civilian waveforms. The most significant properties are, a constant envelope in the timedomain signal, a fundamental robustness to amplitude modulation distortion and
an efficient utilization of power in the transmit amplifiers since the power amplifier can work in saturation. A complete overview of CPM can be found in [7].
The complex baseband representation of the CPM waveform without filtering is
Chapter 6: Spectrum Management and Software Defined Radio Techniques
163
2 Es  j t , 
e
(1)
Ts
s t ,  
with Es denoting the symbol energy and Ts representing one symbol period. The parameter
denotes the bipolar represented information input values. The phase
th
of the n symbol of this CPM modulation can be computed by
n
 t ,   2 h  i q t  iTs  , (2)
i
with nTs  t  (n 1)Ts . The accumulated phase is defined by
2 h
K
nL

n
 i (3)
i
1
where K  is a given normalization constant (for the NBWF) and indicates
2
the kind of CPM modulation. In case of L = 1 a full response system is considered,
if L > 1 there is a memory of length L in the system and adjacent symbols overlap.
The phase shaping pulse is
q t 
t
 g    d  (4)
and g    represents the – REC (Rectangular) partial response phase shaping pulse.
As already mentioned the CPM-NBWF is described in modes, by employing different physical layer parameters, but for all modes there is no transmission
filter defined [2] and the phase shaping is done with a rectangular pulse. Thus, two
adjacent modulations symbols are connected by equally spaced samples. The list
of important waveform parameters are given below in Table I.

Table I. CPM-Waveform-Modes and specific parameters
CPM-Mode
User Data Rate
[kbps]
L
M
h
Code Rate
Symbol Rate
[ksps]
NR
10
2
2
1/2
1/3
30
N1
20
2
2
1/2
2/3
30
N2
31.5
2
2
1/4
3/4
42
N3
64
3
2
1/6
4/5
80
N4
96
3
2
1/11
3/4
128
In the first columns of Table I, the five different modes are indicated with
their respective user data rate in the second column. Furthermore, L is denoting the ratio of width of the phase shaping pulse and the symbol duration; thus
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Military Communications and Information Technology...
all specified modes use partial response modulation. The parameter M indicates
the number of signal states, which is binary for all the different modes; h is representing the modulation index (max. phase change in a symbol duration), which
the developers of the waveform are using to compensate the spectral growth due
to the increasing symbol rates (and data rate).
The Forward Error Correction (FEC) is realized with a convolutional mother
code with code rate R = 1/3 and constraint length 4. The mother code is given by
the octal generator polynomials G  13,15,178 and is defined to be in a non-recursive form. For all modes except NR, the mother code is punctured by the puncturing matrices given in [2]. Convolutional codes are vulnerable to burst errors, where
a contiguous sequence of bits or symbols gets corrupted. In order to break these burst
errors in single bit errors; an interleaving scheme is employed between FEC and
modulator. The interleavers have been designed following the “S-Random/Dithered
Relative Prime (DRP)” approach [6] which ensures a minimum distance between adjacent input bits. Since, a joint iterative demodulation and decoding
scheme is suggested by the inventors in the receiver, the interleaver has another important function besides enhancing the robustness against burst errors.
The interleaver guarantees that the sources of reliability information in the generation of extrinsic information on the receiver side are independent, namely
the independence between the inner code (the CPM modulator) and the outer
convolutional code.
A conclusive block diagram with the transmitter of the CPM-NBWF is given
in Fig. 1.
Figure 1. Block diagram of CPM-NBWF transmitter
III. Simulation environment
From a spectrum perspective, the modes given in Table 1 are following
the design constraint, that 99% of the power is within 25 kHz bandwidth. As shown
in [8], even this constraint is not completely fulfilled. The 99% power bandwidth
is given by:
• N1 = 25.89844 kHz
• N2 = 23.78906 kHz
• N3 = 27.10938 kHz
• N4 = 28.50000 kHz
Chapter 6: Spectrum Management and Software Defined Radio Techniques
165
However, another evident constraint tells, that all information of the waveform
is embedded within 25 kHz. We have to bear in mind that the increase of data rate
is achieved with an increase of the symbol rate and the symbol rate is somehow reciprocal to the bandwidth. Hence, we want to analyze, if the decrease of the modulation
index h is able to compensate the spectral growth due to the increase of the symbol
rate. Therefore, only the modulation scheme is under analysis, since the FEC shall
only be used to increase the robustness to interferences and not to compensate
shortcomings of the application of the modulation scheme. Furthermore, the FEC
does not contribute to the spectral shaping of this waveform. The robustness to
interferences is not considered in this paper.
A block diagram of the simulation is given in Fig. 2. First, uniformly distributed random bits are generated and then CPM modulated using the parameters
of Table I (excluding FEC). The system employs oversampling with factor 16. This
means that after the modulator each bit (M = 2) for all modes) is represented by
16 samples. The signal stream is then filtered by an equiripple Finite Impulse Response (FIR) low pass filter. The pass frequency f pass is variable and f stop 1.2 f pass ,
is indicated in Fig. 2. Furthermore, the pass band ripple is limited to 0.1 dB and
the stop band attenuation is given by Astop  60 dB .
Figure 2. Block diagram of simulation chain
The filtered signal is then demodulated by the respective CPM demodulator
and the Bit Error Rate (BER) is measured.
IV. Simulation results
The previous described setup permits to measure the BER dependent on the (by
f
and with
the FIR filter limited) one-sided bandwidth W 
1.1 f pass f pass 
2
this, a possibility to verify, whether all information is contained within the 12.5 kHz
one-sided (25 kHz two-sided) channel.
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Military Communications and Information Technology...
First, the generated results are presented in Fig. 3, where the BER is drawn
over the one-sided bandwidth W in kHz.
Figure 3. One-sided bandwidth W versus BER
In Fig. 3, it is easy to observe that the mode N1 needs clearly less bandwidth
than the maximum of 12.5 kHz. Also, the mode N2 can transmit all information
within a bandwidth smaller than the 12.5 kHz one-sided (25 kHz two-sided) channel.
One could assume that the symbol rate for those modes could be further increased
in order to maximize the data rate, but we have to bear the 99% power bandwidth
in mind. Thus, the long range modes are compliant to the spectral requirements.
(NR is disregarded here, since NR and N1 have the same modulation parameters
and only differ in the puncturing).
The behavior under filtering is very different for the high throughput modes N3
and N4, since they have a higher bandwidth demand to transmit all the containing
information. If the maximum bandwidth W=12.5 kHz (one-sided) is granted to
the system, the mode N3 has a BER of approximately 6% and the mode N4 of approximately 35%. Those are not tolerable values and to be able to transmit the bits
at a BER of 10-7, the mode N3 would need two channels, thus 50 kHz (two sided)
and the mode N4 three channels, thus 75 kHz (two sided), in order to be compliant to the current NATO channelization. This observation reduces the spectral
efficiency of the system dramatically, if we assume that NATO will not change
the 25 kHz channelization for this waveform. The results of the above section are
summarized in Table II.
All modes are advertised as occupying 25 kHz bandwidth; the respective
spectral efficiencies are indicated in Table II. Simulations results show (Fig. 3)
Chapter 6: Spectrum Management and Software Defined Radio Techniques
167
that the necessary bandwidth occupation for the modes N3 and N4 is higher;
the realistic, corrected spectral efficiency of the high throughput modes N3 and
N4 is approximately as high as the spectral efficiency of the long range mode N2,
as can be seen in Table II. Furthermore, the high throughput, low-h, modes are
more sensitive to noise, as can be seen in Table III.
Table II. CPM-Waveform with realistic spectral efficiences
CPM-Mode
Spectral Efficiency
a)
25
Necessary bandwidth
in NATO channels
Spectral Efficiency
real
NR
0.4
1 channels (25 kHz)
0.4
N1
0.8
1 channels (25 kHz)
0.8
N2
1.26
1 channels (25 kHz)
1.26
N3
2.65
2 channels (50 kHz)
1.28
N4
3.84
3 channels (75 kHz)
1.28
b)
a) is defined as the user throughput divided by 25 kHz band width
b) is defined as the user throughput divided by necessary band width
Table III. Nosie sensitivity of low-h CPM (Oversampling 16)
CPM-Scheme
(Modulation parameters)
Necessary Eb / N0
to reach a BER of 10-3
Necessary Eb / N0
to reach BER of 10-6
M = 2; L = 2; h = 1/2
(N1 without FEC)
7.5 dB
11.1 dB
M = 2; L = 2; h = 1/4
(N2 without FEC)
12.8 dB
16.5 dB
M = 2; L = 3; h = 1/6
(N3 without FEC)
17.7 dB
21.4 dB
M = 2; L = 3; h = 1/11
(N4 without FEC)
23 dB
26.4 dB
Thus, combining two or respectively three channels to provide the necessary bandwidth to N3 respectively N4 is disadvantageous. It would be the same
from a spectral occupation (see Table II) perspective and significantly better
from a robustness (see Table III) perspective, if e.g. the N2 waveform would be
employed with double or triple data rate and occupying the respective two or
three frequency channels.
However, we further want to verify, if the modulation index can be used to
compensate the spectral growth of the waveform, due to the increase of the symbol rate. To do so, the results are normalized to the symbol rate and in Fig. 4
B . Ts (Ts is the symbol duration; B = 2W) versus the BER is plotted.
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Military Communications and Information Technology...
Figure 4. B . Ts (Ts is the symbol duration; B = 2W) versus BER
It can be observed that all modes have a different normalized bandwidth
occupation. The higher the throughput, the lower the bandwidth needs. The valid
question at this point is, if the normalized bandwidth gains of N3 and N4 are caused
by the reduction of h, because the value of L (ratio phase pulse length / symbol time)
also has an impact on the bandwidth [7] and L = 3 for the modes N3 and N4 and
L = 2 for the modes N1 and N2. To verify this, further simulations are conducted,
with the modulation settings of N3 and N4 but employing a value of L = 2. These
results are plotted using dashed lines in Fig. 4.
As it can be seen, these “verification modes” perform normalized to the symbol rate very similar to the mode N2. Thus, if L = 2 is used for the low h modes
(N3 and N4), they are almost as bandwidth efficient as N2. Thus, the observed
bandwidth gains between the mode N2 and N3/N4 are due to the higher values
of L. Hence, there are only negligible bandwidth gains from reducing h beyond ¼.
V. Conclusions
In this paper the upcoming NATO NBWF has been analyzed, regarding
the necessary bandwidth occupation of the system. This particular waveform
is keeping the amount of information constant during one symbol duration and
Chapter 6: Spectrum Management and Software Defined Radio Techniques
169
to increase the data rate, the symbol duration itself gets reduced and the symbol
rate increases. Thus the bandwidth occupation of the system increases as well,
which is not desired, since the use of a 25 kHz channel (two-sided) is a mandatory
requirement [5]. This increase of bandwidth is compensated with the reduction
of the modulation in h of the CPM modulation scheme, which is the maximum
phase change within a symbol duration. Therefore, while making the symbols
duration shorter, the phase change within a symbol is also reduced. It is shown
that the spectral growth cannot be compensated by a reduction of h. The modes
N3 and N4 occupy (to function properly) two and respectively three 25 kHz
channels, which does not meet [5], especially because military spectrum is a very
scarce resource.
On the other hand the modes N1 and N2 are very robust and well-defined
waveforms fulfilling all requirements regarding spectral occupation [5]. Therefore,
we propose the following two steps:
1. Move on with N1 and N2 for a fast ratification and make these new waveforms available to the user
2. Revisit higher data rate modes in the future for a possible annex and ensure
usable modes fulfilling requirements of all nations.
There are certain possible solutions for overcoming the issue pointed out
in the paper within the second proposed step.
The other waveform candidates, which were submitted to the LOS Comms
CaT could be taken into consideration for the high throughput modes or modifications would had to be made to the current waveform,
If the CPM-waveform is kept also for the high throughput modes there are
also certain modification possibilities.
First, the modulation parameters of the mode N2 are kept, but the number
of bits per symbol M = 1 would have to be increased to M = 2 respectively M = 3,
which would double/triple the data rate (63 kbps resp. 94.5 kbps) on the expense
of the demodulator complexity [7].
Second, the symbol rate of N2 gets increased (doubled/tripled offering (63 kbps
resp. 94.5 kbps); occupying two resp. three 25 kHz channels, while being fairly
resistant to interference compared to the current modes N3 and N4.
References
[1] Technical Note 1246, ‘Wireless communication architecture (land tactical): scenarios,
requirements and operational view’, NC3A, The Hague, Netherlands, 2007.
[2] LOS Comms CaT (formaly known as: SC/6 AHWG/2), “Technical Standards for Narrow
Band Physical Layer of the NATO Network enabled Communications Waveform and
VHF Propagation Models (STANAG Draft 4),” 2010.
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[3] C. Brown and P. Vigneron, “Spectrally Efficient CPM Waveforms for Narrowband
Tactical Communications in Frequency Hopped Networks”, Military Communications
Conference, 2006. IEEE MILCOM 2006, 23-25 Oct. 2006.
[4] C. Brown and P. Vigneron, “Equalisation and Iterative Reception for Spectrally
Efficient CPM in Multipath Environments”, Military Communications Conference,
2010. IEEE MILCOM 2010, Oct. 31-Nov. 3 2010.
[5] NATO C3B, “Requirements for a Coalition Tactical Radio Waveform,” (AC/322-N
(2011)0010-REV1), January 2011.
[6] D. Divsalar, G. Montorsi, S. Benedetto and F. Pollara, “Serial concatenation
of interleaved codes : Design and performance analysis,” in IEEE Trans. Inform.
Theory (1998), April, pp. 409-429.
[7] J.G. Proakis, “Digital Communications,” Mcgraw-Hill Publ.Comp., Edition: 4., August
2000.
[8] J. Nieto, “NBWF Investigations”, LOS Comms Cat Workshop, Rome, Italy, March 2011.
Legacy Waveforms on Software Defined Radio:
Can Hierarchical Modulation Offer
an Added Value to SDR Operators?
Marc Adrat1, Tobias Osten1, Jan Leduc1, Markus Antweiler1,
Harald Elders-Boll2
1
Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,
Wachtberg, Germany, [email protected]
2
Cologne University of Applied Sciences, Cologne, Germany, [email protected]
Abstract: Military tactical communications is taking the next step in its evolution. Many nations
spend considerable efforts to bring the novel Software Defined Radio (SDR) technology into service.
SDRs allow military radio operators to change waveforms on-the-fly according to the mission needs.
New capabilities can be loaded as so-called Waveform Application (WFA). Before novel waveforms
with wideband networking capabilities will be available in the future, most nations have launched
projects to port legacy waveforms to SDRs. These WFAs on the modern SDRs shall ensure interoperable communication to legacy radios in situations where both types of radio equipment are deployed
at the same time in the same mission.
In this paper, we present first results of our analysis if an added value can be provided to the operators
at SDRs, e.g., in terms of a higher data throughput or robustness. As an example, we apply the concept
of hierarchical modulations to a legacy waveform. The modulation scheme of the legacy waveform
acts as the base-layer which ensures the over-the-air interoperability to legacy radios. Additional
information can be transmitted between SDRs only utilizing some extra enhancement-layers.
Keywords: Software Defined Radio, Waveform Application, Hierarchical Modulations, Bit Interleaved
Coded Modulation with Iterative Decoding
I. Introduction
It is expected that the modern Software Defined Radio (SDR) technology will
considerably extend the capabilities of future military tactical communications.
Among many other advantages of SDR technology, one key benefit is that waveforms can be loaded onto the SDRs as so-called Waveform Applications (WFA)
according to the mission needs. Another key benefit is that SDRs are expected to
be powerful enough to host modern WFAs with wideband networking capabilities.
This research project was performed under contract with the Federal Office of the Bundeswehr for Information Management and Information Technology, Germany.
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Military Communications and Information Technology...
Such Wideband Networking Waveforms (WNW) typically set the most demanding requirements for the design of high capable heterogeneous SDR platforms.
However, if WFAs with lower computational demands are deployed on SDRs, like
legacy waveforms, some of the computational resources remain unused. These
spare resources might probably be beneficially utilized by applying advanced signal
processing algorithms. An example will be discussed later on in this paper.
Moreover, it can be anticipated that SDR technology will be introduced to the
military forces in a step-by-step process. There will be a significant period of time
where legacy radios and modern SDRs hosting the corresponding legacy WFA will
be deployed at the same time in the same mission. Guaranteeing interoperability
between both types of radio equipment, legacy radios and modern SDR, is the
utmost requirement in the Porting process. Porting in this context means that the
waveform functionality which is known from the legacy radio is implemented
as a piece of software which runs on the SDR platform. This piece of software is called
Waveform Application. Usually, interoperability is guaranteed by reproducing the
known functionality of the legacy radios one-to-one in the WFA for SDRs.
However, as we have already proposed in [1], it might be of value to apply
modern advancements in digital signal process­ing at the receiving end of a communication link. While keeping interoperability on the air interface, the operator
at the receiving SDR can experience a benefit if compared to the operator at the
legacy radio thanks to the more sophisticated receiver signal processing. Such
benefit can be, e.g. a reduced bit error rate or an extended communication range.
In the example given in [1] the concept of Bit Interleaved Coded Modulation
with Iterative Decoding (BICM-ID) [2] [3] has been applied at the receiver using
some MIL-STD188-110B-like waveform modes [4]. It has been shown that some
gains are achievable, but unfortunately that these gains have to be considered
as negligibly small if the concept of BICM-ID is directly applied to the standardized configuration settings. It has also been shown that substantial gains can be
realized if a change of a single line of software code at the transmitter is tolerable.
Of course, this would violate the key requirement to guarantee interoperability to
legacy radios.
In this paper, we analyze if the concept of Hierarchical Modulation can beneficially be applied. Again we use some MIL-STD188-110B-like waveform modes
as example. Our objective is to provide some extended services to the operators
at the SDRs. Using the known configuration settings from the legacy waveform
as a base-layer in the hierarchical modulation scheme, allows ensuring interoperability between legacy radios and SDRs. Thanks to some enhancement-layers in the
hierarchical modulation scheme additional data becomes transferable within an
SDR-to-SDR communication link.
Important note: In this paper, we focus on presenting the basic idea, discussing some useful design guidelines as well as showing first simulation results. These
simulation results focus on the analysis if an enhanced data throughput can be
Chapter 6: Spectrum Management and Software Defined Radio Techniques
173
provided as added value to the operator at an SDR. In a second complementing
paper, see [8], we discuss if a higher robust­ness (and with this a higher range) can
be realized.
This paper is structured as follows. In Section II we explain the idea of hierarchical modulation in the context of legacy waveforms on SDRs in more detail.
In Section III we describe the simulation environment. Simulations results are
presented in Section IV. After having given a short outlook on the results in [8] and
having described some future steps of our analysis in Section V, we finally conclude
our findings in Section VI.
II. Hierarchical modulations in the context of legacy
waveforms on SDRs
Hierarchical modulation allows to multiplex and to modulate multiple streams
of user data into a single stream of modulation symbols. It is sometimes also referred to as Layered Modulation because one of the input data streams determines
the base-layer symbols and the other input data streams determine the extra
enhancement-layer symbols.
One prominent practical implementation of hierarchical modulation can
be found in the area of digital video broadcasting where the base-layer carries
the information of a robust, but typically low-resolution video stream. The extra
enhancement-layers, which might only be decodable by receivers under very good
channel conditions, allow to increase the resolution and therewith the quality
of the video.
Thus, our basic idea is to apply a similar technique in the porting process
of legacy waveforms to modern software defined radios. In the ported counterpart
of the legacy waveform, the information of the legacy waveform represents the
base-layer guaranteeing interoperability. On top of that the operators at SDRs can
transmit additional data using the extra enhancement-layers.
A. Example base-layer and enhancement-layers
To simplify matters, in the following we restrict our considerations to a comprehensible example using an 8-PSK signal constellation as base-layer. Such a digital
modulation scheme is widely used in a couple of present military tactical communication schemes like in NATO STANAG 4285 [5] or MIL-STD188-110B
Appendix C [4]. The extension of our considerations to other digital modulation
schemes is straight­forward.
The left part of Figure 1 shows an 8-PSK signal constellation set with a Gray
labeling for the individual symbols. This set shall serve as the base-layer for our
hierarchical modulation schemes. Note, in this simple example the base-layer carries information solely in its phase.
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Figure 1. Different Signal Constellation Sets with exemplary symbol labels; left: 8-PSK base-layer,
center: 8-PSK base-layer plus 4-ASK enhancement layer (star);
right: 8-PSK base-layer plus 4-QAM enhancement layer (diamonds)
In order to provide an add-on to the operators at SDRs we are aiming for
transmitting some additional data in so-called enhancement-layers. Two examples
are shown in the center and in the right part of Figure 1.
In the first example, we apply a 4-ASK enhancement-layer as an overlay to
the 8-PSK base-layer. This results in an overall 32-QAM scheme which would
allow the operator at the SDR to transmit two additional bits. Simply speaking, the base-layer selects one of the eight sectors in the I/Q-plane (inphase/
quadrature) while the enhancement-layer selects one out of four magnitudes
in each sector. Later on, this scheme will also be referred to as “star-shaped”
32-QAM scheme.
In the second example we again apply an enhancement-layer with 4 signal
constellation points to the 8-PSK base-layer. However, inspired by [6] the slightly
different placing of signal constellation points allows exploiting the I/Q-plane more
effectively. In the rest of this paper, this scheme will be called “diamond-shaped”
32-QAM scheme.
Two categories of questions arise immediately:
• How to design the enhancement-layer in detail? How to place the signal
constellation points? How to label them?
• What are the beneficial/adverse consequences with respect to the performance, e.g. in terms of bit error rate behavior? What are the consequences
for the operators at the legacy radios resp. software defined radios?
Answers to the design aspects (first category) will be given in the next subsection. The consequences (second category) will be analyzed in Sections III and IV.
B. Designing the enhancement-layer
When designing the enhancement-layer we have to consider a couple of design
rules. Among others, these are:
Chapter 6: Spectrum Management and Software Defined Radio Techniques
175
1. The mean energy ES required per modulation symbol shall be the same for the
original 8-PSK scheme and the extended star- resp. diamond-shaped 32-QAM.
2. The Euclidean distance between any pair of signal constellation points shall
be such that not a single pair exists which reveals a dedicated bottleneck
with respect to the bit error rate performance.
3. The symbol labels shall be optimized in view of a receiver using the
concept of Bit-Interleaved Coded Modulation with Iterative Decoding
(BICM-ID) [2][3].
The first two design rules are related to the placing of signal constellation
points in the I/Q-plane while the third design rule is dedicated to the labeling.
Both aspects will be optimized separately from each other.
1)
Placing of signal constellation points for the star-shaped 32-QAM scheme: Figure 2 shows an extract of the star-shaped 32-QAM scheme.
Figure 2. Design of star-shaped 32-QAM scheme (extract)
Let us assume that the four signal constellation points representing the 4-ASK
enhancement-layer are equidistantly separated by the distance α. The 4-ASK scheme
shall be centered around β. In order to make sure that the mean energy Es of the
resulting star-shaped 32-QAM scheme is the same as for the original 8-PSK scheme
(see first design rule), it can be shown that α and β have to satisfy the condition


ES  5 4 2 . (1)
If the spacing between the four signal constellation points representing the
4-ASK enhancement-layer tends towards zero, i.e.   0, the term   ES .
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Thus, the star-shaped 32-QAM scheme degenerates to the original 8-PSK scheme
(this fact can be considered as a consistency check). If we increase α, the term β starts
to decrease. While doing so, we have to take into consideration that the Euclidean
distance between the inner signal constellation points on radius r1* becomes smaller
the larger α resp. the smaller β is. In order to satisfy the second design rule, we enforce
that the Euclidean distance between the inner signal constellation points on radius r1*
is also α. It can be shown that in this case α and β have to satisfy the condition
1 3  sin 22.5


 . (2)
2  sin 22.5
Solving Eqs. (1) and (2) for ES = 1 yields α = 0.331 and β = 0.928999. From
that we can easily determine the radii of the star-shaped 32-QAM scheme shown
in the center of Figure 1
r1∗ = 0.432499
r2∗ = 0.763499
r = 1.094499
∗
3
2)
 with * 0.331. (3)
r4∗ = 1.425499
Placing of signal constellation points for the diamond-shaped 32-QAM scheme:
Figure 3 shows an extract of the diamond-shaped 32-QAM scheme.
Figure 3. Design of diamond-shaped 32-QAM scheme (extract)
In this scheme we assume that two of the signal constel­lation points are placed
with a fixed phase offset of 22.5° on a circle with radius r r2 r3. A third point
is located at a larger radius r4◊, while the Euclidean distance α to the first two points
Chapter 6: Spectrum Management and Software Defined Radio Techniques
177
on radius r shall be the same as in between the first mentioned two points. The
fourth point is located at a smaller radius r1◊. Again, in order to make sure that the
mean energy Es of the resulting diamond-shaped 32-QAM scheme is the same as for
the original 8-PSK scheme (see first design rule), it can be shown that r and r1◊
have to satisfy the condition

r1

2

4 ES  2  cos11.25 3 sin11.25  r 2 . (4)
Note, while doing the consistency check, whether the diamond-shaped 32-QAM
scheme degenerates to the original 8-PSK scheme, e.g. by letting  r1  r  0 , we
have to keep in mind that at the same time the phase offset of 11.25° disappears.
With this in mind, the term in the inner parenthesis in (4) becomes 1.
Again, in order to ensure that the Euclidean distance between the inner signal
constellation points on radius r1◊ is at least α, the terms r and r1◊ have also to satisfy
the condition
sin11.25
r1 
r. (5)
sin 22.5
From (4) and (5) follows
r  0.5098
r2  r3  1.0001
 with   0.3902. (6)
r4  1.31878
The minimum Euclidean distance between any combination of two signal constellation points in the diamond-shaped 32-QAM scheme is α◊ = 0.3902. This is slightly
higher than for the star-shaped 32-QAM scheme α* = 0.331. This offers the potential
for a higher performance as it will be discussed in more detail in Section IV.
Finding the optimal labels for the star-shaped resp. diamond-shaped 32-QAM
scheme: The symbol labels for both 32-QAM schemes shall be chosen such
that a receiver design based on the BICM-ID concept is optimally supported.
The concept of BICM-ID was first described in [2] and is based on a serial
concatenation of a Forward Error Correction (FEC) component, a bit-level interleaver
and a digital modulation scheme. On the receiving end, there is a feedback loop
between the BCJR-decoder [7] and the demodulator. So-called Extrinsic Information
generated by the BCJR-decoder is interleaved and then fed back to the demodulator
as additional a priori information for the received channel symbols.
It has already been shown in [3] that the BICM-ID concept is optimally
supported if a so-called Semi Set Partitioning (SSP) symbol labeling is applied.
For a given signal constel­lation the SSP mapping ensures that the so-called
Harmonic Mean dH [3] is maximized. Table I shows the Harmonic Means for all
signal constellations under consideration in this paper.
3)
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Table I. Harmonic Means for different Signal Constellation Sets
Restricted SSP
Free-running SSP
Signal Constellation Set
Harmonic Mean dH
8-PSK (Gray)
0.809
Star-shaped 32-QAM
0.470
Diamond-shaped 32 QAM
0.403
8-PSK
2.877
Star-shaped 32-QAM
2.501
Diamond-shaped 32 QAM
2.520
If we are totally free in designing the symbol labeling the maximum achievable values are given in the lower part of Table I (marked as free-running SSP). It can
be seen that (besides the better α◊ > α* mentioned above) the diamond-shaped
32-QAM offers also a slightly higher Harmonic Mean dH if compared to the starshaped 32-QAM. Note, a comparison to the optimal dH for the 8-PSK constellation
with a free-running SSP is not fair because due to the higher number of signal
constellation points in the 32-QAM schemes, the Euclidean distances (which are
taken into account by the Harmonic Mean) are typically smaller.
However, since our key objective is to ensure interopera­bility to legacy radios
we are not totally free in the design of the symbol labeling. We have to take into
account the labeling of the original 8-PSK scheme. For instance, in case of the
4.8 kbps mode of MIL-STD188-110B Appendix C [4] the original labeling is given
by a Gray labeling. The left part of Figure 1 shows the corresponding example. In this
paper, when designing the labelings for the 32-QAM schemes we restrict ourselves
to the case in which the labeling of the base-layer is part of the overall labels. For
the given example considered here that means that the Gray labeling of the 8-PSK
scheme determines the three leftmost bits in the overall five bit long labels for the
32-QAM schemes. Taking this restriction into account yields the Harmonic Mean
values mentioned in the upper part of Table I. Obviously, the restriction results
in significantly smaller Harmonic Mean dH values if compared to the free-running
optimization approach. Consequently, the attainable performance gains will be
significantly smaller. The corresponding SSP symbol labelings are shown in the
center and in the right part of Figure 1.
III. Simulation environment
In the following, the pros and cons of considering hierarchical modulation
in the porting process of legacy waveforms to SDRs shall be analyzed using a simulation example. For this purpose we use a simulation environment where the baselayer (i.e., the legacy system) resembles the 4.8 kbps mode of MIL-STD188-110B
Appendix C [4].
Chapter 6: Spectrum Management and Software Defined Radio Techniques
179
A block diagram for the simulation environment under consideration is shown
in Figure 4.
Figure 4. Block diagram of simulation environment with base-layer and enhancement-layer
signal processing
A. Base-layer
In the 4.8 kbps mode of MIL-STD188-110B Appendix C the input data is at first
encoded by a rate R = 1/2, constraint length L = 7 convolutional mother code with
generator polynomial G = {171,133}8 . Afterwards the codewords are punctured
to Rp = 3/4 using the puncturing pattern (1 1 1 0 0 1).
The punctured codewords are block interleaved using one out of several possible interleaver sizes. The interleaver size is mainly determined by the maximum
acceptable delay on the communication link and it must be a multiple i of 768 bits
(with i = 1,3,9,18,36,72).
In contrast to the original 4.8 kbps mode of MIL-STD188-110B Appendix C [4]
in this paper we neglect aspects like scrambling or synchronization. We focus on
an interleaver size of 6912 only, i.e. i = 9. Instead of a full tail-biting convolutional
code we use a terminated convolutional code. Anyhow, we assume that none of these
simplifications has a major impact on the relevance of our findings for an established
real-world legacy system.
B. Enhancement-layer
Similar simulation settings are applied to the enhancement-layer. The only
difference is that we have to take into account the lower number of bits which are
transmitted on the enhancement-layer (only 2 bits instead of 3 bits are considered
in the determination of modulation symbols). From that follows that the interleaver
size in the enhancement-layer is 4608. It is designed as an S-random interleaver.
C. Combination of base-layer and enhancement-layer
After having determined the interleaved blocks of punctured codewords
for the base-layer and the enhancement-layer, both data streams are multiplexed
to a single data stream. As mentioned earlier, in this paper we restrict ourselves
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Military Communications and Information Technology...
to the case in which the 3 bits of the base-layer determine the three leftmost bits
and in which the 2 bits of the enhancement-layer determine the two rightmost bits
of the overall 5 bit long labels at each signal constellation point (see labeling
in Figure 1).
As transmission channel serves an Additive White Gaussian Noise (AWGN)
channel with known ES /NO where ES determines the energy per modulation
symbol and NO the power spectral density of the AWGN. At the receiving end
of our simulation chain we apply the concept of BICM-ID independently to both,
the base-layer and the enhancement-layer. Note, the behavior of a legacy radio
is inherently included by decoding the base-layer without any BICM-ID iteration
(i.e., no feedback loop in Figure 4).
IV. Simulation results
Table II summarizes all relevant cases which need to be considered during
the evaluation.
Table II. Relevant cases to be considered (TX: transmit; RX: Receive)
Transmitter
Legacy Radio
Receiver
Software Defined Radio
Legacy Radio
TX: Base-Layer
RX: Base-Layer
TX: Base- & Enhancement-Layer
RX: Base-Layer
Software
Defined Radio
TX: Base-Layer
RX: Base- & Enhanc. Layer
TX: Base- & Enhancement-Layer
RX: Base- & Enhancement-Layer
A. Legacy radio as receiver & arbitrary transmitter
In a first experiment we want to analyze the performance for the cases in which
a legacy radio acts as receiver. We can expect a reduced performance if we use an
SDR as transmitter because of the adverse effects of the enhancement-layer.
Figure 1 illustrates the reason for our expectation. The demodulator of a legacy
system will make a decision for the most probable sector in the I/Q-plane. As an
example the sector for the bit pattern 000 is highlighted in light-grey. This sector
is exactly the same for all three cases, i.e. for a legacy radio transmitting the baselayer only (see leftmost plot in Figure 1) as well as for the star-shaped resp. diamondshaped SDR cases (see plots in the center and on the right of Figure 1). However,
the Euclidean distance of the signal constellation points to the sector resp. decision
boundaries becomes usually smaller for the SDR cases. Thus, a performance loss
in terms of Bit Error Rate (BER) behavior has to be tolerated.
Figure 5 shows the corresponding simulation results.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
181
Figure 5. Bit error rate performance of Legacy Receiver for Legacy resp. Software Defined
Radio Transmitters
As expected, it can be seen that the operator at the legacy radio will experience a performance loss of up to ~ 4 dB in ES/NO if a SDR with one of the 32-QAM
schemes serves as transmitter. The star-shaped 32-QAM scheme behaves slightly
better than the diamond-shaped 32-QAM scheme. One reason for this observation can be found in the fact that in the star-shaped 32-QAM scheme only the two
signal constellation points on radii r1*and r2*exhibit a smaller distance to the sector
resp. decision boundary than for the original 8-PSK scheme while in the diamondshaped 32-QAM scheme these are three, namely the points on radii r1◊and r2  r3
(cmp. to Figure 1).
Notice, as mentioned earlier the behavior of a legacy receiver is inherently
included in Figure 4 by decoding the base-layer without any BICM-ID iteration
(i.e., no feedback loop). In addition, Figure 5 also shows for all three transmitters
a second curve labeled Error-Free Feedback (EFF). These curves illustrate the best
possible BER performance attainable in a BICM-ID system. For this purpose,
during the simulation the values on the feedback loop are directly taken from the
transmitter side. With this, they can be considered to be error-free. Obviously, due
to the Gray labeling the perfor­mance differences between the non-iterative legacy
scheme as well as the error-free feedback bound are negligibly small.
B. SDR as receiver & legacy radio as transmitter
In this case the base-layer of the SDR receiver reveals a similar performance
as the legacy receiver because the sector resp. decision boundaries are identical
(cmp. to Figure 1). The only difference is that the additional interference/noise
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Military Communications and Information Technology...
being introduced by the enhancement-layer is not added at the transmitter of the
communication link, but at the receiving end. The corresponding simulation results have already been presen­ted in Figure 5. Since the transmitter was a legacy
radio, there is no information on the enhancement-layer. Thus, decoding the
enhancement-layer does not provide any benefit and can be skipped. The decision,
if the enhancement-layer needs to be decoded, can be derived from the side information whether transmitter is a legacy radio or an SDR. We assume that this side
information can be provided to all radio operators once in advance, e.g. during the
network planning/management phase.
C. SDR as receiver & SDR as transmitter
Last but not least, we consider the case using an SDR on both ends of the
communication link. Figure 6 shows the corresponding simulation results.
Figure 6. BER performance of SDR receiver for SDR transmitter
As a reference we also plotted a copy of the BER curve for the case where we
have legacy systems on both ends. In order to allow a fair comparison between all
curves they are plotted as a function of the mean energy EB per user data bit. As
a consequence the BER curve for the legacy system shown in Figure 5 is shifted by
10 • log10 (4 / 3 • 1 / 3) = –3.52 dB to the left (w.r.t. the punctured code rate Rp
and number of bits per symbol). Since we transmit in total 5 bits per symbol using the SDR modes, all the other curves include a shift to the left by
10 • log10 (4 / 3 • 1 / 5) = –5.74 dB.
Figure 6 shows two sets of simulation results, one for the star-shaped 32-QAM
scheme and one for the diamond-shaped 32-QAM scheme. Each set contains BER
Chapter 6: Spectrum Management and Software Defined Radio Techniques
183
curves for different numbers of iteration I = 0,1,5,10 and for error-free feedback.
It can easily be seen that performance improvements are possible by iterations for
both sets. In addition, the diamond-shaped 32-QAM scheme outperforms the starshaped 32-QAM scheme thanks to the better properties mentioned in Section II.B.
However, it can also be seen that due to the restrictions in the design of both
schemes, there is still a considerable gap to the reference curve. The SDR modes
perform even worse than the original 8.0 kbps mode of MIL-STD188-110B
Appendix C [4] which also allows transmitting 5 bit per symbol.
Note, the simulation results for both 32-QAM schemes reveal a plateau at a BER
of 0.2. The reason for this plateau can be found in the fact that the plotted BER
curves show the mean BER for the base-layer and the enhancement-layer. Since the
base-layer is more robust than the enhancement-layer, the three bits transmitted
on the base-layer exhibit a much better BER performance than the two bits on the
enhancement-layer. Thus, in this range of EB/N0, where the plateau appears, the
mean BER can be approximated by 2 / 5 • 0.5 = 0.2.
V. Next steps in our ongoing research activity
In the preceding sections the basic idea, the design, and some first results of our
ongoing research activity have been described. Unfortunately, these first results
show that with the given restrictions in the design of the 32-QAM schemes no
added value, in terms of data throughput, can be offered to the operator at the SDR.
A. Improved robustness
Anyways, in parallel we already started some investigations if an improved
robustness (and with this maybe communication range) can be offered as added
value, e.g., by transmitting extra error protection information on the enhancementlayer. For instance, we can transmit the parity check information which had been
eliminated from the coded data stream by puncturing.
Figure 7 shows a first exemplary simulation result where we use a rate 9/20
FEC code in combination with the 32-QAM scheme instead of the rate 3/4 code
and the 8-PSK scheme. The effective ratio of data bits carried in each modulation
symbol remains the same, while at the same time interoperability to the legacy
system remains preserved.
The simulation results in Figure 7 show that thanks to the stronger error
protection scheme an added value in terms of higher robustness can be provided
to the SDR operator. A single iteration is sufficient to provide a gain if compared
to the legacy 8-PSK scheme. After 5 Iterations gains of more than 3 dB in ES/NO
can be realized.
A more detailed analysis of this case is beyond the scope of this paper and
will be presented in [8].
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Military Communications and Information Technology...
Figure 7. BER performance of robust SDR schemes transmitting extra error protection
information on the enhancement-layer
B. Further potentials for improvement
In addition, we see some aspects which offer the potential for improvement
of both usages of the enhancement layer, either for the higher throughput or the
higher robustness.
Currently, the design of the Multiplexer resp. De-Multiplexer shown in Figure 4
reveals some limiting restrictions. The Multiplexer combines the data streams of the
base-layer and the enhancement-layer to a single data stream by simply combining the 3 bit resp. 2 bit patterns to a single 5 bit long label. As a consequence, the
BICM-ID decoders for the base-layer as well as the enhancement-layer shown
in Figure 4 run strictly in parallel and cannot benefit from each other. In addition,
from the current Multiplexer design it follows that the Harmonic Mean dH of the
restricted SSP optimization is substantially smaller than the dH of a free-running
SSP optimization (see Table I). A more sophisticated Multiplexer/Combiner might
provide higher Harmonic Mean dH values and it might support a joint decoding
approach of both layers.
Figure 8 shows a simulation using the free-running SSP labeling. This simulation can be considered as an upper practical performance bound for an alternative
Multiplexer/ Combiner design. Obviously, significant performance gains can be
realized if compared the preceding simulations using the restricted SSP labeling.
As a next step of our ongoing research activity, we will analyze how to design
an alternative Multiplexer/Combiner which comes closer to the practical bound.
In addition, we will investigate how close this practical performance bound can
be approached.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
185
Figure 8. BER performance of SDR Receiver for SDR transmitter with an optimal SSP
symbol labeling
VI. Conclusions
In this paper, we analyzed if an added value can be provided to an SDR
operator using an enhanced legacy waveform. For this purposes the concept
of hierarchical modulations has been applied in the porting process. The original
legacy waveform is represented as the base-layer. The focus of the present paper
has been to present the basic idea, the major design considerations as well as some
first simulation results. Unfortunately, these results have shown that due to some
restrictions in the design process no gains in terms of data throughput can be
realized. However, an outlook on an ongoing research activity has been given
which allows providing a significant gain in terms of robustness. More details
will follow in a complementing paper [8].
Finally, with respect to the original question being raised in the title of this
paper, we conclude: partially yes, applying hierarchical modulations in the
porting process of legacy waveforms allows realizing an improved robustness.
We also see a potential for providing a higher throughput, but, however, so far
we have not been able to exploit the additional capacity of the enhancementlayer to realize this.
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References
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Data Fusion Schemes for Cooperative Spectrum
Sensing in Cognitive Radio Networks
Djamel Teguig1, 2, Bart Scheers1, Vincent Le Nir1
1
Royal Military Academy – Department CISS,
Polytechnic Military School-Algiers-Algeria,
Renaissance Avenue 30-B1000 Brussels, Belgium,
{djamel.teguig, bart.scheers}@rma.ac.be, [email protected]
2
Abstract: Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive
radio network (CRN) by combining sensing information of multiple cognitive radio users. In this
paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown
through computer simulation that the soft combination scheme outperforms the hard one at the cost
of more complexity; the quantized combination scheme provides a good tradeoff between detection
performance and complexity. In the paper, we also analyze a quantized combination scheme based on
a tree-bit quantization and compare its performance with some hard and soft combination schemes.
Keywords: Cooperative spectrum sensing, cognitive radio (CR), data fusion, soft, quantized and
hard fusion rules
I. Introduction
In recent years, the demand of spectrum is rapidly increasing with the growth
of wireless services. The scarcity of the spectrum resource becomes more serious.
Cognitive radio provides a new way to better use the spectrum resource [1]. Therefore, a reliable spectrum sensing technique is needed. Energy detection exhibits
simplicity and serves as a practical spectrum sensing scheme. As a key technique
to improve the spectrum sensing for Cognitive Radio Network (CRN), cooperative
sensing is proposed to combat some sensing problems such as fading, shadowing,
and receiver uncertainty problems [2].
The main idea of cooperation is to improve the detection performance by
taking advantage of the spatial diversity, in order to better protect a primary user,
and reduce false alarm to utilize the idle spectrum more efficiently.
The three steps in the cooperative sensing process are [3]:
1. The fusion center FC selects a channel or a frequency band of interest for
sensing and requests all cooperating CR users to individually perform local
sensing.
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2. All cooperating CR users report their sensing results via the control
channel.
3. Then the FC fuses the received local sensing information to decide about
the presence or absence of signal and reports back to the CR users.
As shown in Fig. 1, CR3 suffers from the receiver uncertainty problem because
it is located outside the transmission range of primary transmitter and it is unaware
of the existence of primary receivers. So, transmission from CR3 can interfere with
the reception at a primary receiver. CR2 suffers from multipath and shadowing
caused by building and trees. Cooperative spectrum sensing can help to solve these
problems if secondary users cooperate by sharing their information.
Figure 1. Sensing problems (receiver uncertainty, multipath and shadowing)
To implement these three steps, seven elements of cooperative sensing are
presented [4] as illustrated in Fig. 2.
Figure 2. Elements of cooperative spectrum sensing [4]
Chapter 6: Spectrum Management and Software Defined Radio Techniques
•
189
Cooperation models: is concerned with how CR users cooperate to perform
sensing.
• Sensing techniques: this element is crucial in cooperative spectrum sensing
to sense primary signals by using signal processing techniques.
• Hypothesis testing: in order to decide on the presence or absence of a primary
user (PU), a statistical test is performed to get a decision on the presence of PU.
• Control channel and reporting: is used by CR users to report sensing result
to the FC.
• Data fusion: is a process of combining local sensing data to make cooperation
decision.
• User selection: in order to maximize the cooperative gain, this element provides us the way to optimally select the cooperating CR users.
• Knowledge base: means a prior knowledge included PU and CR user location, PU activity, and models or other information in the aim to facilitate
PU detection.
In this paper, we will focus on the data fusion rules. The decision on the presence of PU is achieved by combining all individual sensing information of local CR
users at a central (FC) using various fusion schemes. These schemes can be classified
as hard decision fusion, soft decision fusion, or quantized (softened hard) decision. The hard decision is the one in which the CR users make a one-bit decision
regarding the existence of the PU, this 1-bit decision will be forwarded to the FC
for fusion. In [5], a logic OR fusion rule for hard-decision combining is presented
for cooperative spectrum sensing. In [6], two simple schemes of hard decision combining are studied: the OR rule and the AND rule. In [7]-[8], another sub-optimal
hard decision scheme is used called Counting Rule. In [9] that half-voting rule
is shown as the optimal hard decision fusion rule in cooperative sensing based on
energy detection. In the case of soft decision, CR users forward the entire sensing
result to the center fusion without performing any local decision. In [10] a soft
decision scheme is described by taking linear combination of the measurements
of the various cognitive users to decide between the two hypotheses. However, in [11]
collaborative detection of TV transmissions is studied while using soft decision
using the likelihood ratio test. It is shown that soft decision combining for spectrum sensing achieves more precise detection than hard decision combining. This
was confirmed in [12] when performing Soft decision combination for cooperative
sensing based on energy detection. Some soft combining techniques are discussed
in [13-14-15] as square-law combining (SLC), equal gain combining (EGC) and
square-law selection (SLS) over AWGN, Rayleigh and Nakagami-m channel.
The paper is organized as follows. We present in Section II the system model
related to cooperative spectrum sensing. In Section III, we describe different fusion
rules for cooperative spectrum sensing; several hard, soft and quantized schemes
are proposed and discussed. Simulation results in section IV are given to compare
these fusion rules. We conclude this paper in Section V.
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II. System model
Consider a cognitive radio network, with K cognitive users (indexed by
k = {1, 2 ... K}) to sense the spectrum in order to detect the existence of the PU.
Suppose that each CR performs local spectrum sensing independently by using N
samples of the received signal. The spectrum sensing problem can be formulated
as a binary hypothesis testing problem with two possible hypothesis H0 and H1.
H : xk ( n )  wk ( n )
(1)
H
x

n
h
s
n

w
n
:
(
)
(
)
(
)
k
k
k
Where s(n) are samples of the transmitted signal (PU signal), wk ( n ) is the receiver
noise for the kth CR user, which is assumed to be an i.i.d. random process with zero
mean and variance σn2 and hk is the complex gain of the channel between the PU and
the kth CR user. H0 and H1 represent whether the signal is absent or present respectively. Using energy detector, the kth CR user will calculate the received energy as [16]:
N
Ek   xk ( n ) (2)
In the case of soft decision, each CR user forwards the entire energy result
Ek to the FC. However, for hard decision, the CR users make the one-bit decision
given by Δk, by comparing the received energy Ek with a predefined threshold λk.
1, Ek  k

k 
(3)
0, otherwise
Detection probability Pd,k and false alarm probability Pf,k of the CR user k
are defined as:
Pd,k = Pr {Δk = 1|H1} = Pr {Ek > λk|H1}
Pf,k = Pr {Δk = 1|H0} = Pr {Ek > λk|H0}
Assuming that λk = λ for all CR users, the detection probability, false alarm
probability and miss detection Pm,k over AWGN channels can be expressed as follows respectively [17]
Pd
Qm ( 2  ,  ) (4)
,k
Pf ,k 
 ( m,  / 2)
(5)
(m)
Pm ,k  1 Pd ,k (6)
where γ is the signal to noise ratio (SNR), m = TW is the time bandwidth product,
QN(.,.) is the generalized Marcum Q-function, Г(.) and Г(.,.) are complete and
incomplete gamma function respectively.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
191
III. Fusion rules
This section describes the fusion rules that are used for the comparison.
III.1. Hard decision fusion
In this scheme, each user decides on the presence or absence of the primary
user and sends a one bit decision to the data fusion center. The main advantage
of this method is the easiness the fact that it needs limited bandwidth [12]. When
binary decisions are reported to the common node, three rules of decision can be
used, the “and”, “or”, and majority rule. Assume that the individual statistics Δk
are quantized to one bit with Δk = 0, 1; is the hard decision from the kth CR user.
1 means that the signal is present, and 0 means that the signal is absent.
The AND rule decides that a signal is present if all users have detected a signal.
The cooperative test using the AND rule can be formulated as follows:
H

K

H
otherwise
(7)
The OR rule decides that a signal is present if any of the users detect a signal.
Hence, the cooperative test using the OR rule can be formulated as follows:
K
H :  k 1
k
H : otherwise
(8)
The third rule is the voting rule that decides on the signal presence if at
least M of the K users have detected a signal with 1≤ M ≤ K. The test is formulated as:
H

M

H
otherwise
(9)
A majority decision is a special case of the voting rule for M = K/2, the same
as the AND and the OR rule which are also special cases of the voting rule for
M = K and M = 1 respectively.
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Cooperative detection probability Qd and cooperative false alarm probability
Qf are defined as:
K




Q
Pr


1
H

Pr


M
H


d
1
k
1


 i1
(10)


K
Q
Pr 
 1 H 0
 Pr   k  M H 0 
f
 i1




Where Δ is the final decision. Note that the OR rule corresponds to the case M = 1,
hence
Qd ,or 
1 
Q f ,or 
1 
K
k1
(1 Pd ,k ) (11)
K
k1
(1 Pf ,k ) (12)
The AND rule can be evaluated by setting M = K.
Qd ,and  
K
P (13)
k1 d ,k
Q f ,and  
K
k1
Pf ,k (14)
III.2. Soft data fusion
In soft data fusion, CR users forward the entire sensing result E k to the center
fusion without performing any local decision and the decision is made by combining these results at the fusion center by using appropriate combining rules such
as square law combining (SLC), maximal ratio combining (MRC) and selection
combining (SC). Soft combination provides better performance than hard combination, but it requires a larger bandwidth for the control channel [18]. It also
generates more overhead than the hard combination scheme [12].
Square Law Combining (SLC): SLC is one of the simplest linear soft combining schemes. In this method the estimated energy in each node is sent to the center
fusion where they will be added together. Then this summation is compared to
a threshold to decide on the existence or absence of the PU and a decision statistic
is given by [19]:
Eslc   Ek (15)
k
th
where Ek denotes the statistic from the k CR user. The detection probability and
false alarm probability are formulated as follow [19]:
Chapter 6: Spectrum Management and Software Defined Radio Techniques
Qd ,SLC QmK ( 2  slc ,  ) (16)
Q f ,SLC 
Where  slc 
193

 ( mK ,  / 2)
(17)
 ( mK )
and γk is the received SNR at kth CR user.
k
k
Maximum Ratio Combining (MRC): the difference between this method
and the SLC is that in this method the energy received in the center fusion from
each user is ponderated with a normalized weight and then added. The weight
depends on the received SNR of the different CR user. The statistical test for this
scheme is given by:
Emrc   wk Ek (18)
k
Over AWGN channels, the probabilities of false alarm and detection under
the MRC diversity scheme can be given by [21]:
Qd ,MRC  Qm ( 2  mrc ,  ) (19)
Q f , MRC 
Where:  mrc 

 ( m,  / 2)
(20)
(m)
k
k
Selection Combining (SC): in the SC scheme, the fusion center selects
the branch with highest SNR [20].

max( 1 ,  2 ,.....,  K )
sc
Over AWGN channels, the probabilities of false alarm and detection under
the SC diversity scheme can be written as [21]:
Qd ,SC Qm ( 2  sc ,  ) (21)
Q f ,SC 
 ( m,  / 2)
(22)
(m)
III.3. Quantized data fusion
In this scheme, we try to realize a tradeoff between the overhead and the detection performance. Instead of one bit hard combining, where there is only one
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threshold dividing the whole range of the detected energy into two regions, a better
detection performance can be obtained if we increase the number of threshold to
get more regions of observed energy.
In [12], a two-bit hard combining scheme is proposed in order to divide
the whole range of the detected energy into four regions. The presence of the signal
of interest is decided at the FC by using the following equation:
 w n  L (23)
i i
i
where L is the threshold and it is equal to the weight of the upper region,
ni is the number of observed energies falling in region i and wi is the weight value
of region i with w0 = 0 w1 = 1, w2 = 2 and w3 = 4.
In this paper, we extend the scheme of [12] to a three-bit combining scheme.
In the three-bit scheme, seven threshold λ1, λ2… and λ7, divide the whole range
of the statistic into 8 regions, as depicted in Fig. 3. Each CR user forwards 3 bit of information to point out the region of the observed energy. Nodes that observe higher
energies in upper regions will forward a higher value than nodes observing lower
energies in lower regions.
The three-bits combining scheme is performed in four steps:
1: Define a quantization threshold λi (i = 1 ... 7) for each region according to
the maximal received energy of the signal.
Figure 3. Principle of three-bit hard combination scheme
2: Each user makes a local decision by comparing the received energy with
the thresholds predefined in 1, and sends 3-bits information to the FC.
3: The FC sums the local decisions ponderated with weights wi (i = 0.., 7).
In our case, we have taken: w0 = 0, w1 = 1, w2 = 2, w3 = 3, w4 = 4, w5 = 5,
w6 = 6, w7 = 7.
4: The final decision is made by comparing this sum with a threshold L.
w
ik
k
 L (24)
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195
IV. Simulations and results
In this section we study the detection performance of our scheme through
simulations, and compare its performances with soft and hard fusion schemes.
First, we present the performance of the hard combining schemes as depicted
in Fig. 4. Secondly, we will compare the performance of the different fusion rules
in case of soft combining. Next, the two-bit and the three-bit quantized schemes
are compared in term of detection performance.
For the hard decision, we present in Fig. 4 the ROC curves of the ‘AND’ and
the ‘OR’ rule, and compare it to the detection performance of a single CR user. For
the simulations, we consider 3 CR users. Each user has a SNR of –2 db. As shown
in Fig. 4, the OR rule has better detection performance than the AND rule, which
provides slightly better performance at low Pfa than the OR, because the data fusion center decide in favor of H1 when at least one CR user detects the PU signal.
However in the AND rule, to decide of the presence of a primary user, all CR users
must detect the PU signal.
Figure 4. ROC for the hard fusion rules under AWGN channel, SNR = –2 dB, K = 3 users,
and energy detection with N = 1000
Fig. 5 shows the ROC curves of different soft combination schemes discussed
in section III.2 under AWGN channel. For the simulations, each CR user sees a different SNR. We observe from this figure that the MRC scheme exhibits the best
detection performance, but it requires channel state information. The SLC scheme
does not require any channel state information and still present better performance
than SC. When no channel information is available, the best scheme is SLC.
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Figure 5. ROC for soft fusion rules under AWGN channel with K = 3 users,
and energy detection with m = 5
Fig. 6 shows the ROC curves for quantized data fusion with 2-bit and
3-bit quantized combination, the figure indicates that the proposed 3-bit combination scheme shows much better performance than the 2-bit combination scheme
at the cost of one more bit of overhead for each CR user, this scheme can achieve
a good tradeoff between detection performance and overhead.
Figure 6. ROC curves for quantized data fusion under AWGN channel with SNR = –2 db,
K = 4 users and N = 1000 samples
Chapter 6: Spectrum Management and Software Defined Radio Techniques
197
For comparison, we show in Fig. 7 the ROC curves for the different fusion
rules under AWGN channel. As the figure indicates, all fusions method outperform the single node sensing, the soft combining scheme based on SLC rule
outperforms the hard and quantized combination at the cost of control channel
overhead, the 3-bit quantized combination scheme shows a comparable detection
performance with the SLC, with less overhead.
Figure 7. ROC for combining fusion rules under AWGN channel with K = 3 users, SNR = –2 db
and energy detection with N = 1000 samples
V. Conclusion
In this paper, the effect of fusion rules for cooperative spectrum sensing is investigated. It is shown that the soft fusion rules outperform the hard fusion rules.
However, these benefits are obtained at the cost of a larger bandwidth for the control
channel. The hard fusion rules occur with less complexity, but also with a lower
detection performance than soft combination schemes. The proposed quantized
three-bit combination scheme wins advantage of the soft and the hard decisions
schemes with a tradeoff between overhead and detection performance. In practical
application, we can select an appropriate method of data fusion and decision algorithms according to the requirement of detection performance and the requirement
of the available bandwidth for the reporting channel.
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References
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A doctoral thesis of Philosophy. The University of Edinburgh. January 2011.
Implementation of an Adaptive OFDMA PHY/MAC
on USRP Platforms for a Cognitive Tactical
Radio Network
Vincent Le Nir, Bart Scheers
Department Communication, Information, Systems and Sensors (CISS),
Royal Military Academy (RMA), 30, Avenue de la Renaissance, B-1000 Brussels, Belgium,
{vincent.lenir, bart.scheers}@rma.ac.be
Abstract: Cognitive radio is envisioned to solve the problem of spectrum scarcity in military networks and to autonomously adapt to rapidly changing radio environment conditions and user needs.
Dynamic spectrum management techniques are needed for the coexistence of multiple cognitive
tactical radio networks. Previous work has investigated the iterative water-filling algorithm (IWFA)
as a possible candidate to improve the coexistence of such networks. It has been shown that adding
a constraint on the number of transmitter’s sub-channels improves the convergence of IWFA. In this
paper, we propose an adaptive orthogonal frequency division multiple access (OFDMA) physical
(PHY) and medium access control (MAC) for the coexistence of multiple cognitive tactical radio networks. The proposed scheme is implemented on universal software radio peripheral (USRP) platforms
using Qt4/IT++ and the USRP hardware driver (UHD) application programming interface (API).
Keywords: Cognitive radio; adaptive OFDMA; distributed bit and power allocation; USRP
I. Introduction
Cognitive radio (CR) has been introduced by Mitola as an extension of software radio. In this technology, radio nodes are intelligent agents that search out
ways to deliver services according to the user needs and the radio environment [1].
CR has been an active topic of research since most regulatory bodies found that
the spectrum is underutilized although most available spectrum is licensed, leaving
small room for future wireless applications [2].
CR can also solve the problem of spectrum scarcity for military networks.
As the electro-magnetic environment in an operational theater can be very hostile,
cognitive tactical radio networks can autonomously adapt to rapidly changing
conditions and user needs [3].
The coexistence of cognitive tactical radio networks requires dynamic spectrum
management techniques in order to reduce the interference and to improve the performance in terms of throughput, power (longer battery life), and delay. Dynamic spectrum
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management techniques can be centralized/distributed (decisions are made centrally/
locally), cooperative/non-cooperative (some information is shared/not shared between
networks), and can use a horizontal/vertical sharing model (all networks have equal/
limited rights to access the spectrum) [4]. Our target application for the coexistence
of cognitive tactical radio networks corresponds to a mission involving multiple coalition nations in a foreign country with no a priori frequency planning, meaning that
there is no frequency management cell (FMC) to coordinate the different networks.
Moreover, the networks can’t exchange information between each other’s and they
have equal priority rights. Therefore, we are interested in a distributed non-cooperative
technique in a horizontal spectrum sharing model.
The iterative water-filling algorithm (IWFA) is an adequate dynamic spectrum
management technique to meet these requirements [5]. Indeed, IWFA is an autonomous algorithm solving the distributed power control problem in a frequency selective
interference channel. Robust versions of the IWFA have been designed to cope with
dynamic wireless channels [6, 7, 8, 9, 10]. However, IWFA does not converge to a unique
solution (multiple Nash equilibriums). This aspect is inherent to IWFA because at each
iteration some power is poured in the best sub-channel regardless the interference
caused to the other networks, while they have a better benefit avoiding each other by
taking different sub-channels. The convergence of the IWFA can be improved by adding
a constraint on the number of transmitter’s sub-channels [11, 12].
Random access multi-channel medium access control (MAC) protocols have
been proposed for CR networks [13]. Multi-channel MAC protocols have also been
designed for IWFA [6, 14]. These protocols use a dedicated control channel to coordinate the radios. It is unlikely that a dedicated control channel can be used in a military
context since it is a single point of failure. A possible workaround is to use a rendezvous
multi-channel MAC protocol; however the radios may beacon for a long time before
establishing a rendezvous. Another alternative is to employ time division multiplexing
access (TDMA) to obtain a collision-free transmission schedule or frequency division
multiplexing access (FDMA) as described in [15]. In this paper, we propose an adaptive orthogonal frequency division multiple access (OFDMA) PHYsical/MAC to
allow simultaneous collision-free transmissions in a cognitive tactical radio network.
The adaptive OFDMA PHY/MAC has the following characteristics:
• It is based on the IWFA with selection of a single sub-channel [11, 12].
It consists of grouping several OFDM sub-carriers to form a sub-channel.
In a first mode, it uses a fixed bit-loading per sub-carrier. In a second
mode, it uses an adaptive bit-loading related to the spectrum sensing and
the channel estimation on each OFDM sub-carrier.
• It is robust against multi-path due to the insertion of a cyclic prefix and
allows a single-tap equalizer due to the orthogonality of the sub-carriers.
• It uses a blind demodulation chain, meaning that it uses the cyclic prefix to
estimate blindly the timing offset and to detect the presence of an OFDM
signal. The timing offset estimate is used to synchronize control packets
Chapter 6: Spectrum Management and Software Defined Radio Techniques
203
and data packets, and to estimate blindly the frequency offset, transmission channel, phase offset and transmitted bits. The residual ambiguity
given by the BPSK or the adaptive QAM modulation schemes is solved by
transmitting a single sub-carrier pilot or by using differential encoding.
The adaptive OFDMA PHY/MAC has been implemented on universal software radio peripheral (USRP) platforms using Qt4/IT++ and the USRP hardware
driver (UHD) application programming interface (API). Qt is a cross-platform
application framework that is widely used for developing application software with
a graphical user interface (GUI) [16]. IT++ is a C++ library of mathematical, signal
processing and communication classes and functions. Its main use is in simulation
of communication systems and for performing research in the area of communications [17]. The goal of the UHD is to provide a host driver and API for current and
future USRP products. The UHD driver can be used standalone or with 3rd party
applications such as Gnuradio, Labview, or Simulink [18].
This paper is organized as follows. First, the adaptive OFDMA MAC protocol
is described in Section II. Second, the adaptive OFDMA PHysical layer functions
are described in Section III. The implementation of the adaptive OFDMA PHY/
MAC on USRP platforms using Qt4/IT++ and the UHD API is described in Section IV. Finally, Section V concludes the paper.
II. Adaptive OFDMA MAC protocol
In this Section, the adaptive OFDMA MAC protocol is described. It is assumed that the CRs have agreed on the same front-end parameters to transmit and
receive, i.e. carrier frequencies, sampling rates and bandwidths. It is also assumed
that the CRs have the same OFDM parameters, i.e. number of sub-carriers, cyclic
prefix (CP) size, and number of sub-channels. The adaptive OFDMA MAC protocol uses control packets for the handshaking between two CRs. These control
packets are request-to-send (RTS) and clear-to-send (CTS) packets. In a first mode,
the two control packets include source address, destination address and best subchannel sensed by the CR. In a second mode, control packets also include target
rate constraint, bit and power allocation. Therefore, unlike carrier sense multiple
access (CSMA) scheme and other random access multi-channel MAC protocols,
the two control packets convey some information and need to be aligned with
the timing of OFDM symbols transmitted in the same bandwidth of interest to
keep the orthogonality between sub-carriers.
A. First mode
The first mode uses a fixed bit-loading, e.g. a fixed BPSK or QAM modulation
over the different sub-carriers. In the following, we suppose a transmission from CR 1
to CR 2. CR 1 and CR 2 perform spectrum sensing in the bandwidth of interest and
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determine their best sub-channel based on an energy estimate. The RTS includes
the source address CR 1, the destination address CR 2, and the best sub-channel
of CR 1 given by spectrum sensing. CR 1 transmits the RTS on its best sub-channel.
CR 2 demodulates all the sub-channels in parallel (see Section III.C) and discovers
that a RTS is sent based on the destination address of CR 1. CR 2 gets the RTS source
address and the best sub-channel of CR 1. The CTS includes the CR 2 source address,
the CR 1 destination address, and the best sub-channel of CR 2 by spectrum sensing.
CR 2 transmits the CTS on CR 1 best sub-channel. CR 1 knows to receive on its best
sub-channel. CR 1 demodulates the CTS and gets CR 2 best sub-channel. Finally,
CR 1 transmits the data on CR 2 best sub-channel.
B. Second mode
The second mode uses an adaptive QAM over the different sub-carriers. CR 1 and
CR 2 determine their best sub-channel based on an energy estimate. The RTS includes
the CR 1 source address, the CR 2 destination address, the CR 1 best sub-channel,
and the target rate constraint. CR 1 transmits the RTS on its best sub-channel. CR 2
demodulates all the sub-channels in parallel and discovers that a RTS is sent based
on the destination address of CR 1. CR 2 gets the RTS source address, the target rate
constraint, and the best sub-channel of CR 1. If the CR 1 best sub-channel is the same
as the CR 2 best sub-channel, the CTS includes the CR 2 source address, the CR 1
destination address, the CR 2 best sub-channel, and the bit and power allocation for
CR 1 target rate constraint. CR 2 transmits the CTS on CR 1 best sub-channels. CR 1
knows to receive on its best sub-channel. CR 1 demodulates the CTS and gets CR 2
best sub-channel, as well as the bit and power allocation for its target rate constraint.
Finally, CR 1 transmits the data on their common best sub-channel with adaptive QAM.
If the CR 1 best sub-channel is different from the CR 2 best sub-channels, CR 1 gets
the best sub-channel of CR 2 by the CTS, and send a second RTS on CR 2 best subchannel to allow CR 2 to compute the bit and power allocation. CR 2 sends a second
CTS with this information on CR 1 best sub-channel. Finally, CR 1 transmits the data
on CR 2 best sub-channel with adaptive QAM.
III. Adaptive OFDMA physical layer
In this Section, the key functions of the adaptive OFDMA PHY are described,
i.e. the spectrum sensing, the OFDM signal detection, the blind OFDM demodulation and the distributed bit and power allocation.
A. Spectrum sensing
A CR needs to determine its best sub-channel and communicate this information to another CR. The bandwidth of the signal of interest B is divided into
Chapter 6: Spectrum Management and Software Defined Radio Techniques
205
a number of sub-channels S of width B/S. As there is no assumption about the type
of the observed signals, a nonparametric method should be used. We propose
to use the classical Barlett’s method for power spectrum estimation known also
as the method of averaged periodograms [19]. We assume that the complex sampling
rate equals the bandwidth of the signal of interest. The averaged periodograms for N
sub-carriers of a complex baseband signal with K blocks of N samples y = [y(kN),…
,y((k+1)N-1)] with k = [0,…,K-1] is given by

E i 
K1 N 1
j 2 in

1
N
|
(
)
|2 (1)
y
kN

n
e

KN k 0 n 0
with i = [0,…,N-1] bins. The best sub-channel selection consists of integrating
the estimated power spectrum of the frequency bins corresponding to the width B/S
for all sub-channels and determines the sub-channel which have the lowest energy
S opt = min
m
( m1) N
1
S

E (i ) (2)
mN
i
S
with m = [0,…,S-1]. This sensing procedure is used first by CR 1 for the selection
of its best sub-channel for RTS transmission, and is further embedded in the RTS
control packet. Secondly, this sensing procedure is used by CR 2 to communicate
its best sub-channel to CR 1 in the CTS control packet via CR 1 best sub-channel.
B. OFDM signal detection
The CR also needs to detect within the bandwidth of interest B the presence
of an OFDM signal even if the signal is sent over a single sub-channel. A survey
of OFDM signal detection techniques can be found in [20, 21, 22, 23]. Some
techniques require the knowledge of a pilot sequence, OFDM parameters, and
noise variance to determine the detection threshold. Detection techniques requiring the knowledge of a pilot sequence have better detection performance, but
worse bandwidth/power/complexity efficiency. Detection techniques requiring
the knowledge of the noise variance assume a white noise assumption and degrade
severely in the presence of colored noise. Moreover, when the theoretical values
of the threshold are not known, they have to be empirically computed by feeding
the detector with pure noise signals and calculating the test statistic.
In the proposed adaptive OFDMA MAC protocol, control and data packets
are sent only over a subset of the available sub-carriers. Moreover, other unknown
signals or colored noise can be present in the bandwidth of interest. We propose
to use a modified version of the cyclic prefix based sliding window correlation detector to determine the presence of an OFDM signal in the bandwidth of interest.
This detector does not require the knowledge of a pilot sequence but only requires
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the knowledge of the OFDM parameters, i.e. number of subcarriers N, cyclic prefix
size P. The detection threshold is based on the non-correlated part of the cyclic
prefix sliding window correlation estimate instead of the noise variance. This allows
detecting the presence of an OFDM signal even in the presence of an unknown
signal in the bandwidth of interest. Moreover, this detector also gives an estimate
of the OFDM timing offset when it is detected. Assuming a complex baseband
signal with K blocks of N+P samples samples y = [y(k(N+P)),…,y((k+1)(N+P)-1)]
with k = [0,…,K-1], the cyclic prefix sliding window correlation estimate is given by
K2 P1
|
(  ) 
k0
 y (k ( N  P)  j ) y (k ( N  P )  j  N ) |
*
j
( K 1) P  2y
(3)
with σy2 the variance of the received complex baseband signal. The absolute value
of the correlation estimate is able to cope with frequency and phase offsets introduced by Doppler shifts and clock mismatches. The estimate of the timing offset
is given by

 opt
max
() (4)
{0,..., N P1}
In order to determine the presence of an OFDM signal, the estimate of the timing offset is compared with the non-correlated part of the cyclic prefix sliding
window correlation estimate. Assuming a channel delay spread spanning the entire
cyclic prefix, the non-correlated part of the cyclic prefix sliding window correlation
estimate is given by ρ(mod(θopt + 2 P, N + P)). Assuming that the non-correlated
part is a Gaussian distribution with mean mnc and variance σnc2, the detection
threshold η is given by

 mnc   nc (5)
with α an integer corresponding to the number of standard deviations necessary
to discriminate between an OFDM signal and a non-OFDM signal using only
the cyclic prefix sliding window correlation estimate ρ(θ). The detector scheme is
( opt )   Presence of an OFDM signal
(6)
( opt )   Absence of an OFDM signal
Using K = 50 blocks in the application, α has been set to 40, allowing very few
false alarms and mis-detections.
C. Blind OFDM demodulation
The cyclic prefix sliding window correlation estimate can be used to estimate
the frequency offset [24]
Chapter 6: Spectrum Management and Software Defined Radio Techniques
opt 
207
1
(  opt ) (7)
2 N
After time and frequency offset corrections, the OFDM symbols are transformed to the frequency domain by the discrete Fourier transform (DFT) operation.
As there is no interference between two consecutive OFDM symbols, we obtain independent sub-carriers with the following channel model
Y ( kN 
i)
H ( kN  i ) X ( kN  i )  N ( kN  i ) (8)
with k = [0,…,K-1] and i = [0,…,N-1], in which Y(kN+i), H(kN+i), X(kN+i), and
N(kN+i) are respectively the demodulated data, the channel frequency response,
the transmitted symbol and the noise for the block k and sub-carrier i. Assuming
the channel invariant over the K blocks, a blind estimate of the channel amplitude
is given by
opt

| H i  |2
K 1
1
|Y (kN  i) |2 (9)
K k0
Assuming the channel invariant over the K blocks, a blind estimate of the phase
offset can be obtained for M-PSK signals [25] by the following expression
K1
1
(i ) est 
 Y ( kN  i ) M (10)
K k0
Modulation stripping has also been investigated for M-QAM signals in [26].
Knowing the phase offset estimate for each sub-carrier, it is possible to correct
linear shift of the phase in the frequency domain due to an incorrect timing offset
estimate belonging to the ISI free region, as well as abrupt changes of the phase
in the frequency domain due to the phase ambiguity introduced by the blind phase
offset algorithm (phase unwrapping). The phase correction for M-PSK signals
is performed using the Algorithm 1.
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D. Distributed adaptive bit and power allocation
The distributed bit and power allocation (adaptive QAM) can only be determined when a CR sends its control packet on the other CR best sub-channel. If CR 1
best sub-channel is the same as CR 2 best
sub-channel, then only one handshake
is necessary (RTS-CTS) to inform CR 1
about CR 2 best sub-channel, bit and
power allocation. However, if CR 1 best
sub-channel is different from CR 2 best
sub-channel, two handshakes are necessary (RTS-CTS-RTS-CTS) because CR 2
informs CR 1 about its best sub-channel
in the first CTS and the bit and power
allocation in the second CTS. The distributed bit and power allocation is based on
the waterfilling algorithm, in which an inner loop maximizes the bit allocation for
a transmit power constraint, and an outer
loop minimizes the transmit power for
a target rate constraint. The algorithm
for distributed bit and power allocation
is described in Algorithm 2, in which
λ is the Lagrangian parameter [27],
Г is the SNR gap which measures the loss with respect to theoretically optimum
performance [29], p = [p(0),…,p(N-1)] and popt = [p(0)opt,…, p(N-1)opt] are the power
allocation vectors, bopt = [b(0)opt,…,b(N-1)opt] is the bit allocation vector, Ptot is the total
power constraint, Δf is the sub-carrier bandwidth, R is the data rate and Rtarget is the
target rate constraint. As shown in [11, 12], when the number of sub-channels is lower
than the number of concurrent links, CR users have to share the same sub-channels
and iterative updates of bit and power allocation can lead to convergence problems
due to multiple Nash equilibriums. To avoid this problem, the number of sub-channels
is larger than the number of concurrent links to ensure the convergence to a single
Nash equilibrium. This leads to a distributed adaptive OFDMA solution.
IV. Implementation using QT4/IT++ and the UHD API
In this Section, the implementation of the adaptive OFDMA PHY/MAC on
USRP platforms using Qt4/IT++ and the UHD API is described. Several classes
and procedures have been implemented for this application using QThread to enable multi-threading, Qwt to enable plotting, IT++ to enable mathematical operations, and Gstreamer to enable video/audio transmission and reception.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
209
A. Names and description of the implemented classes
•
•
•
•
•
•
•
•
•
Class BitWaterfilling. This class does the bit and power allocation according to
spectrum sensed and estimated channel for a target rate constraint or a total
power constraint.
Class BlindOFDM. This class gets a vector of bits from a named pipe (FIFO)
coming from text, video, or audio in the application. It modulates this vector
of bits into a fixed BPSK, QAM or an adaptive QAM OFDM vector according
to its best group of subcarriers. It also performs a blind detection of a received
OFDM vector based on the cyclic prefix, and blindly demodulates a received
fixed BPSK, QAM or an adaptive QAM OFDM vector into a vector of bits
according to its best group of subcarriers (blind time offset correction, blind
frequency offset correction, blind phase offset estimation). Finally, it puts
a vector of bits into a named pipe (FIFO) to be read by a text reader, video
reader, audio reader in the application.
Class File. This class converts from/to characters to/from bits. It also reads/
writes a vector of bits from/to a file.
Class MainWindow. This class is dedicated to the GUI thread. This class
has a first push Button to Start/Stop Tx, a second push Button to Start/Stop
Rx, and a third push Button to Start/Stop Video. It uses a lineEdit to take command or to input text and textEdit to display some text. It also uses multiple
lineEdits to control Tx rate, Tx frequency, Tx gain, Tx amplitude, Rx rate, Rx
frequency, Rx gain, FFT size, CP size, and number of sub-channels.
Class Packets. This class does a conversion between vector of a double, float,
integer and a vector of bits to include in the RTS/CTS packets. It encodes/
decodes the RTS/CTS packets with necessary information (source address,
destination address, best sub-channel, bit and power allocation).
Class Plot. This class plots some information (spectrum sensing, best group
of sub-channels, target rate constraint, bit and power allocation).
Class Protocols. This class is dedicated to the worker thread (QThread).
It can reinitialize the parameters as requested by the GUI (FFT size, CP size,
number of sub-channels). This class implements a state machine for a Tx/Rx
handshaking adaptive OFDMA MAC protocol based on RTS sending/listening,
CTS listening/sending and data sending/listening. It also performs a time offset
estimation before RTS sending, CTS sending to avoid inter-carrier interference.
Class Sensing. This class estimates the spectrum based on averaged FFT. It takes
a decision based on the mean spectrum estimated shifted by a fixed amount
of dB, and selects the best group of sub-carriers according to the number
of sub-channels.
Class Text. This class is implemented as a separate thread (QThread). It reads
some input text in the GUI and put it in a named pipe (FIFO). It also displays
some text in the GUI from a named pipe (FIFO).
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•
•
Military Communications and Information Technology...
Class UHDDevice. This class reinitializes USRP parameters as requested
in the GUI (Tx rate, Tx frequency, Tx gain, amplitude, Rx rate, Rx frequency,
Rx gain). It reads a certain amount of samples at a particular time (timestamp). It writes a certain amount of samples at a particular time (timestamp).
It also checks for errors after sending/receiving data. The procedures are
implemented such that the received and transmitted samples are in a steady
state for the USRP (first samples should be discarded because of the time to
power up).
Class Video. This class is implemented as a separate thread (QThread). It uses
a Gstreamer transmit pipeline showing the video transmitted on screen and
writing the same data in a named pipe (FIFO) which will be read by the OFDM
modulator for transmission. It also uses a Gstreamer receive pipeline showing
the video received on screen if a video container is detected.
B. Description of the application
Figure 1 shows the application window for spectrum sensing. The GUI parameters can be chosen during run-time. The transmission parameters are the Tx
Rate in Msps, the Tx Frequency in MHz, the Tx Gain in dB, the Tx Amplitude
corresponding to a linear multiplication factor. The reception parameters are the Rx
rate in Msps, the Rx frequency in MHz, the Rx Gain in dB. The OFDM parameters are the FFT size, the CP size, and the number of sub-channels. There are two
buttons to Start Tx and to Start Rx, which correspond to different state machines.
The Tx state machine does a spectrum sensing, send RTS, receive CTS, and send
data procedure, while the Rx state machine does a spectrum sensing, receive RTS,
send CTS, and receive data procedure. The spectrum sensing is performed using
the method of averaged periodograms (Barlett’s method) described in Section II.
The red plot corresponds to the sub-carriers whose powers are larger than a decision threshold (in this case a mean decision threshold shifted by a fixed amount
of dB). One can see a DC offset due to the USRP and some occupied sub-carriers
between sub-carriers 320 and 340.
Figure 2 shows one of the plots provided by the application. The different
plots are the spectrum sensing and decision as shown in Figure 1 (Tab 1), the best
sub-channel plot (Tab 2), the estimated channel amplitude plot (Tab 3), the power
allocation plot (Tab 4), the bit allocation plot (Tab 5). For the system to work well,
a good compromise should be taken between the FFT size, CP size, the expected
frequency offset, and the number of OFDM symbols used for estimation of the channel. As shown on Figure 2, a good compromise has been found with an FFT
size 512, CP size 128, and number of OFDM symbols 50 such that we get a very
good estimate of the channel. This serves the bit and power allocation function to
have accurate values since the spectrum sensing is based on average periodograms
and the estimate of the channel is based on the average of multiple OFDM symbols.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
Figure 1. Application window for spectrum sensing
Figure 2. Application window for channel estimation
211
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Figure 3 shows the application window for text, video and audio (Tab 6). This
tab is used to input some text, video or audio in a named pipe called ‘inputpipe’
whenever the Tx state machine is launched. Once the enter command or the video
button is pressed, the Tx state machine switches from a spectrum sensing state to
a RTS sending state to initiate a communication. It is also used to output some text,
video or audio from a named pipe called ‘outputpipe’ whenever the Rx state machine
is launched. Once the enter command or the video button is pressed, the Rx state
machine switches from a spectrum sensing state to a RTS listening state to receive
a communication. After handshaking between the two CRs, data can be received
as shown on Figure 3.
Figure 3. Application window for text, video and audio
Future improvements of this application would be to implement other algorithms for spectrum estimates (Multitaper method, Welch, Hamming, Hanning...) and to implement decision algorithms based on noise estimation. However,
a whitening approach might take a long time because of eigenvalue decomposition.
Secondly, a comparison of the new OFDM signal detection algorithm with existing
algorithms in the literature could also lead to an improvement of the application.
Thirdly, it would be interesting to implement other algorithms for OFDM demodulation to compare different non-data-aided and data-aided approaches.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
213
V. Conclusion
In this paper, we have proposed an adaptive OFDMA PHY/MAC on USRP platforms for a cognitive tactical radio network. In the first part of the paper, the adaptive
OFDMA MAC protocol has been described, as well as the key function of the adaptive OFDMA PHY, i.e. the spectrum sensing, the OFDM signal detection, the blind
OFDM demodulation and the distributed bit and power allocation. In the second part
of the paper, the adaptive OFDMA PHY/MAC implementation on USRP platforms
using Qt4/IT++ and the UHD API has been described. Several classes have been
implemented, as well as support for text, video and audio transmission.
References
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Validation of the ITU 1546 Land-Sea
Propagation Model for the 900 MHz Band
Krzysztof Bronk1, Rafał Niski1, Jerzy Żurek1, Maciej J. Grzybkowski2
1
National Institute of Telecommunication,
Wireless Systems and Networks Department, Gdansk, Poland,
{K.Bronk, R.Niski, J.Zurek}@itl.waw.pl
2
National Institute of Telecommunication,
Electromagnetic Compatibility Department, Wroclaw, Poland, [email protected]
Abstract: The article discusses the accuracy of the ITU-1546 land-sea propagation model for
the 900 MHz band. This analysis is mainly based on the measurement results which were compared
with the data resulting from the theoretical model. This paper is comprised of four sections. Firstly,
the measurement methodology and scenarios are described. In the next section, the method of field
strength calculations for the mixed paths, introduced by the ITU, is briefly explained. The next, and
most important, part is filled with the comparison (and statistical analysis) of the theoretical and
measured field strength values, which allows to evaluate the model. Finally, in the last section, a short
conclusion is presented.
Keywords: component; land-sea propagation model, validation, measurements, GSM 900
I. Introduction
The following article presents the selected results of the extensive measurement
campaign at the Baltic Sea waters conducted as a part of the EfficienSea project [5, 6].
All the activities described in this paper were carried out by the National Institute
of Telecommunications (NIT) in cooperation with other EfficienSea partners, i.e.:
Maritime Office in Gdynia – MOG and Gdynia Maritime University – GMU.
The campaign took place in the second half of 2011 and comprised four
parts lasting from one to five days, during which a total of over 40 000 measurement points have been gathered. For the purpose of this campaign two ships have
been utilised: MS “Tucana” (operated by the MOG) – during the 1st and 2nd part
of the campaign and MS “Horyzont II” (operated by the GMU) – during the 3rd
and 4th part respectively.
The whole measurement campaign covered selected radiocommunications
systems available at sea, including 2G/3G, TETRA, Telenor VHF Data, Iridium.
In this paper the authors present some results for the GSM 900 system only.
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In the subsequent part of this paper the measurement methodology and measurement devices set will be presented. After that, the method of propagation loss
evaluation for the mixed propagation paths, based on the ITU 1546 recommendation, will be discussed. Finally, a comparative analysis of the measurement results and
the results of the predicted communication ranges, obtained from the computations
(performed with the software tool created by the NIT) will be presented.
II. Field strength measurements
As the part of the EfficienSea project activities, the measurement campaigns
at the Baltic Sea waters were conducted. The main goal of them was to analyse
the coverage, availability, type and quality of the 2G/3G data transmission services
offered by one of the Polish cellular providers at the Polish coastal waters (especially at the Bay of Gdansk and along the Polish coast line, see Fig. 7). The most
important output of this measurements was an indication which data transmission
services (EDGE, UMTS or HSPA) were available in the particular area of interest.
Besides that, a big number of additional information have been obtained, such as:
the actual achievable throughputs (for uplink and downlink), ping values, signal
levels, Cell-IDs, etc.
The measurement set for this part of the campaign can be schematically
depicted as in Fig. 1.
Figure 1. The measurement set for 2G/3G systems tests
Chapter 6: Spectrum Management and Software Defined Radio Techniques
217
The central and most important element of the measurement set was an application module. This software tool (developed by the NIT for the purposes of this
campaign) handled all the functions connected with control over the equipment and
FTP client, triggered the subsequent steps of the measurement algorithm, performed
the necessary calculations and stored the results. It also had a very strong capability of detecting and handling errors and unexpected situations, so consequently
the whole measurement process was as automatic as possible.
The user interface of this application is presented in Fig. 2. In this picture
the main functions of each of the modules are indicated.
Figure 2. User interface of measurement software tool
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Military Communications and Information Technology...
The second part of the measurement set was the GSM/UMTS module, based
on the Dev Kit platform by Sierra Wireless, which was originally manufactured for
telecommunication devices testing. This platform was equipped with a card slot
supporting both regular modems as well as professional ones for measurement
purposes. On the Dev Kit platform, a GSM/UMTS MC-8795V card was installed,
which supported HSPA data transmission with rates up to 7.2 Mb/s (downlink)
and 5.76 Mb/s (uplink). The communication between the application module and
the GSM/UMTS module was based on the AT commands.
The Dev Kit platform and the MC-8795V card are depicted in Fig. 3.
Figure 3. The Dev Kit platform and the MC-8795V card
Another part of the measurement set was the GPS module Holux M-215
– a USB wired GPS receiver equipped with a MTK chipset, which supported the data
transmission protocol NMEA0183 v.3.01. The module’s sensitivity was ­159 dBm
and the cold-start time was 36 seconds.
The additional parts of the measurement set were the Anritsu MS2721B
Spectrum Analyzer (high-performance handheld spectrum analyzer, operating
in the frequency range of 9 kHz ÷ 7.1 GHz, suitable for a great variety of RF, microwave or cellular signal measurements, even in the harsh physical environment)
and a highly-precise, calibrated antenna SAS-521F-7 by A.H. Systems.
The methodology of the measurement was as follows. In the NIT premises
in Gdansk, an external FTP server (connected to the Internet via a very fast fibre connection) has been activated, and 13 test files have been uploaded into it.
Those files contained some random data and they varied in size (the possible sizes
of the test files were: 10 kB, 25 kB, 50 kB, 100 kB, 250 kB, 500 kB, 1 MB, 2,5 MB,
5 MB, 10 MB, 25 MB, 50 MB and 100 MB). The rest of the necessary measurement
equipment was placed aboard the ship.
Using the GSM/UMTS/HSPA Module, a radio link between the ship and
the External FTP server was established. The module was able to work in a few
modes: preferred 2G or 3G or the best available service (EDGE/UMTS/HSPA),
which gave an instant information about the services available in a given area.
Chapter 6: Spectrum Management and Software Defined Radio Techniques
219
After establishing the connection, it was possible to start a bi-directional data
transmission between FTP  ship and to measure its duration. In order to test
the downlink transmission, the application module started to download a file
from the FTP server; to test the uplink transmission, the same file was uploaded
into the FTP. The size of the file that was being transmitted in a given moment
was not random, but selected by the application module according to the special
algorithm. If the available throughput was low, the file size was gradually decreased, so that the duration of a single measurement was not excessively long;
on the other hand, if the throughput increased, it was also possible to increase
the size of a test file, so the file size was being changed adaptively. When the transmission was complete, the software calculated the real throughput (separately for
downlink and uplink), which was done through dividing the (known) file size by
the (measured) transmission duration. The GSM/UMTS/HSPA modem allowed
to obtain some additional parameters as well, i.e.: signal level (in dBm), number
of the utilized GSM/UMTS channel and Cell ID. Through the ICMP protocol,
the value of the ping was also extracted.
Additionally, the application module was connected with the Anritsu MS2721B
programmable spectrum analyzer to enable the optional channel power measurements.
An integral part of the measurement set was a GPS module which provided
a precise location- and time-stamp for every single measurement record (additionally,
the current speed in km/h, course in degrees and altitude in m AMSL were obtained).
After finishing a complete set of a measurement, the application module created a record with the results. Every record comprised the following information:
• Date and time of the measurement;
• Geographic coordinates (longitude and latitude);
• Direction of radio link (downlink/uplink);
• Measured transmission throughput [in kb/s];
• Measurement duration [in ms];
• Amount of transmitted data [in bytes];
• Ping [in ms];
• Available service [EDGE/HSPA/UMTS];
• Signal level [in dBm];
• Speed [in km/h];
• Course [in deg];
• Altitude [in m AMSL];
• Cell ID;
• Nr of GSM/UMTS channel;
• Channel power [in dBm].
Since the spectrum analyzer made its own measurements as well, it also created a log file with the results. The analyzer was also equipped with an in-built GPS
receiver, so every record in this log file was marked with a time-stamp and precise
geographic coordinates of the point where the current measurement took place.
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Military Communications and Information Technology...
In some measurement campaigns it was not possible to use the spectrum
analyser, therefore to obtain the same conditions of received signal lever measurements, the modem measurement method was chosen. Moreover, the modem
provided additional parameters which were important part of the measurements.
It should also be noted that all measurements were performed on the move,
and the ship’s speed was not greater than 20 km/h.
III. Method of mixed path field strength calculations
The results of the radio wave field strength measurements in the 900 MHz
band, carried out for the mixture of land and see paths, have been compared to
the field strength calculations performed according to the method provided by
the ITU-R P.1546-4 Recommendation [1]. In this Recommendation, the mixedpath method of calculations utilises the values of El (d) and Es (d) to represent
the field strength at a specific distance d from the transmitting antenna. The additional factor, i.e. the representative clutter height, R, for all-land and all-sea paths
respectively, interpolated for transmitting/base antenna height h1, as well as for
frequency (900 MHz) and percentage time (50%), has been used as required.
The receiving/mobile antenna height used for the purpose of the calculations
was equal to the real height of the antenna mounted at the shipboard on the level
of 10 meters over sea surface.
The mixed path field strength, E [dB(μV/m)], has been evaluated in line
of the ITU method [1] as:
E  1 A El  d T   A Es  d T  (1)
where:
dT – total distance between transmitter and receiver [km],
El – field strength calculated for land path zone at the distance dT [dB(μV/m)],
Es – field strength calculated for see path zone at the distance dT [dB(μV/m)],
A – mixed path interpolation factor, given by formula:
2/3
A  [1 1 Fsea  ]V (2)
where the fraction of path over sea, Fsea, is given by:
and the factor V is expressed by:
Fsea 
d sT
(3)
dT

 

V max1.0, 1.0 
(4)


40.0 
Chapter 6: Spectrum Management and Software Defined Radio Techniques
221
with:
Ns
M
l
d
d

  Esn  d T  sn   Elm  d T  lm (5)
d sT
d lT
n 1 
m 1
where:
Esn(dT) –field-strength value [dB(µV/m)] for distance dT, assumed to be all of sea
or coastal-land zone type n,
Elm(dT)–field-strength value [dB(µV/m)] for distance dT, assumed to be all of land
zone type m.
The length of total propagation path dT is equal to:
d
d sT  d lT (6)
T
where:
d sT   d sn –total length of sea and coastal land paths traversed by radio
n
wave [km]
d lT   d lm – total length of land paths traversed by radio wave [km]
(7a)
(7b)
m
When the radio path consists of two parts of land or sea zones, then Ns=2
and Ms=2 respectively. When a mixed path includes only one land and only one
sea zone, then Ns=Ms=1. Only those types of paths (see Fig. 7) were taken into
consideration during the mixed radio path field strength calculations. A detailed
method to determine the particular field strength values E (El or Es), using the field
strength versus distance curves, valid for the land and/or see paths, is described
in the ITU-R P.1546-4 Recommendation.
The mixed path field strength calculations were conducted by means of the Digital Terrain Model (DTM) of Polish coast and Baltic Sea. DTM model SRTM-3 with
the 3 seconds resolution, which means, for areas in Poland, a rectangle of the size
60x90 meters, has been used during these calculations.
The comparison of the mixed path field strength calculations’ results obtained
for the 900 MHz frequency band at the distance up to 37 km from the transmitting antenna are illustrated in the next section. These results were necessary
to validate the quality of the mixed path field strength prediction provided by
the ITU 1546 method.
IV. Comparison of results of measurements and field strength
calculations
All the presented measurements were carried out on board the MS Horizon II
ship (see Fig. 4). The antennas of the measurement set were placed 10 m AMSL
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Military Communications and Information Technology...
and the measurement route was within the Bay of Gdansk and along the east part
of the Polish coastline – measurement locations are depicted in Fig. 7. For the further
analysis, eight GSM 900 base stations (for which the distance from the sea was at
least 5 km) have been selected. The BTSs’ parameters obtained from the network
operator are given in the Table 1. In the considered case, the measurement points
for seven stations correspond to the path profile type 1 (land – sea) and for one
station the type 2 of the profile is more suitable (land – sea – land – sea). For all
of the stations the statistical analysis of the obtained field strength measurements
results has been conducted. The input parameter, i.e. the difference between the calculated and measured field strength values, ΔE, is given as follows:
E E1546  Emeas [dB] (8)
where E1546 denotes the median of the field strength calculated on the basis of the
ITU 1546 model and Emeas indicates the field strength value obtained in the measurements.
The levels (values) of the electric field strength E were calculated on the basis of the parameters shown in table 1 (including BTS antenna’s characteristics),
in the software tool developed by the NIT [2], in which the newest version of the
ITU 1546-4 Recommendation was implemented.
In Figs. 6 and 7 the values of ΔE as a function of distance for different
types of the path profile were shown. It is noteworthy that the red line denotes
the mean value and the green ones represent the standard deviation.
Figure 4. Horyzont II – the ship utilized during the measurement campaign
Chapter 6: Spectrum Management and Software Defined Radio Techniques
Figure 5. Dependence of ΔE on distance for propagation path type 1
Figure 6. Dependence of ΔE on distance for propagation path type 2
Figure 7. Measurement area
223
29,8
36,7
-6,5
8,5
2,3
3,0
Max path length
ΔE min [dB]
ΔE max [dB]
average of ΔE [dB]
RMSE [dB]
1
Path type
Min path length
390
BTS1
Measurements number
BTS Name
27,5
330
Antenna azimuth [deg]
EIRP [dBW]
46
Antenna height [m] AGL
K 739650
66
GSM channel
Antenna type
59
54,48
Latitude [deg]
Terrain height [m] AMSL
16,55
BTS1
Longitude [deg]
BTS Name
2,6
-5,2
-0,5
-11,5
16,4
15,7
1
75
BTS2
26,9
27,2
K 730691
60
49
49
67
54,74
17,90
BTS3
27,1
K 739 685
70
39
65
142
54,50
18,46
BTS4
2,9
-7,1
-0,5
-12,5
30,2
16,4
1
118
BTS3
1,1
-8,1
-5,5
-10,5
35,6
26,9
1
141
BTS4
Table II. Detailed analysis results
K 739650
330
48
59
67
54,74
17,90
BTS2
4,1
-4,4
0,5
-10,5
33,4
26,6
1
24
BTS5
27,9
K 742 272
145
35
50
140
54,47
18,47
BTS5
Table I. BTS information obtained form the network operator
1,7
-7,9
-4,5
-11,5
23,5
19,7
1
26
BTS6
26,3
K 739622
60
16
65
127
54,34
18,56
BTS6
3,7
-4,6
-0,5
-13,5
34,1
16,9
1
19
BTS7
25,2
K 742264
60
23
56
43
54,36
18,64
BTS7
2,9
-0,2
8,5
-6,5
33,2
28,1
2
53
BTS8
26,8
K 730691
300
49
65
102
54,29
19,61
BTS8
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Military Communications and Information Technology...
Chapter 6: Spectrum Management and Software Defined Radio Techniques
225
Table 2 presents the results of the analysis, i.e.: number of measurements,
minimum and maximum distance between measurement points and BTSs (length
of the radio path), minimum and maximum ΔE values as well as the average value
of the ΔE and RMSE.
In the Table 3 the summary results of the measurement errors, for both types
of the path profile as well as for all measurement points, are presented.
Table III. Overall analysis results
BTS Name
BTS1-7
BTS8
All
Measurements number
793
53
846
1
2
1 & 2
Min path length
15,7
28,1
15,7
Max path length
36,7
33,2
36,7
ΔE min [dB]
–13,5
–6,5
–13,5
ΔE max [dB]
8,5
8,5
8,5
average of ΔE [dB]
–2,4
–0,2
–2,2
RMSE [dB]
5,4
2,9
5,3
Path type
The obtained results show a very good agreement of the measurement data
with the results obtained from the mathematical model introduced by the ITU
in the recommendation 1546-4. It is proved by the small values of the average error
ΔE, which amounts to –2,4 dB for the propagation paths of type 1 (land-sea) and
–0,2 dB for the propagation paths of type 2 (land-sea-land-sea), as well as –2,2 dB
on average. Negative values of the average ΔE mean that in most cases the measured
level of the signal strength was higher than expected.
Additionally, the relatively small values of the RMS error, which are respectively: 5,4 dB for propagation path type 1, 2,9 dB for type 2 and 5,3 dB on average
verify positively the correctness of the measurement method and reproducibility of the measurements. Both values (average and RMS errors) are smaller for
the propagation paths type 2, which might suggest that for these type the correctness
of the model is better. On the other hand, it must be mentioned that the number
of the measurements for the propagation path type 1 was 793, whereas for type 2
it was almost 15 times less, merely 53.
V. Conclusions
In the paper the comparative analysis of the ITU 1546 land-sea propagation
model for the 900 MHz band and the actual field strength measurements results
has been presented. An important part of this research was a statistical analysis,
which allowed to validate the accuracy of the ITU 1546 model.
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Military Communications and Information Technology...
The comparative research and measurement [3,4], which has been carried
out earlier by the authors in the maritime VHF band, showed that the measured
signal level was higher than the level calculated according to the method described
in the recommendation ITU 1546. In this case the differences were from several to
dozen dBs. It proves, yet again, that for the land-sea paths in the 900 MHz band,
the discussed model is slightly underestimated.
To make the comparison more reliable it would be advisable to assess and
consider the type of the environment in which BTSs are located. This issue is addressed in the authors’ current research activities.
Acknowledgment
The measurement results utilised in this paper have been obtained as part
of the EfficienSea project, partially financed by the EU Baltic Sea Region
2007-2013 Programme.
References
[1] ITU-R, Method for Point-to-Area Predictions for Terrestrial Services in the Frequency
Range 30 MHz to 3000 MHz, Recommendation ITU-R P.1546-4, Geneva, 10/2009.
[2] R. Niski, K. Bronk, J. Stefański, Opracowanie narzędzia programowego do
prognozowania zasięgów stacji brzegowych dla potrzeb radiokomunikacji morskiej
(nr 08300047) Instytut Łączności – Państwowy Instytut Badawczy, 2007.
[3] R. Niski, J. Żurek, New empirical model of propagation path loss for the Baltic Sea
in the marine VHF frequency band (Polish Journal of Environmental Studies) HARD,
Olsztyn 2007, no. 4B, vol. 16, pp. 143-145.
[4] R. Niski, J. Żurek, Empiryczna weryfikacja modeli propagacyjnych w strefie
przybrzeżnej Morza Bałtyckiego (Zeszyty Naukowe Wydziału Elektroniki,
Telekomunikacji i Informatyki Politechniki Gdańskiej. Radiokomunikacja, Radiofonia,
Telewizja) Politechnika Gdańska, Gdańsk 2007, nr 1, s. 117-120.
[5] Communication for e-Navigation – results of the tests and measurements, E-Navigation
Underway 2012 Conference, 18-20 January 2012, Copenhagern – Oslo – Copenhagen.
[6] A. Lipka, K. Bronk, R. Niski, Measurement campaign at the Baltic Sea, 2012,
EfficienSea report.
Chapter 7
Mobile Ad-hoc
and Wireless Sensor Networks
Algorithms for Channel and Power Allocation
in Clustered Ad hoc Networks
Luca Rose1, Christophe J. Le Martret1, Mérouane Debbah2
1
Thales Communications and Security, France,
{luca.rose, christophe.le_martret}@thalesgroup.com
2
Alcatel – Lucent Chair in Flexible Radio, Supelec, France,
[email protected]
Abstract: In the context of mobile clustered ad hoc networks, this paper proposes and studies a self-configuring algorithm which is able to jointly set the channel frequency and power level of the transmitting nodes, by exploiting one bit of feedback per receiver. This algorithm is based upon a learning
algorithm, namely trial and error, that is cast into a game theoretical framework in order to study its
theoretical performance. We consider two different feedback solutions, one based on the SINR level
estimation, and one based on the outcome of a CRC check. We analytically prove that this algorithm
selects a suitable configuration for the network, and analyse its performance through numerical
simulations under various scenarios.
I. Introduction
In recent times, the interest for technological solutions which allow communications to happen in difficult conditions, e.g. without the aid of a central controller,
has gained much momentum. The development of cognitive radios (CR), devices
able to sense their environment and to modify their configuration in accordance,
has made this a reality.
On operational theatres, the presence of a fixed central controller infrastructure,
for instance a base station, configuring the whole network is difficult to implement
and is not desirable for the weakness it presents against potential enemies. Moreover,
one can expect future equipments on the battlefield to be able to exploit the free
spectrum to communicate and to keep their transmit power as low as possible.
The goal is both minimizing their spatial frequency footprint, avoiding to pollute
transceivers from other networks, and reducing the battery drain while achieving
a certain Quality of Service (QoS). The concept of cognitive, self-configuring ad hoc
network, thus, is a candidate solution to all of the above challenges.
In our work, we consider clustered ad hoc networks where the nodes are grouped
into subsets (clusters), each of which is led by a cluster head (CH). We assume
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Military Communications and Information Technology...
that all the clusters share the same frequency band, each CH being in charge of allocating sub-channels of the common resource to the multiple transmitter-receiver
links that need to be operated within its cluster.
The CH, basically, fulfils two purposes: (i ) it selects a frequency-channel and
a power level to be employed by the devices within its control zone, (ii ) it manages
the intra-cluster communication by allocating logical sub-channels to each link.
Thus we can consider our system as locally centralized, and globally distributed.
In order to do so, we assume that the CH only relies on local information, without
any form of cooperation or explicit coordination with the other CHs. This reduces
the amount of signalling demanded and makes the network more resistant to jamming attacks. For the same reasons, we need to minimize the amount of feedback
between the CH and the nodes under its control.
The closest works to ours are [1-4]. In [1] an algorithm for interference avoidance is presented assuming an underlying clustered ad hoc network. The algorithm
sets the frequency channel, leaving to the CH the duty to choose the power based
on the needs of the cluster’s devices. The authors assume the clusters to be far
apart from each other in such a way that the interference created form one cluster
to another does not depend on the actual transmitters location. In [2], authors
consider and present a trial and error (TE) algorithm, and analytically study its
convergence properties. There, the scenario under analysis is composed of a group
of communicating links, without considering the structure of a clustered network.
In [3], authors suggest the use of iterative water filling (IWF) to allocate sub-channels
and power in order to achieve a certain QoS, measured in terms of achievable rate.
The authors assume a system with low interference, i.e., interferers very distant
from each other, such that the convergence of the IWF could be insured. In [4],
authors consider a clustered network where, in each cluster, a single transmitter
broadcasts to the other nodes. In this work, each transmitter allocates its power
using an IWF strategy aiming at maximizing the weighted mean of the throughputs.
In a clustered network with many transmitters and only one decision maker, it is
not practical to implement such a water-filling strategy. Indeed, this would require
all the receivers to feedback to the decision maker their channel state information.
Thus, this strategy requires a large amount of signalling to allow the CH to evaluate
the correct power allocation. Moreover, there exists a sufficient literature, e.g. [4-6]
showing that, in decentralized networks, the operating point achieved through
IWF is often less efficient than the one achieved through spectrum segregation,
i.e. forcing each link to operate only on a small fraction of the available bandwidth.
In our paper, we present and detail an algorithm which, when employed by all
the CHs, is able to set the network channel and power configuration by exploiting
the information of only one bit feedback per receiver. This algorithm, namely trial
and error learning algorithm, has been studied in [2] under the assumption of a static
scenario (i.e., time invariant channel, fixed power gains and network topology),
with the transceivers aiming at achieving a certain SINR to fulfil a given QoS.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
231
In this paper, we study several scenarios taking into consideration cluster
mobility as well as more realistic communication performance metrics. We show
the capability of the proposed algorithm to statistically steer the network into a state
where clusters next to each other employ different channels.
Therefore, the main contributions of this paper are the following: (i ) we detail
a self configuring algorithm by defining all its parameters; (ii ) we study its behaviour under several scenarios; (iii ) we compare two ways of measuring transmission success, either by comparing the estimated SINR to a target or by considering
packet integrity through cyclic redundancy check (CRC) of the transmitted packet;
(iv ) through numerical simulations, we estimate the optimal number of spectral
resources, (i.e., channels) the network should be providing for the algorithm to well
perform.
The paper is organized as follows. In Sec. II we present the general model of an ad hoc network and provide its associated game-theoretical model in Sec. III. In Sec.
IV we briefly describe the resource allocation algorithm and we show the test bench
scenarios in Sec. V providing the results of the experiment in Sec. VI. Finally, we
conclude our work in Sec. VII.
II. System model
In this work, we consider a network populated with
of which composed by
clusters, each
K
links (transmitter-receiver pairs), with N N  N k .
k
Let
 1,2,, K  indicates the set of clusters and
k
 ,  ,,   the set
k
1
k
2
k
Nk
of links within an arbitrary cluster . The nodes communicate by sharing a common spectrum, thus creating mutual interference. The overall spectrum is divided
into
channels, and we denote by
 1,2,,C  the set of available channels.
Each cluster, say , is managed by its CH, which selects its transmission setting,
i.e., a channel  and a power level , to be used by all the devices belonging
to the cluster. The power level
is chosen among a finite set of possible power
levels  0,, PMAX , where PMAX is the maximum amount of power that can be
used by a transmitter device. The CH divides the selected channel, , into N sc
orthogonal logical sub-channels and assigns them to the links to avoid intra-cluster
interference. Assuming a time division multiple access scheme (slotted frame),
each CH allocates to each link a set of sub-channels per slot, as depicted in Fig. 1.
We define by
the set of sub-channels allocated to link and by an arbitrary
element of . In every cluster we also assume that the transmit power on each
sub-channel is constant for all the links.
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Military Communications and Information Technology...
Figure 1. Sub-channel assignment instance. Each different colour corresponds to a different link
We consider flat and block fading channels, i.e., channels power gain is both
time and frequency invariant for the whole duration of one transmission. As such,
the level of multiple access interference (MAI) in each sub-channel suffered by
a receiving node, for instance the receiving node of link , on the sub-channel
is given by the sum of the interference created by all the transmitters which employ
the same sub-channels at the same time, that is:
N
MAI ( k ,s )   1c c   x p x g (l ,  km )1st s. (1)
k
x
m

N sc
x k
l x
In (1), g (l ,  km ) indicates the channel power gain between the transmitting
node of link and the receiving node of link
, and 1  is the indicator function. Therefore, the level of the SINR experienced by the receiver of link
on
sub-channel is given by:
SINR ( k ,s ) 
where g (  km ,  km ) indicates link
model [7], i.e.
m
N k pk g (  km ,  km )
, (2)
N sc  2  MAI ( k ,s )
m
power gain, which is modelled by the two-ray
k
m
j
l
g ( ,  ) 
In (3),
tennas of nodes
and
and
Gk G j h2k h2j
m
d
m
l
4
( km , lj )
l
. (3)
represent the antenna gains,
,
the height of the an-
respectively, and d ( k , j ) is the distance between the two
m
l
nodes. In order to study the performance of the network, we assume the queue of each
transmitter to be not empty, i.e., we analyse the system in a fully loaded situation.
For the sake of simplicity, we consider an uncoded binary phase shift keying
(BPSK) modulation scheme for each sub-channel transmission. Since the transmitters may use multiple sub-channels per link to perform their communication, we
introduce an equivalent SINR that accounts for all the sub-channels in order to
assess the link performance. We define our equivalent SINR based on a bit error
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
233
rate (BER) point of view. Here, we consider the interference as Gaussian noise, thus,
the equivalent SINR may be expressed, by applying uncoded BPSK BER formula, as:
N

k
, (4)
SINR eq (  km )  erfc1
erfc
SINR

k
(  m ,s ) 
N
sc s


where erfc is the complementary error function.


III. Game formulation
In this section, we model the scenario presented in Sec. II under a normalform formulation [8].
A. Normal-form
A game in a normal-form is defined by a triplet:


  ,  , uk k (5)
where, represents the set of players,   1  2 ... K is the joint set of actions with      , i.e., ak  ( pk , ck ) . Since the utility is a measure of the individual quality of the chosen action, its formulation strongly depends on the type
of feedback chosen. Here, we formulate our utility function as

1 
1 pk    Feedback x ( a), (6)
uk ( a)


1 N k 
 PMAX

x  k
where Feedback x ( a) is a one bit value, which depends on the nature of the feedback
chosen in the network, as described in the following section. This utility function is chosen to be monotonically decreasing with the power consumption
and increasing with the number of successful transmission Feedback x ( a). The parameter tunes the interest we have in satisfying the constraints over the power
consumption.
Definition 1. (Interdependent game). The game is said to be interdependent
if for every not empty subset
 and every action profile a  ( a , a )
it holds that:
i   , a '   a : ui ( a '  , a )  ui ( a , a ). (7)
In the following, we assume that game is interdependent. This is a reasonable assumption, since, physically, this means that no cluster is electromagnetically
isolated. Under a normal-form formulation, the solution concept used is the Nash
equilibrium (NE), which we define as follows:
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Definition 2. (Nash equilibrium in pure strategies). An action profile
is a NE of game if  and a k  k

uk ( ak* , a* k )  uk ( a ' k , a* k ). (8)
Generally speaking, a game can have an arbitrary number of NE, thus, to
measure the efficiency of each one, we introduce the social welfare function, defined
K
by the sum of all individual utilities: W ( a)  uk ( a) .
k
B. QoS and feedback strategies
In this work, we express the QoS constraints in terms of SINR, which means
that we fix a given SINR target for each link. For simplicity sake, this value will be
assumed here constant, i.e. equal to , for all links. As explained in the previous
section, the utility function design (6) allows the system to take these constraints
into account.
We discuss now two different feedback strategies that can be applied in real
systems.
1) SINR-based feedback:
This is the first strategy that naturally arises, given that the QoS is expressed
in terms of SINR. Most of communication systems estimate the received SNR
based on pilot sequences, and thus the SINR when MAI is present. Relying on this
capability, we define the feedback as:
Feedback x a   1SINR a. (9)
This formulation was proposed and studied in [9]. There, authors proved that
with a utility function such as (6), the action profile which maximizes the social
welfare is the one which (i ) maximizes the number of links which simultaneously
satisfy the SINR condition, (ii ) minimizes the network power consumption. Tuning the parameter
allows to favour either the QoS constraints satisfaction for
large values, or the consumed power for small values.
x
2) CRC-based feedback:
Usually, communication systems implement a CRC to check the integrity
of the received packets. From this information, it is thus possible to infer the quality
of the communication link, and this allows us to consider another kind of feedback
defined as:
Feedback x ( a)  1CRC ( a )0. (10)
Here, the receivers feedback a 1 if the packet is received without errors and
a 0 otherwise. Note that, in this case the result in [9] does not apply, especially since
x
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
235
the CRC is a stochastic function of the action profile . In this case, the theoretical
framework is not able to predict the exact point of convergence of the algorithm.
However, simulation results, illustrated in Sec. VI, indicate that this way of evaluating the feedback results in better performance.
IV. Trial and error
In this section, we briefly summarize the TE algorithm, introduced in [10], [11],
and applied to wireless networks in [2]. TE is a state machine which selects, in a fully
decentralized way, a strategy for a player such that, when every player is using
the same scheme, the system is at an optimal NE a large proportion of the time
with high probability. A state of a player is defined as a triplet zk  ( mk , ak , uk ),
where mk , ak , uk represent, respectively, the mood, the benchmark action and
the benchmark utility of player . There are four possible moods, each implying
a different behaviour and depending on different responses by the network.
• Content
If player is content, then it plays action ak with probability (1 ) , and
another action (chosen randomly according to some probability distribution)
with probability
Here, 0   1, namely the experimentation probability,
0.02
is a parameter of the system. Numerical simulations suggest 
as a value
K
with a good trade off between stability and experimentation. At each iteration,
each player compares the actual utility
with the benchmark utility uk . There
are four possible outcomes: (i ) if uk  uk , and it did not experiment, i.e., ak  ak ,
player mood becomes hopeful, (ii ) if uk  uk , and it experimented, i.e., ak  ak ,
then, with probability ( F ( u ( a )u )) ,
becomes the new benchmark action, and
the new benchmark utility; (iii ) if uk  uk and ak  bar ak then the player mood
turns to watchful; (iv ) if uk  uk and ak  ak , then nothing changes. Here F () ,
is a non increasing function as explained in [11].
k
k
• Hopeful
If player is hopeful it evaluates its utility
and compares it with the benchmark utility uk . If uk  uk , then the player mood becomes content and the benchmark
becomes the new benchmark utility. If uk  ruk , then the player becomes
watchful.
• Watchful
If player is watchful it evaluates its utility
and compares it with the benchmark utility uk . If uk  uk , then the player mood becomes discontent. If uk  uk ,
then the player becomes hopeful.
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• Discontent
If player is discontent, it experiments a random action
and evaluates its
corresponding utility . Then, with probability G ( u ) the player mood becomes
content, with
and
as new benchmark action and utility. Here, G (), is a non
increasing function as explained in [11].
k
A. Trial and error properties
The theoretical properties of TE have been thoroughly analysed in precedent
works. In this section, we report two among the most relevant results with our
notations.
Theorem 1. Let be an interdependent game, and let it have at least one NE
and let each player employ TE, then a NE that maximizes the social welfare among
all equilibrium states is played a large proportion of the time.
This theorem, shown in [11], states that the algorithm does not only look for
individual optimality (the NE) but, among the states individually optimal, it searches
the one which maximizes the global outcome.
Theorem 2. Let   and let game be interdependent with at least one NE.
Then, TE converges to the NE where the number of links satisfied is maximized and
the power employed to obtain this result is minimized.
This result, proven in [9], shows that TE is able to select among all the possibilities an optimal working point for the network under analysis, at least for a large
proportion of the time.
V. Scenario description
The scope of this section is to present and describe the scenarios used to
run the simulations and study the performance of TE. First, we consider a static
dense scenario. Second, we consider a mobile scenario with one cluster moving
around four static clusters. We aim at illustrating that TE is suitable for configuring networks even in mobility, where channels are, thus, no more time-invariant.
In the following, we set   K 1 , to comply with the conditions in Theorem 2.
A. Static scenario
In this scenario, we consider a square field of 5 km per side populated with
5
K  16 equally dimensioned square clusters, each of which has a side of km.
4
In each cluster, 8 nodes are randomly positioned as in Fig. 2. The clusters are
not overlapping, the nodes belonging to each cluster are coloured with different
colours, and the role (transmitter or receiver) is decided once and for all. In this
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
237
scenario, each cluster has N sc  sub-channels, which are randomly associated
with the links. This means that, between two TE loops there will be three time slots,
N
and three feedbacks. For each of these packets sc  2 sub-channels are randomly
Nk
assigned for each link.
Figure 2. Square scenario setting with K = 16 clusters and N k = 4 pairs. Clusters and nodes
are static with SINR-based feedback. CH, AVG PW, and AVG SAT indicate respectively the most
frequently selected channel, the APC and the AS.
B. Mobility scenario
In this scenario, we evaluate the performance of TE in the presence of a moving cluster. We assume
clusters to be aligned and sharing the spectrum
while a fifth cluster is far enough to be creating little interference. An instance
of this starting situation is depicted in Fig. 3. In this case the topology is such that,
between the four static clusters, there exists an empty space for the fifth cluster to
pass. Therefore, when all the five clusters are aligned, no cluster is overlapping with
another. This happens after around 2250 iterations. Later, the cluster in mobility
reaches the end of the field after 3000 iterations. Here, the number of available channels is restricted to
.
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Figure 3. Cluster positions at the beginning of the mobility scenario with K=5 clusters in a field
of 1 km side. Four clusters are static and aligned, the cluster at the bottom is the one in mobility.
VI. Simulation results
In this section, we evaluate the performance of the TE for the scenarios
introduced in Sec. II according to some metrics defined in the following section.
A. Performance metrics
In order to evaluate the performance and the behaviour of the proposed
algorithm, we have selected the following metrics:
• Average satisfaction (AS): defined as the average number of positive feedbacks the receivers send to their CH, for each iteration of the TE. It evaluates how much the algorithm enables to satisfy the criterion selected by
the feedback (either SINR or CRC).
• Average power consumption (APC): defined as the average amount of power
used by the transmitters in a cluster to achieve the corresponding satisfaction level. It captures how much power is consumed per cluster.
• Packet error rate (PER): defined as the average dropped packets, it helps
evaluating the link quality and thus if the algorithm is correctly configuring
the network.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
239
• Channel switch per iteration (CSpI): defined as the average number of channels that have changed for each TE iteration and thus captures the channel
allocation stability.
B. Static scenario, SINR-based feedback
In this section, we analyse the performance of TE, in terms of satisfaction and
power consumption, applied to the square scenario described in Sec. V-A. Here,
receivers feedback their satisfaction based on the comparison between the received
SINR and the threshold , fixed in the simulation equal to 10 dB.
In Fig. 4, we plot AS in the network and the APC by the nodes as a function
of the iteration number. As we can see, full satisfaction is not reached. This is due
to the scarcity of resources in the network that does not permit full satisfaction.
This can be understood intuitively since, in a network with K  16 clusters sharing
channels, each cluster has on the average two neighbour clusters which
employ the same channel.
Figure 4. Achieved AS and APC as a function of the TE iterations for a square static scenario,
with SINR-based feedback
In Fig. 2, we show the node localizations on the field and the corresponding
links with the AS and APC along with the most often chosen channel for each
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cluster. Note that having the same channel as the most used ones does not imply
a collision, since the channels might be used in different time slots. On the contrary, having two different channels as the most used one implies no interference
for a large part of the simulation.
C. Mobility scenario
In this simulation, we refer to the scenario presented in Sec. V-B. First we
consider the case where receivers feedback their satisfaction based on the comparison between the received SINR and the threshold  10 dB. Then, we consider
the case where receivers send a CRC-based feedback.
In Fig. 5, we plot the global performance of the system in terms of AS and APC.
It is possible to see the drop down of the system performance after 2000 iterations.
The algorithm reacts by increasing the power level and by modifying the channel
configuration. The satisfaction level, then, increases when the algorithm rearranges
the channel and power allocation scheme in order to suit the new topology. Note
that, when the mutual interference is too high, TE turns off one cluster by selecting
zero power. The rationale behind this is that, if the desired level of SINR is not reachable by the current topological configuration, then the algorithm prefers to stop one
of the clusters to improve the individual utility. When the algorithm reaches a different
channel assignation pattern it is, again, possible to achieve a higher level of satisfaction.
Figure 5. Achieved AS and APC as a function of the TE iterations for a square static scenario,
with SINR-based feedback
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
241
In Fig. 6, we plot the AS and APC in a similar scenario, where the feedback is based on the evaluation of a CRC over a packet of 256 bytes. Note that,
here, the reaction to the approach of the moving cluster appears to be a sudden
increment in the power level. The power level increment is larger then when
using an SINR-based feedback. Intuitively, this is due to the fact that the CRC
test is more tolerant on the SINR decrement than the SINR test. Therefore,
the transmission power increment is more effective to insure the compliance
with the constraints when considering a CRC-based feedback than when considering a SINR-based one.
Figure 6. Achieved AS and APC as a function of the TE iterations for a the mobility scenario,
with SINR-based feedback
In Fig. 7 we plot a summary of the simulation run. Here each colour represents
one of the possible two channels, while the height of the bins represents the used
power. The static clusters are indexed with numbers 1, 2, 4, and 5 and the moving
cluster is indexed with the number 3. When the system reaches time instant (i )
the 3rd cluster is close enough to create interference to the other clusters. This forces
the system to reorganize the power-channel pattern. When the moving cluster
is completely aligned with the others (ii ) the system starts working in an orthogonal way and the power starts decreasing. At (iii ) the cluster is far enough to stop
creating interference.
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Figure 7. Channel-power allocation as a function of the TE iterations for the mobility scenario with
two channels. Each colour represents a different channel, and the heights of the graph the transmit power level. Clusters 1, 2, 4, 5 are static, cluster 3 is in mobility. (i) beginning of the interference from
the 3rd cluster, (ii) Five clusters are aligned, (iii) end of interference from the 3rd cluster. The blue
solid lines represent PMAX = 50 W.
D. Static scenario, CRC-based feedback
In this section, we analyse the performance, in terms of satisfaction and power
consumption when the TE is applied to the square scenario described in Sec. V-A.
We recall that, in the following graphs, the upper curve measures the AS, where
the feedbacks are calculated with a CRC on the received packets. We recall that in this
simulation each packet is considered to be 256 bytes long. In Fig. 8, the performance
of such a system is summarized. The upper curve represents the AS reached in the network, while the lower curve represents the APC. Note that, it is not possible to directly
deduce the PER from the satisfaction. Especially, low levels of AS do not automatically
translate into high levels of PER. This is because, when the transmitter is employing
zero power, which may happen especially if the satisfaction level is low, the feedback
is zero, but it cannot be considered as an unsuccessful transmission. Therefore, to
evaluate the PER, we need to reduce the level of no-satisfaction by the amount
of time the transmitters were using zero power. On the other hand, a high level
of AS, can guarantee a high number of packets received correctly, which translates
in a low PER. In this system, simulation results indicate an average PER = 2.8 10-3.
Note that, when we employ an SINR-based feedback, we obtain PER = 0.23, which
is much higher for equivalent average employed power.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
243
Figure 8. AS and APC as a function of the TE iterations for a square static scenario,
with CRC-based feedback
In Fig. 9, the performance of the algorithm on a single node is reported. It is
possible to see that, generally, most of the transmitted packets are correctly received.
Moreover, it appears that packets errors increase during some particular time windows, i.e., errors appear in burst. This is probably due to a change in the network
(for instance another cluster starts employing the same channel) which makes
the power-channel pair chosen by the CH inappropriate for the transmission.
Figure 9. Fraction of packet correctly received. Single node CRC outcome for scenario
V-A CRC-based feedback
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E. Channel switch per second
The stability of a network configuration is an important parameter to evaluate
the performance of a self configuring algorithm. TE attempts to steer the network
to a NE, which is inherently stable point. Nonetheless, the stochastic nature of TE,
the incompleteness of the information and the lack of CH cooperation leave
space for interference and collisions. To evaluate this instability we have defined
the CSpI metric in Sec. VI-A. To compute it, we run 20 simulations on the scenario
described in Sec. V-A and we count the number of time a CH switches its channel. We performed this evaluation both in the case of a SINR-based feedback and
of a CRC-based feedback and found CSpISINR = 4.5 10-3 and CSpICRC = 4.3 10-3.
As we can see the results are very close one to each other. This is due to the fact that
avoiding other clusters interference is important independently from the nature
of the feedback. As a consequence, in both cases clusters try to employ good (low
interference) channels.
F. Average satisfaction versus available channels
Here, we aim at evaluating the variation of TE’s performance as a function
of the available channels. In this simulation we use the scenario depicted in Sec. V-A,
where we set a CRC-based feedback. In this scenario, we have K  16 clusters and
we vary the number of available channels for the network from 4 to 18. For each
Figure 10. Expected satisfaction versus available channels. This plot has been realized assuming
a square field as the one described in V-A, assuming a SINR-based feedback
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
245
of this values, we run 20 tests, each of which lasts 6000 TE iterations. We recall
that, three packets are sent for each iteration, and each packet has 256 bytes length.
The result is depicted in Fig. 10. It is possible to see that the curve does not reach
the full satisfaction. This is due to the stochastic nature of the algorithm. Since
clusters are experimenting, and the CH have no way of cooperating one with each
other, a certain, even if low, level of unsatisfaction is unavoidable. From these
results, it appears that the optimum number of channels should be 10. Here, we
mean optimum as the minimum number of channels needed to keep the network
satisfied at least 90% of the time.
VII. Conclusion
In this paper, we have presented and studied the performance of a resource
allocation algorithm, namely the trial and error (TE) learning algorithm. We have
shown that it is effectively capable of setting the transmission parameters (channel
and power) of clustered ad hoc network, using only one bit feedback per receiver.
This feedback must be an evaluation of the quality of the transmission link. In our
settings, we have proposed two different types of feedback strategies: one based
upon the measurement of the SINR at the receiver, the other reporting the CRC
check status of the transmitted packet over the link.
In a crowded network, when several clusters try to share a few spectral resources, TE is able to find a setting such that the largest part of the cluster fulfils its
QoS constraints, employing a low level of power. The clusters which are not able to
fulfil their QoS constraints are automatically turned off, saving battery power and
avoiding useless interference.
When clusters are moving, the changes in the topology force the algorithm to
react quickly and to find a different channel and power allocation scheme, such as to
satisfy the new conditions. This may be done by a temporary increase in the power
level, or by a reorganization of the channel assignment.
Several paths could be followed to extend this contribution. Experimentation
parameters which adapt on the satisfaction levels, for instance, could be used to let
the algorithm discriminate between almost static or high mobility situations. Moreover, a study on a more effective probability distribution for the experimentation,
and on the effect of the values of the parameters and could bring insight on
ways to improve the performance. Finally, the case when an action profile depends
upon stochastic parameters would need to be investigated to study convergence
properties of the game when CRC-based feedback is used.
VIII. Acknowledgment
This research work was carried out in the framework of the CORASMA EDA
Project B-0781-IAP4-GC.
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References
[1] B. Babadi and V. Tarokh, “GADIA: A greedy asynchronous distributed interference
avoidance algorithm,” IEEE Transaction on Information Theory, vol. 56, pp. 6228-6252,
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[2] L. Rose, S.M. Perlaza, M. Debbah, and C.L. Martret, “Distributed power
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Papers 480, 2010.
High Spatial-Reuse Distributed Slot Assignment
Protocol for Wireless Ad-hoc Networks
Muhammad Hafeez Chaudhary1, Bart Scheers2
1
Royal Military Academy, Belgium, [email protected]
Royal Military Academy, Belgium, [email protected]
2
Abstract: Application of ad hoc networks in mission-critical environments requires wireless connectivity that meets certain quality-of-service (QoS). In such networks mechanism to control access to
the shared wireless channel is crucial to ensure efficient channel utilization and to provide the QoS.
TDMA based MAC protocols are considered to be appropriate for this kind of applications; however,
finding an efficient and distributed slot assignment protocol is crucial. In this paper, a distributed
slot assignment protocol is developed which gives high spatial reuse of the channel. To assign slots,
the protocol does not need global topology information: Each node assigns slots based on the local
topology information. The protocol can find the conflict-free slot assignment with limited message
overhead. We evaluate the performance of the proposed protocol and show that the protocol gives
considerably better channel utilization efficiency than exiting distributed slot assignment protocols.
I. Introduction
The last decade has seen an explosive growth in applications of wireless communications and networking technology bringing ubiquitous mobile service into
the everyday realm. A recent forecast by CISCO suggests that during the current
year there will be more wirelessly connected devices than the total human population. Moreover, the demand for high-speed wireless data transfer is increasing at
astronomical rates. The phenomenal increase in the wirelessly connected devices
and the applications running on them have put an extremely high premium on
the communications spectrum, and thus placing great demand on designing spectrum efficient communication and networking protocols to meet the requirements
of the current and emerging applications in wireless networking. Currently, a key
area of research is mobile ad hoc networking, which is driven by the requirement
of having a technology that enables a disparate set of mobile devices/nodes create
a network on demand, as the need arises, to accomplish an assigned mission.
In a wireless network, simultaneous transmissions of two or more nodes
in the same channel may not be successful if their intended receivers are in the radio
interference range of more than one transmitter. A mechanism to control access to
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the shared wireless channel is crucial to ensure efficient channel utilization and to
provide quality-of-service (QoS). Ad hoc networks for mission-critical applications
and emergency response services require that the data be delivered to the destination
node reliably and within certain time limits. To support such QoS, schedule based
medium access control protocols using TDMA scheme are deemed suitable [1].
There are numerous algorithms dealing with TDMA slot scheduling in ad hoc
networks, whereby nodes can find conflict-free slot assignment. In [1], a TDMA
slot scheduling protocol named GinMAC is presented; the protocol requires
the network be arranged in a tree-like structure. Building on the GinMAC, in [2]
the BurstProbe slot assignment protocol is proposed. These slot scheduling protocols require global topology information which may not be available in ad hoc
networks that are inherently bereft of any central coordinating and controlling
infrastructure. In [3, 4] a slot assignment protocol called USAP is proposed which
allows nodes to get conflict-free slot assignment in a distributed way using local
topology information. Kanzaki and his colleagues proposed a protocol named
ASAP in [5, 6] which can be viewed as an extension of the USAP, adding details
on dynamic frame-size selection and more detailed procedures about the nodes
joining/leaving the network. Another related protocol named DRAND is proposed
in [7], and later on extended to Z-MAC [8] which combines artifacts of both TDMA
and CSMA medium access schemes. The main focus of these protocols (and others like in [9, 10]) is to find conflict-free slot assignment to nodes in a distributed
way. The maximization of the channel utilization efficiency (i.e., the spatial reuse
of the slots) is not explicitly considered. Thus from channel utilization viewpoint,
these protocols may give suboptimal performance.
In this paper, we propose a high spatial-reuse distributed slot assignment protocol (HUDSAP). In the protocol, the nodes find slot assignment using their local
topology information. The protocol introduces a priority mechanism by which
nodes having higher number of one-hop neighbors (NoNs) assign slots first. Each
node computes its priority index independently using only the information form
the nodes within its contention zone. We show that the protocol achieves substantially higher channel utilization efficiency than the DRAND and related protocols.
The remainder of the paper is organized as follows: Section II gives preliminaries on slot assignment and the problem formulation; Section III presents details
of the proposed slot assignment protocol; Section IV outlines an adaptive frame
length selection scheme; Section V illustrates the performance of the protocol by
simulation examples; and finally Section VI gives some concluding remarks.
II. Preliminaries and problem formulation
For the slot assignment we represent the network by a graph G =(ϑ, ξ), where
ϑ is the set of vertices that correspond to the nodes in the network and ξ is the set
of edges representing the wireless links between the nodes. We assume that for any
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
249
two distinct vertices i, j Î ϑ, an edge (i, j) exits in ξ if and only if i and j can hear
each other – that is, all edges are bidirectional.
Given a graphical representation of the network, the TDMA slot assignment
problem with the associated assignment constraints can be defined as an equivalent
graph coloring optimization problem. The equivalence between the two problems
is one-to-one; that is, two nodes receive different slots if and only if the corresponding
vertices have received different colors. Moreover, the total number of slots is equal
to the total number of colors used.
The objective of the slot assignment optimization problem is to minimize
the number of slots used to find the transmission schedule for all nodes in the network under the constraint that a node can only assign a slot that has not been used
within the contention zone of the node. The contention zone of a node is assumed to
be limited to its two-hop neighbors. As is often assumed in designing MAC protocols,
such a definition of the contention zone is imposed to remove the hidden-terminal
problem. The hidden-terminal problem arises when two nodes cannot hear transmissions of each other but a third node can hear transmissions of both of them.
An optimal solution for slot scheduling problem is known to be NP-complete [11, 12]: That is, the computational complexity of finding the solution increases
exponentially as the number of nodes increases; which means it becomes prohibitively time consuming to find the optimal slot schedule. That is why to solve the slot
assignment problem, in literature heuristic-based suboptimal solutions are proposed
that vary in their schedule length, the convergence time, and the message overhead.
To this end, in [13], three greedy heuristic-based slot assignment procedures are
proposed: namely, the RAND (random), the MNF (minimum neighbor first),
and the PMNF (progressive minimum neighbor first), listed in increasing order
of complexity. The basic principle underlying each of these schemes is essentially
the following: first, give a unique label to each node, and then assign slots to nodes
in decreasing order of their labels. In RAND, the nodes are labeled in a random
way; in MNF, the node with minimum number of neighbors is labeled first; and
in PMNF, the nodes are labeled as in MNF with a difference that after labeling
a node, the node and its edges are removed. Effectively that means, at each step
among the nodes that have not been assigned slot yet, the RAND takes a node at
random and allots time slot to it; the MNF takes the node with maximum number of neighbors and allots slot to it; and the PMNF first removes the nodes and
the associated edges that have already been assigned slots, then within the updated
network assign slot to the node with maximum number of neighbors.
It has been shown in [13] that the schedule length (SL) achieved with the three
schemes is in the order SLPMNF ≤ SLMNF ≤ SLRAND. That is, from the spatial reuse
point of view, the PMNF is the most efficient and the RAND the least efficient
among the three labeling schemes. However, the problem with these schemes
is that they require knowledge of the global network topology; that is, they are
centralized schemes. For ad hoc networks, distributed slot assignment schemes
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are sought because such networks are devoid of any central coordinating and
controlling infrastructure and the global topology knowledge is hard to come by
at individual nodes.
Recently a distributed implementation of RAND, known as DRAND, is proposed in [7] which can achieve the same channel utilization efficiency as the RAND.
The RAND ordering can be viewed as archetype of the channel scheduling in wireless
networks. The roots of many heuristics for slot scheduling algorithms in the literature can be found to be equivalent to the RAND [3, 5, 7, 8, 14]. The appeal
of RAND lies in its simplicity and the ease with which its distributed version
can be implemented. However, as shown in [13], the performance (in terms of SL)
of the RAND can be significantly inferior to the MNF and PMNF. In this work, we
present a distributed implementation of MNF, which we call as the HUDSAP that
can achieve same channel utilization efficiency as the MNF by using only the local
topology knowledge at individual nodes. Towards this end, the ensuing section
gives details of the proposed protocol.
III. Slot assignment protocol
We assume that the time is divided into slots and the nodes are synchronized
on the slot boundaries. The protocol operates in two main phases: the neighborhood discovery phase and the slot assignment phase. Fig. 1 shows the state diagram
of the protocol, where the slot assignments phase is divided into four states: node classification, waiting slot assignment, active slot assignment, and completed slot assignment.
In the neighborhood discovery phase, a node collects information about the nodes
within its two-hop neighborhood, that is, one-hop neighbors (ONs) and two-hop
neighbors (TNs), the NoNs of these nodes, and their assigned slots. The two-hop
neighbors are strict two-hop neighbors, that is, it excludes the one-hop neighbors
that can also be reached by another one-hop node. Based on that, the node constructs
neighbor information table (NiT), an example of which is shown in Fig. 2. In the slot
assignment phase, the nodes assign slots in a distributed way as explained next.
Each node compares its NoNs with the NoNs of the nodes, in its NiT, which
have not yet assigned slots, and based on that, the node classifies itself in one
of the following three groups:
1.In node group I (NG-I) if the NoNs of the node is greater than the NoNs
of all nodes in its NiT that have not yet assigned slots; for instance, in Fig. 2
initially nodes e and p will place themselves in this group.
2. In NG-II if the NoNs of the node is equal to the NoNs of some (one or
more) nodes in its NiT that have not yet assigned slots; for example, in Fig. 2,
node b and l will initially place themselves in this node group.
3. In NG-III if the NoNs of the node is less than the NoNs of some (one or more)
nodes in its NiT that have not yet assigned slots; for instance, in Fig. 2, all nodes
will initially place themselves in this node group except nodes b, e, l, and p.
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251
Figure 1. State transition diagram of the HUDSAP
The classification at each node is done independently solely based on the local topology information available at the node in the form of NiT. After the classification, the node assigns slot to itself and the procedure of which depends on
its node group. The slot assignment is managed differently in the three groups,
as shall be explained in the ensuing sections. During the slot assignment phase,
following control messages are exchanged between the nodes: slot assignment
request (SAR), slot assignment grant (SAG), slot assignment confirmation (SAC),
slot assignment denial (SAD), and slot assignment failure (SAF). The SAR, SAF,
and SAC messages are transmitted by the node that attempts to assign a slot, and
the SAG and SAD are response messages to the SAR message. These response
messages are transmitted by the nodes within the contention area of the node that
has sent out the SAR message.
A. Slot assignment in NG-I
All nodes in this group assign the first free slot that has not yet been assigned
to any node within their two-hop neighborhood. For the network of Fig. 2, initially the nodes e and p are in this group and there is no slot assigned to any node
in the network; so both of these nodes assign slot 0 to themselves. After assigning
the slot, the nodes announce their slot assignment to their neighbors by sending
the SAC message as shown in Fig. 3. In this case the nodes do not have to wait for
any confirmation from their one-hop and two-hop neighbors about their slot
assignment, because the nodes in this group are sure that there is no other node
within their contention area currently assigning slot until their slot assignment
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is complete; the other nodes in the contention area are prohibited from initializing
slot assignment by the slot assignment procedures for NG-II and NG-III, as we
shall see in the ensuing sections. Each one-hop neighbor after receiving the SAC
announcement does the following things: updates its slot assignment information,
forwards the SAC to its ONs, removes the node which transmitted the announcement from its NiT for further consideration during the classification step, and
changes its state accordingly as shown in Fig. 1. It is interesting to note that all nodes
in this group can complete slot assignment in one time-slot, as no slot assignment
permission is required from neighbors.
Figure 2. Network topology with NoNs of each node within {.}. An example of NiT
of node p is also shown
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Figure 3. Successful slot assignment to nodes e and p in NG-I
Figure 4. Successful slot assignment to node b in NG-II
Figure 5. Failed slot assignment to node b in NG-II
B. Slot assignment in NG-II
For slot assignment in NG-II, we propose two procedures: one based on
an elaborate exchange of control messages, and the other based on a prioritization
mechanism using node IDs.
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Message exchange based slot assignment:
Each node in this group has at least one or more nodes (that have not
assigned slots yet) in its NiT with the same number of NoNs as that node itself. For node i, in this group, let Ni be the number of nodes with the same
NoNs as the node i that have not yet assigned slots. To assign slot, the node
runs a lottery for which the probability of success is chosen as P(i)=1 / (Ni +1).
In case a node does not win the lottery, it will wait for certain time slots chosen
at random before trying the lottery again. If the node wins the lottery, it assigns
the minimum possible slot that has not been assigned to any node within its
contention-zone and sends the SAR message to its neighbors. For the given network topology in Fig. 2, initially nodes b and l are in this group and the probability
of winning the lottery for each of them is 1/3. The probability that only one of them
wins the lottery in a given slot and thus avoid collision of their announcement
messages is 2/9.
For the slot assignment to be complete, the node has to wait for the SAG messages from nodes within its contention area. After the slot assignment is granted by
the nodes in the contention area, the node i sends out a SAC message; an example
of which is shown in Fig. 4. Similar to the NG-I, after receiving the confirmation
message, the one- and two-hop nodes update their slot assignment information, and
remove node i from their NiTs for further consideration in the node classification
step. Interestingly, the node i does not have to wait for response messages from
nodes that have NoNs different than the node itself as well as from nodes having
same NoNs that have completed slot assignment. For example, if node i has only
one node with the same NoNs, then a confirmation from only that node is needed:
if that node is a one-hop neighbor then the slot assignment can be completed
in three slots – one to send SAR, second to receive SAG, and third to send SAC;
if that node is a two-hop neighbor, then can be done in five slots – two additional
slots are used by the one-hop node to relay SAR/SAG messages; and in either
case, a unicast addressing can be employed. This can reduce the traffic overhead
substantially, and thereby reduces the chances of collision of the slot assignment
control messages in the network.
When a node receives a SAR message of a one-hop node, the receiving node
performs one of the following tasks:
I. If the receiving node and all of its ONs have NoNs which are different
than the NoNs of the SAR transmitter, then the receiving node neither
forwards the SAR to its ONs nor sends a reply message (i.e., SAG/SAD).
The receiving node simply waits for the SAC/SAF message form the transmitter of SAR.
II.In case the receiving node has same NoNs as the transmitter of SAR but
the NoNs of all of its ONs are different, the receiver node sends a SAG
message to the transmitter. In this case also, the receiver does not forward
the SAR to its ONs.
1)
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III. If the NoNs of the receiving node are different than the NoNs of the transmitter of the SAR message, but one (or more) of its ON(s) has (have)
same NoNs, the receiver forwards the SAR to those ONs. In this case,
the receiver has to relay back any reply message (i.e., SAG/SAD) from its
ONs: The receiver node sends a SAG message if all of its ONs with same
NoNs reply with SAG, and otherwise sends a SAD message.
IV. If the receiving node and one (or more) of its on-hop nodes have same
NoNs as the transmitter of the SAR message, the receiver node sends a SAD
message to the transmitter if it has already sent a SAG for this particular
slot to one of its ONs other than the transmitter of recently received SAR.
Otherwise, the receiver first forwards the SAR to its ONs with the same
NoNs as the transmitter of SAR and waits for their reply. Once the receiver
receives responses of those nodes, it fuses them and replies with a SAG
message if all those nodes send SAG, else it replies with a SAD message.
When a node receives a forwarded SAR message of a two-hop node, the receiving node performs one of the following tasks:
I. If the NoNs of the receiver are different than the NoNs of the originator
of the SAR message, the receiver discards the SAR message and does
nothing else.
II. In case the NoNs of the receiver are same as the NoNs of the originator
of the SAR message, the receiver replies with a SAD message if it has already sent its own SAR message to its neighbors for the same slot, otherwise
it replies with a SAG message.
When a node receives a SAF message originated from a one-hop node it executes one of the following tasks:
I. If the NoNs of the receiver and all of its ONs are different than the transmitter of the SAF message, the receiver discards the message.
II. In case the NoNs of the receiver are different than the NoNs of the transmitter but some (one or more) of its ONs have same NoNs as the transmitter,
then the receiver forwards the SAF to those ONs.
III. If the NoNs of the receiver are same as the transmitter, but the NoNs
of all of its ONs are different than the transmitter, then the receiver frees
the slot for which it has earlier sent the SAG message to the transmitter
of SAF. After which, the receiver discards the SAF.
IV. In case the receiver and some (one or more) of its ONs have same NoNs
as the transmitter of SAF, the receiver releases the slot and forwards
the SAF to those ONs having same NoNs.
When a node receives SAF originated from a two-hop node, the receiver
discards the SAF if its NoNs are different than the transmitter of the SAF, else
it releases the slot related to the SAF.
When a node receives a SAC message it updates its NiT; if the SAC is originated from a one-hop node, then the receiver also forwards the SAC to its ONs.
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Remark 1: The slot assignment in NG-II by the preceding dialog based mechanism though requires message exchange between a subset of nodes in the contention
area of a node, however, despite this the control message overhead could be high.
For instance, if multiple nodes in a contention area try to assign slots at the same
time, then none of them will succeed and they have to try again after a random
back-off time, which would slow down the convergence of the algorithm. In this
regard, next we present an alternative slot assignment procedure for NG-II which
avoids the preceding detailed message exchange routine when assigning slots to
the nodes.
Figure 6. For linear network topology all nodes except the two nodes at the extremities are in NG-II.
When the number of nodes is large and the nodes in the network are in increasing (or decreasing)
order of their IDs, for slot assignment, the nodes on the right (left) edge have to wait until all nodes
on their left (right) side have completed slot assignment
Alternative priority based slot assignment procedure:
When all nodes have unique IDs, the slot assignment in NG-II can be handled in a much simpler way, very much like in the NG-I. Let there be a one-to-one
function Ψi which could map a node ID i to a unique numeric number µi, that is,
2)
Ψi: i → µi, µi ≠ µj, i, j Î J.(1)
Now any node i in NG-II, instead of randomly deciding about when to initiate slot assignment, compares its ID µi with the IDs of the nodes (that have not yet
assigned slots) having same NoNs in its NiT. If its ID is greater than these nodes,
it assigns the minimum possible slot(s) to itself which is(are) not yet taken by
the nodes in its contention area; otherwise, the node does not try to assign slot(s)
unless the preceding condition is true. After assigning the slot(s), the node sends
out the SAC message to neighboring nodes. Note that, like the nodes in NG-I, for
slot assignment by this procedure the nodes in NG-II are not required to exchange
messages SAR/SAG/SAD/SAF.
Remark 2: The control message overhead of the alternative priority based slot
assignment procedure is quite low compared to the message exchange based procedure. However, under the alternative slot assignment procedure, in certain cases,
some nodes in NG-II may have to wait inordinate amount of time for slot assignment, for example, as shown in Fig. 6, the nodes in NG-II on the right (left) hand
side have to wait until all other nodes in the group have completed their slot assignment. In such scenarios where a node in NG-II has to wait excessively long time to
initiate slot assignment, the message exchange based slot assignment mechanism
could be employed. To be more specific, if a node in NG-II does not receive new
slot assignment information, during a predefined time duration, about any node
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within its contention area having the same NoNs as the given node then the node
initiates slot assignment according to the message exchange based procedure.
In this regard, to avoid more than two nodes to initiate slot assignment at the same
time, after expiration of the predefined time duration, the nodes employ random
back-off mechanism whereby a node waits for randomly chosen time-slots before
starting slot allocation. If during this time, the node receives new slot assignment
information, it will abort the message exchange based slot assignment procedure
and will revert to the priority based slot assignment procedure.
C. Slot assignment in NG-III
The nodes in this group do not try to assign slots to themselves. They listen to
the messages from their neighbors, assist in slot assignment of their neighbors by
forwarding slot assignment control messages as discussed in the preceding sections,
and update their slot assignment information. When a node, in this group, receives
a SAC message about any node in its contention area, it removes that node from
its NiT for further consideration in the node classification step.
Each node, in NG-II and NG-III, that has not yet assigned a slot to itself,
whenever updates its slot assignment information and removes any node from
consideration in its NiT, it reruns the node classification test shown in Fig. 1. Note
that after the classification test, a node in NG-II that has not yet assigned a slot to
itself may find itself in NG-I, and a node in NG-III may find itself in either NG-II
or NG-I (cannot be in both because the groups are mutually exclusive). For example,
when nodes e and p (which were in NG-I) finish slot assignment, their neighbors
d, j, i, and o (which were in NG-III) reclassify themselves in NG-II. Each node
handles the slot assignment according to the procedure specific to its current node
group. It is interesting to note that the nodes can only upgrade their groups and
thus the slot assignment protocol is bound to converge within limited time; that is,
the slot assignment to all nodes will be completed within a bounded time. We will
analyze the convergence time and the message complexity of the protocol in more
details in our future work.
Proposition: The execution of the HUDSAP produces conflict-free slot assignment schedule.
Proof: To show that the slot assignment schedule produced by the HUDSAP
is conflict-free, it suffices to note the following: 1) At any given time only nodes
in NG-I and NG-II are assigning slots and the two groups are mutually exclusive; 2)
By definition, all neighboring nodes of each node in NG-I do not assign slots until
slot assignment is completed for the nodes in NG-I; and 3) Each node in NG-II
assign slot which is not assigned to any other node in its neighborhood by exchanging the control messages, or by the prioritization mechanism.
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IV. Frame length selection
With uniform frame size across the network, although the design and implementation of the MAC protocol will be simplified, however, the channel resources
will remain underutilized. In the conventional TDMA slot assignment protocols,
frame length is fixed based on the maximum expected number of nodes in the network, for instance, to ensure that each node gets at least one slot [8]. These protocols
show poor channel utilization as they must leave enough unused slots for new
coming nodes. Another possibility is to dynamically set the frame length for each
node which is equal to the maximum slot number (MSN) assigned within the network. However, this would require each node to know the MSN. The propagation
of the MSN within the entire network would not be adaptive to the local slot assignment changes – any change in the slot assignment may change the MSN and
the new value has to be propagated throughout the network. Although by setting
the network-wide same frame length based on the MSN effectively removes the requirement of a priori fixing the number of slots in a frame, however, it would still
give lower channel utilization efficiency.
The channel utilization can be improved by variable frame length for each
node depending on the slot assignment in its neighborhood, that is, to change
the frame length dynamically according to the slot assignment to the nodes within its
contention area. If the contention area of a node is limited to its two-hop neighborhood and reuse of slots is allowed outside this area, then each node can set its
frame length which is a function of the MSN assigned within the contention area
(instead of the entire network). Note that, the MSN allocated within the contention
area cannot exceed the two-hop neighborhood size of the node. To have conflictfree transmission among nodes with different frame lengths, usually the lengths
of frames are chosen as multiple of two [4-8].
To set the frame length Li of node i according to the local MSN, the Z-MAC
protocol in [8] proposed the following rule:
Li = 2k,(2)
where k is a non-negative integer. The value of k is selected such that the following
holds:
2k–1 ≤ Ai ≤ 2k–1,(3)
where Ai is the MSN within the two-hop contention area of the node i. This scheme
although could achieve better channel reuse than the uniform frame length rule
across the entire network, however this is not optimal from the point of view of channel utilization efficiency. In this regard, we propose an alternative scheme by which
local framing rule varies depending on the node connectivity. Specifically, we classify
nodes in two groups: leaf nodes and non-leaf nodes. For leaf nodes – a leaf node
is a node which has only single one-hop neighbor; otherwise the node is a non-leaf
node – the frame length is selected as in the Z-MAC. For non-leaf nodes, the framing
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
259
rule is modified as follows. Let Ãi be the MSN within the one-hop neighborhood
of the non-leaf node i. The frame length is set as in (2) and (3) with Ai replaced by Ãi.
An example of the heterogeneous frame length (Z-MAC and proposed) and
the uniform frame length across the network is given in Fig. 7. From the figure,
we can observe increase in the channel reuse due to the variable length frame size.
The increase in channel reuse directly translates into higher channel utilization
efficiency as well decrease in the data transfer delays.
Figure 7. An example of variable and fixed length TDMA frames for a given conflict-free slot assignment: In the case of uniform length frame, all nodes have 8-slot frame length; in the Z-MAC
variable length frame strategy, node a has 4-slot frame length whereas all other nodes have 8-slot
frame length; and in the HUDSAP variable length frame strategy, nodes a and b have 4-slot frame
length whereas all other nodes have 8-slot frame length
Once the nodes have decided their frame lengths, the information are exchanged with nodes within their respective contention areas. After which the nodes
can start data transmission within their assigned time-slots. The Z-MAC framing rule
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produces slot assignment which is always conflict-free. However, in the proposed
framing scheme, there could be occasional conflicts in assigned slots. Such conflicts
can be detected by the nodes once frame-length information is available. When
a node detects a slot conflict due to frame size selection, it compares its assigned
slot with the slot assigned to the node causing the conflict. If the slot of the given
node is less than the conflicting node, then the given node selects its frame length
according to the MSN within its two-hop area (instead of one-hop area); otherwise,
it leaves the conflict resolution to the conflicting node which would use the same
procedure to resolve the conflict.
V. Simulation examples and discussion
In this section, with some numerical experiments, we evaluate channel utilization efficiency (i.e., the spatial reuse of slots) of the proposed HUDSAP and compare
it with the centralized schemes RAND and MNF of [13]. Note that the DRAND
is proven to achieve the same channel utilization efficiency as RAND [7]; so the comparison of HUDSAP with DRAND is not performed here. For a given network,
assuming uniform frame length, the channel utilization efficiency is measured
in terms of the minimum number of slots used by the protocol to find the conflictfree slot assignment schedule. We deploy the nodes in a 400-by-400 planar region.
We conduct two numerical experiments: In the first, we fix the transmission range
of nodes to 40 and vary the number of nodes from 50 to 400; in the second, we fix
the number of nodes to 300 and vary the transmission range from 10 to 50. Each
point in the numerical results is obtained by averaging over 104 random deployments (uniform distribution) of the nodes in the region.
The results are plotted in Fig. 8 and Fig. 9. The figures show that the HUDSAP
gives conflict-free slot assignment schedule that requires substantially less number
of slots than the RAND (and consequently of the DRAND). That means, the spatial
reuse of the slots, and consequently the channel utilization efficiency, is higher
in HUDSAP. It should be noted that we deployed the node in a planar region of fixed
area. Therefore, when either the number of nodes is small or the transmission range
is small, or both, the performance gap is negligibly small. This is because, in such
scenarios, the network is divided into small sub-networks (each comprising a few
nodes) that are disconnected from each other – here we do not impose the condition that the network is connected. The performance of slot allocation protocols
from spatial reuse point of view in such small networks does not differ much.
However, if we impose the condition that the network is always connected, that is,
any node can be reached from any other node (via multi-hops), then there would
be a noticeable performance gap between the HUDSAP and the RAND even for
a network comprising 50 sensors or less, which we observed in simulations that are
not included here due to space constraints. Regarding the topological information,
in DRAND each node requires knowledge about the identities of the nodes within its
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261
two-hop contention area and their slot assignment. Compared to that, in HUDSAP,
each node also requires knowledge about the NoNs of the two-hop neighbors. Note
that, although the NoNs of the one-hop neighbors is available in DRAND but that
knowledge is not employed in slot scheduling.
Figure 8. Channel utilization efficiency comparison for fixed number of nodes
Figure 9. Channel utilization efficiency comparison for fixed transmission range
For MNF two labeling schemes are considered, one based on the the NoNs
and the other based on the number of two-hop neighbors (NtN) which are, respectively, denoted as MNF-NoNs and MNF-NtNs. The figures show that there is no
appreciable gap between the schedule length of the HUDSAP and the MNF-NoNs.
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However, there is a marginal gap between the performance of the HUDSAP and
the MNF-NtNs. The reason for this performance gap is that the HUDSAP is designed for ordering based on NoNs whereas the MNF-NtNs is based on the NtNs.
Even though the performance gap is not substantial, it is straightforward to extend the HUDSAP where prioritization of slot assignment is based on the NtNs,
in which case it is expected that the performance of the HUDSAP and the MNFNtNs will converge. However, that will require each node to know the NtNs of all
nodes within its contention area, which would entail additional protocol overhead.
Given that there is a marginal gain in channel utilization efficiency, the additional
overhead may not justify the gain. Besides, it should be noted that the HUDSAP
is a distributed slot assignment protocol which relies on local topology knowledge
at each node, whereas the MNF is a centralized protocol which requires global
topology information.
Next we evaluate the impact of variable frame-length selection on the channel utilization efficiency. In this regard, for the preceding two network deployment scenarios, we compare the number of additional packets that can be
transmitted within the frame length of Z-MAC when the frame-length is selected according to the proposed framing scheme. In Fig. 10 and Fig. 11 we
plot the additional packets: average over 104 random deployments of the nodes
and the maximum over the deployments. The figures show that with the proposed framing scheme, we could transmit more packets, which when seen for
the network use over considerably longer time window could translate into
substantially higher throughput.
Figure 10. Number of additional packets (maximum and average over deployments of nodes)
that can be transmitted within the frame length of Z-MAC under the proposed adaptive frame size
selection scheme. Comparison is for fixed transmission range
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263
Figure 11. Number of additional packets (maximum and average over deployments of nodes) that
can be transmitted within the frame length of Z-MAC under the proposed adaptive frame size
selection scheme. Comparison is for fixed number of nodes
VI. Concluding remarks
In this work, we proposed a distributed TDMA slot assignment protocol to
schedule access of the wireless nodes to the shared wireless channel. The nodes,
by this protocol, can find conflict-free slot assignment using only the local topology information. The proposed medium access scheduling protocol is suitable for
wireless ad hoc networks for mission-critical applications and emergency response
services which require wireless connectivity be provided that meet certain QoS
requirements. We showed that the protocol can achieve higher channel utilization
efficiency compared to the existing protocols like RAND and DRAND. We also
proposed an adaptive frame size selection scheme which gives higher channel
utilization than the framing scheme proposed in Z-MAC protocol.
In this work we give a qualitative description of our protocol and substantiate
it with performance evaluation with a few numerical examples. In our planned ongoing work, to better understand the behavior of the protocol, we would do further
analysis based on analytical modeling and event driven simulations. We plan to
compare the convergence time and the control message overhead of the protocol.
We also plan to study the behavior of the algorithm for dynamic topology changes
due to the nodes joining and leaving the network, and the movement of the nodes
within the network.
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IEEE Trans. on Commun., vol. COM-38, pp. 456-460, Apr. 1990.
Hybrid Network Synchronization for MANETs
Harri Saarnisaari, Teemu Vanninen
Centre for Wireless Communications, University of Oulu, Oulu, Finland
[email protected], [email protected]
Abstract: Military radio networks such as mobile ad hoc networks (MANETs) usually rely on global
satellite navigation systems (GNSSs) for network time synchronization. However, in reality all nodes
may not have a GNSS timing device or they do not have a direct link to a GNSS timed node. In addition, in some situations GNSSs may fail. Therefore, additional supporting mechanisms are needed
for synchronization. This paper provides a hybrid algorithm that uses GNSS timed nodes as master
time servers if they are available but automatically turns to a distributed mode if GNSS time is not
available providing robustness and survivability. Furthermore, the distributed mode can be used also
as a stand-alone synchronization mechanism, i.e., a GNSS time reference is not necessarily needed
at all. Simulations show the behavior of the algorithm in different scenarios but as main conclusions
it can be said that the availability of GNSS timed nodes speeds up the initial convergence, the convergence rate depends on the number of nodes and the number of hops and that if GNSS time is lost,
the network still maintains synchronism but time is not necessarily GNSS time.
Keywords: component; network; synchronization; hybrid
All the pictures are created by the authors
I. Introduction
Military mobile ad hoc networks (MANETs) usually use time division multiple access (TDMA) as a channel access protocol and often frequency hopping to
provide robustness. These operations require time synchronized nodes. There are
several possible strategies for network time synchronization [1]. One option is to
have full autonomy, where the clocks function independently without affecting
to each other. This option requires frequent calibrations since clocks tend to drift
from each other. Precise clocks that provide autonomy for a period [2] or external
precise time source such as global satellite navigation system (GNSS) are also possibilities herein. A second option is the (centralized) master-slave structure. This
is a hierarchical system where the lower level nodes synchronize with the higher
level nodes but not vice versa. A drawback of this method is that a fault in a master
(or sub-master) node affects the whole (rest of the) network. This makes the network
vulnerable. The advantages of this method are its simplicity and that clock quality
requirements are reduced to the higher hierarchy levels, which lowers the costs.
The third alternative is the mutual (distributed, decentralized) synchronization
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in which the nodes synchronize themselves based on mutual cooperation without
a master. Naturally, there are also hybrid strategies where a master (or a group
of masters) leads the game but the rest of the nodes cooperate in a mutual fashion.
Possibly, there is a hierarchical structure and cooperation exists within a hierarchy
level. Furthermore, the master could be one that is not permanent but it can be
replaced by another node in the case of failure or if it is not available for some
other reason.
Inside the strategies are protocols, which describe how timing messages are
distributed in a network and what and how many messages are needed. Several
protocols have been proposed for network time synchronization (NTS) of wireless
sensor and ad-hoc networks. References [3-9] provide a good snapshot of these
protocols. They follow the above mentioned general strategies in a way or another.
Actions one needs are not usually concentrated in a single spot (system). Instead, one may use information coming from different systems all around the globe.
In order to keep different systems on the same time base it is natural to assume,
or require, that if GNSS time is available it has to be used since it is a provider
of the Universal Coordinated Time (UTC). This paper considers a novel hybrid
synchronization approach proposing an algorithm which uses a GNSS timed
node or nodes as a master or masters if they are available. If those are not available it uses a distributed approach. This increases robustness since lost of masters
does not destroy synchronization. Both use of GNSS time whenever available and
robustness are features sought by military. It is also very possible that all nodes
do not have a GNSS time source device or they are not direct neighbors of such
a node. The proposed algorithm is able to synchronize nodes in a multi hop fashion.
It uses nodes that have heard GNSS timed nodes or their nearest or further distant
neighbors as masters if those are available. Furthermore, the algorithm distributes
knowledge of existence of masters all over the network, and neglects this information
if masters are lost. This information may be utilized in the merging case. The way
how network time synchronization messages are delivered (which channel, how
often, etc) is not proposed, neither there are special requirements on that meaning
that the algorithm is very generic. Simulations demonstrate convergence speed for
different network size, (normalized) network synchronization accuracy and effect
of losing all the masters.
II. Algorithm
The principles of the proposed algorithm are simple. First, use GNSS time
as a master time reference if it is available. Second, if GNSS time is not available in the network, use a distributed algorithm. A consequence of the first principle
is that even if only one GNSS timed node is available, its existence must be distributed all over the network and its time must be used as a reference. In practice this
is implemented by maintaining a heard master (HM) flag in the synchronization
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
267
message. It is on if GNSS time is available and off otherwise. It is set on if a node
receives message from a GNSS timed node (master) or from a node whose HM
flag is on. A consequence of the second principle is that if a node is not a GNSS
timed node or does not receive any time synchronization messages where the HM
flag is on, it uses a distributed time synchronization algorithm.
The state machine of the algorithm is shown in Fig. 1. The shown algorithm
includes the integrity check not implemented in the simulation chain. The integrity
check is essential to self-monitor the network for malfunctioning of GNSS timed
nodes (or detect misleading GNSS timed nodes) and inform the operator if needed.
Herein, this aspect was not considered and all GNSS timed nodes were assumed
to perform properly, i.e., they all show the same time. Neither was inform-mycurrent-network feature implemented. That can ease and speed up merging cases
that is discussed briefly later on.
Figure 1. The state machine of the proposed network synchronization algorithm
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A node initially listens a period for network synchronization messages
(NETSYNC MSG). If it does not receive any it initiates its own message. Then it waits
again. The waiting time may include some randomness around an initial value or
in an interval. It may also be shorter in the initialization phase than in the communications phase. It may be shorter for GNSS timed nodes than for other nodes
since that would favor GNSS timed nodes that are anyway used as master nodes.
If it receives a message its behavior depends on the contents of the message and its
own state. If the node is a GNSS timed node (master) it does not adjust itself. If it is
non GNSS timed node its behavior depends on contents of the message as clearly
described on the state machine. However, some principles may be unclear from
the machine. Therefore, some aspects are considered more detailed in what follows
in this section.
Time tuning at the receiver is based on time difference of the local clock
reading and received time readings [10]. Time tuning is done at the every adjustment instant, i.e., discretely at certain points, not continuously after receiving any
single message. This is because of robustness. If all messages would be used for
time tuning after they arrive it may result a ping pong effect in time or between
master-slave and distributed mode. In addition, if a receiver could receive several
messages it can select the best ones for its own purpose. For example, it receives
a few messages from HM-flag-off nodes and then finally at the end of the listening period a message from a HM-flag-on node. Then, it can be use the best time
source on that period, i.e., the HM-flag-on node and ignore the HM-flag-off nodes.
Observed time difference is used as such if the message is from a master node
(node with a GNSS time device) or from a node with the HM lag on. If several
masters are observed during a period, only one is used. This could be the one
with the smallest time since that is closest to the receiver and, as a consequence,
the bias from uncompensated propagation delay would be the smallest. In the simulations the first master was selected for simplicity. If several HM-flag-on nodes are
observed, average of their time difference to the local time is used to set the time.
In both the cases the observed (and selected) time differences are used in the masterslave fashion, i.e., directly to set the time. In the distributed mode the average time
difference is weighted by a coefficient (0.5 herein) and then added to the previous
time [10].
An adjustment interval should be selected such that a new adjustment is done
before clocks drift too far away from each other. Or, indeed, well before to leave
some margin. What affects to this interval are the initial synchronization error after
previous adjustment, discussed in the next subsection, and clock skew caused by
frequency errors. For example, 1 ppm (part per million) clocks cause 1 µs error
in every second. Since this may be in both directions, the total maximum error is 2 µs.
One has to count how many seconds clocks could be without adjustment and select
adjustment period based on this. At the initial phase the adjustment period, as well
as listening period, could be shorter. In the communications phase, at least, these
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
269
two coincide. It would also be advantageous if time information (NETSYNC MSG)
could be transmitted more than once in the adjustment period since that increases
robustness, e.g., since some messages may be lost. However, this may not be sensible
since that increases overhead in the system. Therefore, final selection is a tradeoff
between overhead and robustness.
Time information is sent in a network synchronization message. The message
includes a time stamp, a master flag and the HM flag information. The master flag
informs that a node is a master (GNSS timed node). The synchronization messages
are send in a control channel (either logical or physical), but in this paper we do
not care how the control channel is actually implemented. It is just assumed that
the network synchronization messages are send at certain intervals and that these
messages can be heard by other nodes. This is a sensible assumption since otherwise
synchronization is impossible.
Possible difficult points in MANETs are late entry and merging. These should
be made as automatic and fast as possible, i.e., the operator should not need to
manage this and it should not take tens of minutes. As the network synchronization
process is part of this, it should be fast too. It is obvious from the state machine that
the GNSS timed node case is trivial. If two GNSS timed networks merge they are
readily synchronized. If a non-GNSS timed node or network merges with a GNSS
timed node or network it adapts GNSS time. However, if two non-GNSS timed
networks merge the situation is more problematic since use of pure distributed
algorithm would yield to very long convergence period. Therefore, the process
should be speeded and that means, e.g., merging case detection and decision which
of the networks has to change its time and which preserves its time.
A. Performance of NTS
It can be concluded from [10-13] that the accuracy of the algorithm depends
on the accuracy of time delivery and biases caused by uncompensated delays and
clock frequency offsets (skew). Since the beating frequency of clocks is not usually controlled, skew occurs in every adjustment period. This term is mitigated by
properly chosen the adjustment interval. Contrary, propagation delay could be
compensated. In master-slave networks bias by uncompensated delays cumulates
on each hop the master’s message has to jump whereas in distributed networks
this effect is somewhat averaged away. Therefore, delay compensation might be
a critical issue especially if the network synchronization requirements are high
and also if distances (measured in seconds) between nodes are large, quite close to
the synchronization requirement. There are several means for the compensation.
In the master-slave scheme master and slave may interact pairwise to measure
the propagation delay. This means that interaction has to be repeated for each pair.
An example of this is the IEEE 1588 precise timing protocol. In two way schemes
nodes send measured time differences back to the origin which use them to mitigate
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the delay. If feedback fails this kind of system reduces to uncompensated systems,
i.e., it still works but has bias. The system is robust in the sense that it does not rely
on delay compensation (assuming that provided synchronization accuracy is sufficient). Node positions, if they are known, can be used to calculate propagation delays
and used for delay compensation. In this sense use of situation awareness picture
(concerning node positions) could be a valuable tool in network synchronization.
Note that the positioning accuracy does not necessarily need to be extremely accurate since 1 µs corresponds to 300 m. If one or few µs is tolerable compensation
accuracy, then 300 m to 1 km positioning accuracy would be sufficient.
III. Simulations
Simulations were implemented using MATLAB. The simulation area is a X
by X square with N randomly deployed nodes. The communication range is αX.
There are M masters that could be switched off at the moment T. Results are shown
as a function of adjustment intervals. All time errors and distances are normalized
to time-of-arrival (TOA) measurement accuracy which is typically about one tenth
of the inverse of the signal bandwidth or, equally, one tenth of the symbol duration.
Therefore, 1 MHz signal provides (about) 0.1 µs TOA accuracy (or 30 m) and 5 MHz
signal 0.02 µs (6 m) accuracy. With these numbers, if X is 500, it corresponds to
15 000 m or 3000 m, respectively. Correspondingly one founds effects of skew and
distances. One counts the effect of skew in TOA accuracy units during the adjustment
period. So, if β is the skew and Tadj the adjustment period, then the effect of skew
is βTadj /σ, where σ is the accuracy. For example, if the skew is 1 ppm and Tadj is 1 s
or 100 s, then for 1 MHz signal this corresponds to 10 and 1000, respectively, and
for 5 MHz signal these are five times larger.
If required synchronization accuracy (worst case) in frequency hopping
systems would be half the hop duration and hop rate would be 1000 hops/s, then
the required worst case synchronization accuracy would be 5000 units or 25 000
units, respectively using the above numerical values. This would be nice to remember
when one interprets the simulation results. Even without reference to hopping systems one should calculate worst case accuracy related to delay estimation accuracy.
Furthermore, we calculate clock time errors with respect a reference node since
this is the meaningful way to do it [10]. If there are masters, a master is selected
as a reference that is quite natural. In the other case node number 1 is selected.
The former selection causes some harm when lost of GNSS nodes is investigated
since error is still measured with respect to a master. However, most significant
conclusions can be drawn even with this small inconvenience. Results are averaged
over 100 runs with same node deployment. The initial time offsets, skews and TOA
errors are altered.
The first result shows ultimate accuracy limits since delays are perfectly compensated and skews are zero. There are 32 nodes including 5 masters and X = 500.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
271
The initial offset (of non-masters) is from range ±3×108. The communication range
is 250. A random deployment is shown in Fig. 2 and resulting time errors at Fig. 3.
Since the communication range is long and masters quite uniformly distributed
over the area, convergence is fast, at the first round, i.e., all nodes are direct neighbors of masters. The not shown root means square error (RMSE) values are equal
to one, i.e., equal to the time delivery accuracy as it should be. Therefore, the time
delivery accuracy is the ultimate limit.
Connectivity plot. Masters are red diamonds.
500
450
400
350
300
250
200
150
100
50
0
0
100
200
300
400
500
Figure 2. Example random node deployment into area and existing connections.
X = 500, N = 32, M = 5, α = 0.5
10
8
6
time error term
4
2
0
−2
−4
−6
−8
−10
0
5
10
adjustment instants
15
20
Figure 3. Ultimate timing errors and convergence speed. X = 500, N = 32, M = 5, α = 0.5
The results in Fig. 4 show the same setup but with uncompensated delays.
The maximum bias is close to the maximum communication range as well as is
RMSE, which value, indeed, is the limit in the single hop case. In the multiple hops
case uncompensated delays would cumulate.
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If the skew is from interval ±500 (for non masters) the total bias still increases
as shown in Fig. 5 as well as RMSE, that goes up to level 400.
300
250
time error term
200
150
100
50
0
0
5
10
adjustment instants
15
20
Figure 4. Timing errors when delays are uncompensated. X = 500, N = 32, M = 5, α = 0.5
500
450
400
time error term
350
300
250
200
150
100
50
0
0
5
10
adjustment instants
15
20
Figure 5. Timing errors when delays are uncompensated and skews are present.
X = 500, N = 32, M = 5, α = 0.5
Shorter communications ranges often yield to separated subnets and are thus
meaningless to study in this paper where the focus is in the behavior of the whole
network. To study this shorter communication range effect we increase the number
of nodes to 128 and kept the rest parameters. The results are in Fig. 6. It can be seen
that some nodes are one hop and some two hop neighbors of masters. Then the range
was decreased to 100, i.e., one fifth of the area size. Although not easily seen from
the Fig. 7, the convergence rate decreases to four rounds (but depends naturally
on the largest number of hops between a master and a node). After convergence
the error is increased. It is believed that this is due to averaging in the algorithm
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
273
since a node computes an average based on all received HM-flag-on messages. This
averaging increases robustness. It would be possible to modify the algorithm to accept only one HM-flag-on message. It is expected that if this selection could be done
wisely, the error would be smaller. However, this feature was not tested. The total
bias is also increased due to this averaging and accumulated skew and delay terms.
Figure 6. Timing errors when delays are uncompensated and skews are present.
X = 500, N = 128, M = 5, α = 0.5
Figure 7. Timing errors when delays are uncompensated and skews are present.
X = 500, N = 128, M = 5, α = 0.2
The effect of losing the masters at the adjustment point 100 is investigated
when X = 500, N = 32, M = 5, α = 0.5. The results in Fig. 8 show that after the lost
the nodes' times diverge from zero (that is the masters' time). However, the nodes'
times stay very close together and their times go to the same direction. Therefore,
they remain synchronized, but that is what is expected. The novelty was to include
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an automated step from the master-slave mode into the distributed mode (and
back if needed).
Finally, convergence speed and error without master nodes is investigated.
The RMSE results in Fig. 9 show that convergence takes about 80 adjustment periods, i.e., much much more than in the master-slave mode. However, the timing
error is at the same level.
6000
5000
time error term
4000
3000
2000
1000
0
0
50
100
adjustment instants
150
200
Figure 8. Timing errors when delays are uncompensated and skews are present and masters
are lost at point 100. X = 500, N = 32, M = 5, α = 0.5
1000
900
rmse of time error wrt ref
800
700
600
500
400
300
200
100
0
0
50
100
adjustment instants
150
200
Figure 9. RMSE timing errors when delays are uncompensated and skews are present without
master nodes. X = 500, N = 32, M = 5, α = 0.5
IV. Conclusions
This paper proposed a novel hybrid master-slave distributed network time
synchronization algorithm that is robust to lost of masters. The algorithm uses GNSS
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
275
timed nodes as masters if they are available and is in a robust distributed mode otherwise. The algorithm has flexibility for further developments. The basic properties
of the algorithm were demonstrated by simulations. The ultimate synchronization
accuracy depends on the time delivery accuracy, but in real life uncompensated
delays or errors in delay compensation and unequal clock frequencies cause larger
time errors. The convergence speed in terms of required adjustments depends on
the number of hops between masters and slaves. The convergence speed in distributed mode may be much slower than in the master-slave mode. In addition, it is
slower for larger networks. Therefore, some kind of master-slave structure would
be beneficial in distributed networks at least in the initial phase. In addition, the adjustment period could be shorter in the initialization phase as usual.
Acknowledgment
This work was done for European Defence Agency (EDA) projects WOLF
(Wireless Robust Link for Urban Force Operations) and ETARE (Enabling Technology for Advanced Radio in Europe).
References
[1] W.C. Lindsey, F. Ghazvinian, W.C. Hagman, and K. Dessouky, “Network
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1985.
[2] H.A. Stover, “Network Timing/Synchronization for defence communications,”
IEEE Transactions on Communications, vol. 28, no. 8, pp. 1234-1244, August 1980,
in a special issue of network synchronization.
[3] K. Römer, P. Blum, and L. Meier, “Time synchronization and calibration in wireless
sensor networks,” in Handbook of Sensor Networks: Algorithms and Architectures,
I. Stojmenovic, Ed. John Wiley & Sons, 2005.
[4] B. Sundararaman, U. Buy, and A. Kshemkalyani, “Clock synchronization for
wireless sensor networks: a survey,” Ad Hoc Networks, vol. 3, pp. 281-323, 2005.
[5] C. Rentel, T. Kunz, “Network synchronization in wireless ad hoc networks,” Carleton
University, Systems and Computer Engineering Department, Ottawa, Canada,
Technical report SCE-04-08, 2004.
[6] C. Rentel, T. Kunz, “A clock-sampling mutual network time-synchronization
algorithm for wireless ad hoc networks,” in IEEE Wireless Communications and
Networking Conference (WCNC), vol. 1, 2004, pp. 638-644.
[7] Q. Li, D. Rus, “Global clock synchronization in sensor networks,” IEEE Transactions
on Computers, vol. 55, no. 2, pp. 214-226, February 2006.
[8] D. Mills, “Improved algorithms for synchronizing computer network clocks,” IEEE
Transactions on Networking, vol. 3, no. 3, pp. 245-254, June 1995.
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[9] E. Serpedin, Q.M. Chaudhari, Synchronization in Wireless Sensor Networks:
Parameter Estimation, performance Benchmarks and Protocols. Cambridge University
Press, 2009.
[10] H. Saarnisaari, “Analysis of a discrete network synchronization algorithm,”
in Proceedings of the IEEE Military Communications Conference, Atlantic City, NJ,
USA, 2005.
[11] A. Fasano, G. Scutari, “The effect of additive noise on consensus achievement
in wireless sensor networks,” in Proceedings of the IEEE International Conference
on Acoustics, Speech, and Signal Processing, April 2008, pp. 2277-2280.
[12] A. Davies, “Discrete-time synchronization of communications networks having
variable delays,” IEEE Transactions on Communications, vol. 23, no. 7, pp. 782-785,
July 1975.
[13] G. Scurati, S. Barbarossa, and L. Pescosolido, “Distributed decision through selfsynchronizing sensor networks in the precence of propagation delays and asymmetric
channels,” IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1667-1684,
April 2008.
Application of Dezert-Smarandache Theory
for Tactical MANET Security Enhancement
Joanna Głowacka, Marek Amanowicz
Faculty of Electronics, Military University of Technology, Warsaw, Poland,
{jglowacka, mamanowicz}@wat.edu.pl
Abstract: The article presents a concept of Dezert-Smarandache theory application for enhancing
security in tactical mobile ad-hoc network. Tactical MANET, due to its specification, requires collection and processing of information from different sources of diverse security and trust metrics.
The authors specify the needs for building a node’s situational awareness and identify data sources
used for calculations of trust metrics. They provide some examples of related works and present their
own conception of Dezert-Smarandache theory applicability for trust evaluation in mobile hostile
environment.
Keywords: situational awareness, trust, inference methods, tactical MANET, security, Dezert-Smarandache theory
I. Introduction
The mobile ad-hoc networks are collections of independent nodes that
can communicate via radio channels. These networks are often developed in conditions of limited or total lack of access to fixed infrastructure.
MANETs are characterized by high dynamic changes in the location of each
node and the vulnerabilities of various types of attacks. Due to the open medium,
ad-hoc networks are more susceptible to eavesdropping and data injections. A dynamic change of network topology contributes to the frequent connecting and
disconnecting nodes, and no central network monitoring makes it difficult to
detect malicious behaviour of nodes. In addition, the network resource limitations contribute to the selfish attacks. They are aimed at consuming a large amount
of bandwidth. One of the selfish behaviours is the failure to transfer the packages
by a node to conserve its own energy.
Security ensuring is particularly difficult for a tactical ad-hoc network, due to
the necessity of dealing with a hostile environment, strict capacity constraints, the reThis article was written as a part of a scientific research project financed by polish government budget for
science in 2010-2013 “An advanced methods and techniques of traffic control in tactical ad-hoc networks”
No. O N517 274 839.
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quirements for services, very rapid changes of network topology and dynamically
forming groups of common interests, which cannot be pre-defined by trust relationships [1]. These networks are characterized by simple capability of adding new nodes,
which may be of diverse nature, such as the allies, neutral or hostile nodes.
Figure 1. A sample mobile ad-hoc network structure
One method of ensuring the security is user authentication. Only the authorized nodes and those verified as allies can have access to the network. However,
during the mission, a node can be taken over by the enemy, or change the nature
of its behaviour – behaving to the detriment of the mission.
Due to the lack of a central management system it is needed for nodes to
cooperate. Each of them is in fact a router ensuring cooperation between subnets
and nodes located at a distance greater than the radio range.
Restrictions on ad-hoc networks contribute to the need of using other means
than in wired networks to satisfy the safety requirements. In addition to authorization
and authentication mechanisms, it is necessary for a node to have the knowledge
on the behaviour of other nodes in the network, determining safety routes for data
transfer and knowledge concerning the reaction manners in certain situations.
The situational awareness building method will be complement of standard security
mechanisms in mobile ad-hoc networks.
II. Node’s situational awareness
A. Definitions
To identify opportunities of secure cooperation between nodes in ad-hoc
networks, it is necessary to collect information about other nodes in the network.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
279
The ability to have accurate information about the surrounding reality and interpretation of the current situation in terms of the performed tasks is defined as a node’s
situational awareness.
The main product of the node’s situational awareness mechanism is information on the node trust levels.
Trust is an interdisciplinary concept, characterized by a variety of definitions.
It is understood as relying on the integrity, strength and ability of a person or thing.
In the case of ad-hoc network it is translated as a set of relationships between people
who use similar communication protocols [1]. These relations are defined based on
previous interactions of individuals. In [2], trust is treated as the degree of belief
about the behaviour of other entities. Trust can also be understood as reputation,
opinion, or the probability of correct behaviour [3].
In MANET, trust is the level of faith, which can be assigned by the node to
its surroundings on the basis of observations and opinions coming from the other
nodes in the network [4].
B. Benefits
Building node’s awareness is essential to achieve the mission. In heterogeneous
networks, the completion of the mission is dependent on the integrity of individuals. The knowledge gained from building node’s awareness can ensure cooperation
only between trusted entities that do not behave suspiciously.
Secure exchange of information between nodes requires proper selection
of the route of data transfer. Sending data via routes that are not safe may contribute to the leak or acquisition of data by unauthorized persons. Lack of metrics
allowing for choosing the path depending on the level of confidence in nodes and
the risk, that exists in choosing the path of data transfer and cooperation between
the nodes, may contribute to the failure of the operation.
Figure 2. The possibility of application of the knowledge from building node’s awareness
in different OSI model layers
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The dynamic process of creating a current situational view of node can be
the basis for decisions on how to control traffic.
The knowledge about the surrounding environment gained through the mechanism of building node’s awareness can be applied in different layers in order to protect
the communication between nodes. In the data link layer it can be used to define
a parameter indicating the possibility of cooperation with the nodes or the need
for failure to communicate with nodes characterizing a low level of confidence.
In the third layer level of trust, it can be used as metric routing protocol that will
allow you to safely share data. Specified nodes confidence level can be used also
in the application layer, where the nodes of questionable confidence level will be
forced to certain behaviour for performing its final assessment assignment.
C. Data sources
Node’s situational awareness in most cases is built based on direct interactions,
indirect observations and recommendations.
Trust determined by the node based on direct interaction and observation
of behaviour of other nodes is called direct trust.
Trust determined on the basis of indirect observations and recommendations
is called indirect trust. Recommendations shall be understood as opinions of other
nodes on the node for which the level of confidence is being specified.
Figure 3. Direct and indirect trust
In many cases information from various sources may be incomplete, inconsistent or conflicting. This requires the selection of appropriate methods of inference, which would allow clear and accurate assessment of the current environment
in which network node operates.
III. Related works
The problem of gathering information about the surrounding node reality and
determining the nodes trust in ad-hoc networks has recently been very popular and
widely developed in the literature, which demonstrates the importance of this topic.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
281
Probabilistic inference is the most frequently used method in the literature to
determine the node trust level. The information about node behaviour is evaluated
as 0 or 1, match or success.
In [5], the theoretical concept was presented to assess the level of trust and
its propagation. Trust in this approach is considered as a measure of uncertainty
expressed in a measure of entropy. In the case of entropy based trust model, promoted trust is calculated on the basis of individual trust values. In the trust model
based on the probability, the value of propagated trust is calculated by the probability of trust relationships. In this model, the probability of correct relations
assessment is also included. This concept also includes an assessment of trust on
the basis of observation.
In [6], new concept of the TMF (Trust Management Framework) was presented. TMF is used for nodes to obey protocol and cooperate with each other.
There are two types of TMF:
• reputation based – trust is assessed based on direct observations and information of the second hand, Bayesian approach based on the distribution
of β is being used,
• trust establishment – trust is assessed based on direct observation and
the relationship established between nodes without regard to previous
opinions of intermediate nodes.
Both types of TMF are immune to numerous attacks, therefore, OTMF (Objective TMF) has been proposed to prevent them. This solution is based on modified
Bayesian approach, in which different weights are assigned to different information
given at the time of their occurrence and that concerning their supplier. Influence
of the previous observations decreases exponentially, and the trust is used as a weight
for second-hand information. The two parameters – “trust value” and “confidence
value” – are combined in OTMF into one metric called “trustworthiness”.
The article [7] presents Hermes framework determination of node trust, which
helps in ensuring the reliability of packet transmission. Framework ensures that
the source sends packets only by the trusted intermediate nodes. In this solution,
each node determines the reliability metric of neighbouring nodes based on direct
observation of transmitted packets. Reliability is further extended with opinions
from other nodes. The proposed solution uses a Bayesian approach to determine
the value of trust. Trust is calculated on the basis of the beta probability distribution. Beta distribution parameters are determined from observations gathered
during the packet forwarding behaviour. A new metric called trustworthiness,
being a combination of trust and confidence metrics, is introduced.
In [8], the trust model was created for the DSR protocol in order to take a decision
on acceptance or rejection of the route. Decisions are made based on the estimated
trust respectively. Trust is determined on the basis of the direct trust and recommendations from other nodes. Direct trust is determined by the sum of experiences
on a given node and the recommendation trust is the sum of the recommendations
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received from the nodes. In determining the total trust value both values are taken
into account, but the direct trust has the higher weight. Direct trust is determined
by observation of packets forwarded by the evaluated node. Collected observations
are evaluated positively if they are not modified. The assessment is proportional to
the type of transmitted packet (e.g. route request, data packet). If sent packages are
modified, the observation is evaluated negatively. Negative evaluation is proportional
to the type of packet transmitted and the type of modification (e.g. modification
of information about the source, recipient, sequence number, route).
The use of probabilistic inference provides an easy way to determine the node
trust level but also has some disadvantages. Classical logic is based only on two
values represented by 0 and 1 or true and false. The border between them is clearly
defined and unchanging. In addition, classical probability theory does not allow
for distinguishing uncertainty (expressed in terms of probability) from incomplete
knowledge (lack of knowledge on the topic).
The other inference method used to evaluate and combine knowledge about
node behaviours is fuzzy logic.
Trust model based on the recommendation similarities (RFSTrust) calculated
with the use of fuzzy mathematics for MANET environment was presented in [9].
The fuzzy trust model is proposed to quantify and evaluate the trustworthiness
of nodes, which includes five types of fuzzy trust recommendation relationships.
Theoretical analysis and the simulation results show that RFSTrust model can effectively prevent selfish nodes and improves the performance of the entire MANET.
As in the case of inference based on classical logic, fuzzy inference does not
allow separation of uncertain knowledge from lack of knowledge.
Another method of enabling the representation of uncertainty is the mathematical theory of evidence.
The use one of the mathematical evidence methods – the Dempster-Shafer
theory (DST)[10] – for the determination of selfish behaviour of each node is presented in [11]. Node cooperation rating is based on the observation of correct packet
delivery. If a source node receives the information about arrival of the package, it will
mean that all nodes in the path behave correctly. In this case, the source node defines
the m() function for each path node, which is named basic belief assignment, as:
0
A  {SELFISH }
m( A)  
.(1)
1 A  {UNSELFISH }
In the absence of proof of information delivery and the lack of error messages,
the source node finds that a path includes not cooperating nodes. Unfortunately,
a number of these nodes and information about which nodes are selfish is not
known. The basic belief assignment is defined as:
 P
A  {SELFISH }
m( A)  
. (2)
1 P A 
{UNSELFISH , SELFISH }

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Each of the nodes in the network is equipped with a dedicated component
implementing an algorithm based on the Dempster-Shafer theory, which uses
the received recommendations and results of nodes observation. The solution
has defined two types of trust. The first determines the extent to which the source
node trusts another node that it will send the package correctly. It was used to
determine the belief function defined by the Dempster-Shafer theory. The second
value indicates the degree of trust in which a node trusts that recommendations
generated by another node are correct.
IV. Concept description
Dynamic evaluation of the environment surrounding the node is possible
by continuously monitoring the node behaviour, their analysis and information
inference.
Figure 4. Node evaluation process
In many cases, the knowledge acquired by a single node is insufficient to fully
assess the current situation, therefore it must be able to exchange information about
situational awareness built between nodes. Nodes can have different access to data
about other nodes, so their passing information may be incomplete or uncertain.
In the solutions described in section III, in most cases it is impossible to distinguish ignorance from uncertain knowledge, taking into account incomplete and
conflicted knowledge derived from various sources.
The Dezert-Smarandache theory [12-14] allows combining information from
multiple sources. It focuses on the problems of combining uncertain, conflicted
and inaccurate information [15]. DSmT overcomes the limitations of applying
the inference methods used so far in the assessment of trust: probabilistic inference, fuzzy logic or DST. These methods enable the binary evaluation of nodes or
creating hypotheses that cannot penetrate. By using DSmT it is possible to create
any number of hypotheses that do not have to be exclusive, and thus more accurate
assessment of the nodes. In addition, this method enables to distinguish the uncertain knowledge from ignorance.
This theory rejects the main limitations of the Dempster-Shafer theory:
• frame of discernment is a finite, exhausted and exclusive set of hypotheses,
• the application of the excluded middle rule,
• acceptance of the Dempster’s rule as a rule a combination of views,
• acceptance of the Dempster’s conditioning rule.
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The DSmT distinguished two types of models:
• free model – where frame of discernment (Θ) consists of extensive but not
exclusive items, so components can be mutually overlapping. This model
is called free because of the lack of assumptions imposed on the hypothesis.
• hybrid model – it allows the modelling of imprecise-views and exclusivity
constraints Θ elements. In this case, the elements may overlap, but they
do not have to.
The DSmT introduces the concept of hyper-power set, which is denoted by DΘ.
This collection is understood as the set of all proposals that were created from elements of Θ with the use of operators  and . For example:
for  
1 ,  2  D   0 , 1 ,..., 4  ,| D  |
5
(3)
0  , 1  1 , 2  1 , 3  1   2 , 4  1   2 .
A. Events monitoring
Node assessment is made based on direct node observation and information
from neighbouring nodes. Examples of observed events by which nodes can be
evaluated are:
• provision of information – some of the nodes in ad-hoc networks are
characterized by self-interested behaviour in order to deprive other nodes
of the shares, for example by failing to forward packets for selfish node
to the other nodes. Validation of packet transmission is possible through
the analysis of incoming acknowledgments, when transmission of acknowledgments is enabled in the network or by tracking the packages sent by
the monitoring node.
• compliance of safety rules – in tactical networks information may have different levels of sensitivity, for example: secret, confidential, non-confidential.
Data on a certain level of sensitivity can be sent only to nodes that have
access to information about a specific level or a higher level. Based on information collected on nodes access levels and data contained in the labels,
it can be verified if a node observes the principles of safety, i.e. whether
it has access only to data which is authorized and makes it available only
to the authorized users.
• recommendation correctness – in the case when trust level is determined
by recommendations from other nodes in the network, it is necessary
to provide protection against “liar” nodes. A “liar” shall construe nodes,
which transmit incorrect recommendations on other nodes, the objective
of re-routing packet forwarding, intercepting or preventing delivery to
the destination node.
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The observed events can be evaluated as 0, 1 – using the classical theory of probability. However, in many cases, the observed behaviour provides some indication
of both hypotheses, which would require omitting the evaluation of the event or
a need to assign two assessments – which would misrepresent the two behaviours.
Each behaviour is treated equally and the designated level of trust makes it impossible to identify the appropriate response to behaviour.
B. Nodes classification
Application of the Dezert-Smarandache theory provides for more hypotheses,
which enable more accurate assessment of behaviour. Additionally, through the creation of secondary hypotheses using sum and product operators, it can constitute
representation of imprecise and uncertain hypotheses.
During the observation of nodes behaviour they can be evaluated as:
• cooperating node (C) – the node transmitting information,
• egoistic node (E) – the node is not transmitting information,
• honest node (H) – the node transmitting the proper recommendations,
• liar node (L) – the node transmitting incorrect recommendations,
• secure node (S) – the node adhering to safety rules,
• unsecure node (U) – the node is not adhering to safety rules.
The set of basic assumptions in some cases may be insufficient for correct classification of nodes. Apart from the hypotheses, it is possible to determine the basal
intermediate hypotheses developed from the basal hypothesis with the sum and
logical product operators. The secondary hypotheses can distinguish:
• uncertain cooperating node (UC) – the node to which correctness of packet
forwarding was tested, but it is not possible to take clear decision whether
it is a cooperating or selfish node. This situation can occur if a node did
not receive confirmation of the package transfer – for each of the nodes
in the path the uncertain cooperating node hypothesis is taken.
UC  C  E
• suspect liar node (SL) – the node whose recommendations may be biased,
the value reported earlier, recommendation differs from the accumulated
knowledge and the other recommendations, however, this difference does
not yet allow for finding that they are wrong and biased.
SL H  L
• uncertain honest node (UH) – the node to which you cannot determine whether the recommendations forwarded by it are correct, because
of the lack of previously accumulated knowledge.
UH
H L
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• suspect unsecure node (SU) – the node whose behaviour indicates partial
compliance with security rules, for example, a node has access to the resources which are not eligible, but only make them available to the authorized individuals.
SU  S U
• uncertain secure node (US) – the node in the case of which you cannot
determine if it complies with the security rules, due to lack of knowledge
regarding the node's resource access level.
US  S U
With such specific hypotheses, it is possible to refine the assessment of nodes
indicating the possibility of exchanging data with the node, but it does not include an incoming recommendation and needs more detailed observation of node’s behaviour, for
example by using an additional mechanism for including a node to certain behaviours.
C. Sample evaluations
Information fusion is done separately for each type of event – co-operation
between the nodes- following the security rules and recommendation correctness.
Each hypothesis is assigned with a value of m(), depending on the number of observed events, which were assigned to a particular hypothesis. The m() is described
by conditions defined by the following formula (4):
m( ) 0
 m( A)  1. (4)
AD 
The set of hypotheses for each type of event allows using the DSm rule of combination for free-DSm models:
mM (  ) ( A) 

k
 m ( X ). (5)
i
i
X 1 , X 2 ,..., X k D  i1
( X 1 X 2 ... X k )
A
Tables 1 and 2 show some example values of the received recommendations
on node’s cooperation observation and compliance with security policies.
TABLE I. Recommendations about node cooperation
cooperating
egoistic
uncertain cooperating
suspect egoistic
m1
0,650
0,030
0,320
–
m2
0,720
0,050
0,230
–
m3
0,690
0,040
0,270
–
mM(Θ)
0,865
0,011
0,020
0,105
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TABLE II. Recommendations about security policy compliance
secure
unsecure
uncertain secure
suspect unsecure
m1
0,230
0,265
0,150
0,350
m2
0,270
0,290
0,070
0,370
m3
0,320
0,280
0,100
0,300
mM(Θ)
0,136
0,130
0,007
0,727
It can be specified based on the collected recommendations, if the node is cooperating and suspect unsecure. This information allows a node to take a decision on further
node observation. The node can forward low sensitivity information, whose transmission to unauthorized units will not contribute to the realization of carried out actions.
V. Conclusion
Ensuring security in tactical MANET requires gathering and processing information about the node surrounding reality. Information from various sources,
however, is often uncertain, incomplete and even conflicting. The method ensuring
coverage of all of this information is Dezert-Smarandache theory, which allows
representing of imprecise hypotheses. By applying the Dezert-Smarandache theory
it is possible to identify specific and general hypotheses, which can combine data
from different sources with access to information on the behaviour of nodes. As part
of further work a function that enables combining data including their update time
and weight of data sources will be determined.
References
[1] K. Seshadri Ramana, A.A. Chari, N. Kasiviswanth, “A Survey on Trust Management
for Mobile Ad Hoc Networks”, International Journal of Network Security & Its
Applications (IJNSA), vol. 2, no. 2, April 2010.
[2] L. Capra, “Towards a Human Trust Model for Mobile Ad-hoc Networks”, Dept.
of Computer Science, University College London.
[3] Z. Han, K.J.R. Liu, Y.L. Sun, W. Yu, “A Trust Evaluation Framework in Distributed
Networks: Vulnerability Analysis and Defence Against Attacks”, INFOCOM 2006.
25th IEEE International Conference on Computer Communications, April 2006.
[4] J. Głowacka, “Procedures of building nodes’ awareness for security in tactical
ad-hoc networks, KKRRiT 2011, Poznań 2011, Telecommunication Review
– Telecommunication News 2011 [CD], no. 6, pp. 405-408 (in Polish).
[5] Zhu Han, Yan Lindsay, K.J. Ray Liu, Wei Yu, “Information Theoretic Framework
of Trust Modelling and Evaluation for Ad Hoc Networks”, IEEE Journal on Selected
Areas in Communications, vol. 24, no. 2, February 2006.
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[6] Jien Kato, Jie Li, Ruidong Li, “Future Trust Management Framework for Mobile
Ad Hoc Networks”, IEEE Communications Magazine, April 2008, pp. 108-114.
[7] C. Zouridaki, B.L. Mark, M. Hejmo, R.K. Thomas, “A Quantitative Trust
Establishment Framework for Reliable Data Packet Delivery in MANETs”, In SASN ’05:
Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks,
pp. 1-10, New York, NY, USA, 2005.
[8] V. Balakrishnan, V. Varadharajan, U.K. Tupakula, P. Lucs, “Trust and
Recommendations in Mobile Ad hoc Networks”, Third International Conference on
Networking and Services, IEEE 2007.
[9] Junhai Luo, Xue Liu, Mingyu Fan, “A trust model based on fuzzy recommendation
for mobile ad-hoc networks”, Computer Networks 53 (2009), pp. 2396-2407.
[10] G. Shafer, “A mathematical theory of evidence”, Princeton U.P., Princeton, NJ, 1976.
[11] J. Konorski, R. Orlikowski, “DST-Based Detection of Noncooperative Forwarding
Behavior of MANET and WSN Nodes”, Proc. 2nd Joint IFIP WMNC., Gdansk,
Poland, 2009.
[12] F. Smarandache, J. Dezert, “Advances and Applications of DSmT for Information
Fusion”, vol. 1, American Research Press Rehoboth, 2004.
[13] F. Smarandache, J. Dezert, “Advances and Applications of DSmT for Information
Fusion”, vol. 2, American Research Press Rehoboth, 2006.
[14] F. Smarandache, J. Dezert, “Advances and Applications of DSmT for Information
Fusion”, vol. 3, American Research Press Rehoboth, 2009.
[15] J. Głowacka, M. Amanowicz, „Situational awareness of a military MANET node
– the basis” („Podstawy tworzenia świadomości sytuacyjnej węzła wojskowej sieci
MANET”), Telecommunication Review – Telecommunication News 2012, no. 2-3,
pp. 59-62 (in Polish).
Mechanisms of Ad-hoc Networks Supporting
Network Centric Warfare
Rafał Bryś, Jacek Pszczółkowski, Mirosław Ruszkowski
Systems Elements Designing Section, Military Communication Institute,
Zegrze Poludniowe, Poland,
{r.brys, j.pszczolkowski, m.ruszkowski}@wil.waw.pl
Abstract: In this article are presented the results of Ad-hoc networks mechanisms analyzes (identification, authorization, autoconfiguration and data exchange). These mechanisms were analyzed taking into
account specifications of Ad-hoc networks using in netcentric operations. After that, have been selected
the most appropriate mechanisms specified to be used in the special conditions. The Ad-hoc networks
mechanisms specifications will be helpful in designing and building of networks for netcentric operations.
Keywords: ad-hoc networks; NCW; autoconfiguration; authorization
I. Introduction
The way of modern warfare is strongly focused on the informations of enemy
status and activities. It prodeuces the need for new and more sophisticated communications systems, which will guarantee fast and secure way to exchange data
during Network-Centric Warfare. The overall military systems architecture provides
MANET (Mobile Ad-hoc NETwork) for a military communications networks,
at the lowest levels of command.
The essential features of Ad-hoc networks intended for use in netcentric
environments are:
• Network decentralization. Each node in Ad-hoc network can perform
the services as well as participate in the data transfer to the recipient.
• Ability for dynamic topology changes. Network nodes are independent
from each other and can arbitrarily changes its location, and thereby in their
mutual relations.
• Radio links usage. It eliminates the need to develop telecommunications
infrastructure.
• High network reliability – in case of failure of any network components,
others can automatically take over their functions.
• Good scalability (ease of expantion). Nodes joining the network that fulfill
certain safety requirements are able to realize services almost immediately.
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According to given above Ad-hoc networks advantages and communications
requirements for netcentric systems, it can be concluded that these networks can be
used in military systems but must fulfill some additional requirements for special
communication systems. Therefore, the Ad-hoc networks functions should be
enhanced with additional mechanisms: adaptation, identification, authentication,
auto-configuration, data exchange and routing.
In next paragraph of this article is presented the specification of above mechanisms in terms of special use (netcentric operations) of Ad-hoc networks.
II. Autoconfiguration mechanisms
Addressing in Mobile Ad-Hoc networks can be classified in terms of nodes
addresses managing, as follows:
• state accumulation addressing (so-called: stateful),
• stateless addressing.
In statefull approach, addresses are accumulated in a network entity that
has knowledge of assigned and not assigned IP addresses, so it can avoid duplication
of addresses. In stateless approach, each node randomly selects its own IP address
and then executes duplicate address detection test to ensure that selected address
is not yet in use on the network.
The accumulation state approach seems to be the most optimal to prevent
addresses duplication, but taking into account network vulnerability to destructive
factors, this solution is not acceptable. Therefore, for the netcentric Ad-hoc networks
it is recommended to use of stateless mechanisms with emphasis on the Ad-Hoc
IP Address Autoconfiguration mechanism.
A. Ad-Hoc IP Address Autoconfiguration
The Ad-Hoc IP Address Autoconfiguration mechanism combines mechanisms
SDAD (Strong Duplicate Address Detection) and WDAD (Weak Duplicate Address Detection). In this way, the duplicate address detection mechanism checks
duplication occurence during the initialization of the node, and detect and solve
duplicated addresses by analysis of routing messages in intermediary nodes.
The combination of two mechanisms allows continuously operate for nodes during
network splitting and merging. As in WDAD, each node selects the key of 128 bits
length and appends it to the routing protocol control packets. Intermediary nodes
must retain the key value for each address in the routing table or cache. Automatic
configuration procedure is exactly the same as in the SDAD mechanism. When
a node receives a routing packet, examines all the IP addresses and keys values
(contained in this packet) and compares it with the addresses and keys contained
in the address table or cache. If more than one key value will be found for the IP
address, the conflict of addresses is stated. In this case the node sends the unicast
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error message to the address with lower value key. During normal operation,
if the node receives information about the error of his address, it releases this address and starts reconfiguring procedure to obtain a new IP address. In the next
subsections is presented idea of the WDAD and SDAD mechanisms.
B. Strong Duplicate Address Detection (SDAD)
SDAD mechanism is the basis for all stateless solutions. It consists of a simple
mechanism that allows an Ad-Hoc node to select IP address and then check to
if it is already used by another node. Although, SDAD produces some problems
and limitations. Performing of duplicate address detection procedure is limited to the nodes initialization phase. So, for some reasons (such as temporary
loss of connection to network), autoconfiguration process leads to duplication
of addresses, and the network is not able to function properly. This protocol
does not guarantee the uniqueness of the network addresses and the probability of address duplication increases with the size of the network. In addition,
the protocol generates a lot of traffic – every joining node sends a few broadcast
packets in flood mode.
C. Weak Duplicate Address Detection (WDAD)
The WDAD mechanism is intended to extend the mechanism for detecting
duplicate addresses on whole life cycle of the network. The idea is that duplicate
addresses can be tolerated as long as packets reach the destination node indicated
by the sender, even if the destination address is used by another node. Therefore,
each node selects an identification key using for the routing protocol to distinguish
the potential duplicate IP addresses.
The main disadvantage of WDAD mechanism is its dependence on routing
protocol. The WDAD requires some changes in the routing layer to support the key
identifier of the node. In the routing layer, each node is identified by a virtual address involving the combination of IP address and key value. In addition, WDAD
duplicate addresses detection relies on local routing information, which makes
it completely adaptable only to proactive routing (each node uses a full routing
table). In case of reactive routing, the possibility of duplicate address detection
in a realitvely short time is reduced – delays increase. WDAD does not generate
additional traffic caused by the automatic configuration mechanism, but produces
the overhead of routing protocol packets.
III. Identification and authorization mechanisms
The security issues are carried out in two phases: authentication of participants of data exchange session, data authentication, and integrity guarantee. This
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article presents the mechanisms used in the first stage. There can be distinguished
mechanisms for different functional purposes, as follow:
• identification,
• authentication,
• authorization.
The user authenticity checking process and giving the authority for a specific set
of services is carried out by subsequent performing of the mechanisms mentioned
above. First of all must be carried out user identification, what provides recognition
of the unique features assigned to a person or object. This process allows to identify
user or object of automated data processing system. The result of the identification
process is so-called electronic identity. The next step is to carry out the authentication process, which aim is to provide a correlation of the electronic identity with
real world. The aim of the authentication process is to ensure at least one of the two
parties of data exchange about the identity of the other. After successfully authenticating the parties of data exchange session, the authorization process is executed.
The aim is to give the user or program or process appropriate permissions (access
and resource use rights) associated with the authenticated part.
The following chapters are presented mechanisms used in the WLAN, WMAN
and WPAN network techniques.
A. IEEE 802.11
The IEEE802.11 technique as the most often used, has a number of mechanisms proposals:
• MAC filtration,
• WEP (Wired Equivalent Privacy),
• EAP (Extensible Authentication Protocol) and 802.1X,
• WPA/WPA2 (WIFI Protected Access).
The most powerful security mechanism used in the WLAN is WPA and WPA2
(an upgraded version). It is also recommended to use in Ad-hoc networks dedicated to
support netcentric operations. With regard to identification and authentication, both
protocol versions use the same mechanisms. The difference is only in used to the encryption data protocol. The first version uses the TKIP protocol, while the second – AES.
WPA (WiFi Protected Access) is the successive WEP mechanism and provides
implementation of safety procedures at a higher level. It inherits from its predecessors some parts of properties and also introduces new, such as data encryption or
integrity checking mechanisms. It is assumed that the WPA2 mechanism (IEEE
802.11i) is a combination of: WPA2 = 802.1x + EAP + AES + CCMP.
The EAP (Extensible Authentication Protocol), described in recommendation
RFC2284, was intended to authenticate endpoints of PPP connection for remote
access to network resources. The objective of this standard is to control network
access, based on ports.
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In terms of authentication, 802.11i mechanism implemented two policies,
namely:
• WPA Personal – often denoted as WPA-PSK (Pre-Shared Key), where
both parties identify together and encrypt data using previously set key of
256-bit length,
• WPA-Enterprise – is dedicated to the larger networks, where the station
authentication is carried out using the RADIUS server and mechanisms
described in 802.1X recommendation.
This standard also set a new security architecture – RSN (Robust Security
Network). In this architecture the process of secure connection establishing is divided into four phases: agreeing of security policy, authentication, key generation
and distribution, and safe data exchange ensuring data integrity and confidentiality.
From the netcentric operations point of view, the WPA2-PSK mechanism seems
to be optimal, because there is no need to use network authentication server. In case
of WPA2-Enterprise mode, each new station that joining to network or disconnected
and connected (due to changes in propagation) is obligated to authenticate with
entity (authentication server), which can be inaccessible due to a server crash. For
this reason, in 802.11 networks should be applied WPA2 Pre Shared Key mechanism,
taking into account all the principles of key material distribution and protection.
In this case, the PMK (Pairwise Master Key) used to determine the PTK (Pairwise
Transient Key), during the 4-way handshake, is determined on the basis of three
values: SSID, SSID length and shared secrets – WPA password.
B. IEEE 802.15 series
1) IEEE 802.15.4
The ZigBee system architecture developed by the ZigBee Alliance is based on
the physical layer PHY and data link layer – MAC layer specification, as is described
in Recommendation 802.15.4:2004 and the later version 802.15.4:2006. In the structure is the network coordinator. This entity has full functionality in the star structure
taking part in relay of data between devices. In others, network coordinator functions are limited to determining the basic parameters of the network.
In terms of secure communication, the ZigBee can operate in several modes:
open mode without security mechanisms included, only authentication and integrity
check – AES-CBC-MAC, only encryption – AES-CTR, and authentication, integrity
check and encryption AES-CCM. The authorization is thus realized in two above
modes, and is made in the data link layer MAC. This is performing by calculating
a common secret (MIC – Media Integrity Code) using CBC-MAC mechanism
basing on a common cryptographic key held by cooperating stations. This secret
is calculated based on a symmetric key (known by two parties of data exchange
session) and the bits in the entire data frame (including the header and data field).
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Therefore, apart from the sender and recipient authentication, there is the frame
integrity check performed. The MIC code is transmitted in each 802.15.4 frame
except confirmations frames – ACK.
2) IEEE 802.15.3
The 802.15.3 networks are organized in a picocells (called piconet), are managed by the picocell supervisor PNC (PicoNet Controller). PNC is selected from
the terminals located in the area of mutual radio range. PNC is responsible for
synchronizing the piconet elements (using “beacon”), management of QoS parameters, power saving modes, and network access control. PNC also performs security
functions relating to all piconet members. Secure relationships DEV-DEV can be
created independently, without the use of PNC.
The user’s identification, authentication and authorization are done in a similar way as for 802.15.4 networks, using CBC-MAC algorithm. It is realized based
on knowledge of the symmetric key by both parties of exchanging data session
and the “nonce” value, which carrying the identifiers of the sender and recipient.
The last block of CBC-MAC algorithm result is a message integrity code calculated
on the basis of identification data and common cryptographic key. In the 802.15.3
networks, all the messages (including the beacon message) are authorized.
3) IEEE 802.15.1
IEEE 802.15.1 networks are organized in piconets, managed by the terminal
selected as the Master, which oversees the complex piconet consist of up to 255 slave
devices, where only up to 7 may have an active state. Communication between slave
devices always takes place through the master device. Security (encryption and authentication) is done through a pairing process for secure communication between
terminals, using a cryptographic key for the SAFER+ algorithm (algorithm E).
In the first phase of the pairing, initial key KINIT (Initialization Key) is generated
using the shared 128-bits pseudorandom string (IN_RAND) and PIN number (up
to 128 bits of length). If the PIN is shorter, is completed with the hardware address
of the master device (BD_ADDR). Figure 1 shows the key generation process.
Figure 1. KINIT key negotiation from PIN number
Than, the devices encrypts the data carrying in time slots and carry out negotiations of new 128-bits cryptographic key KLINK – Link Key. This key is stored
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by the devices and is used to encrypt the proper data exchange. The possession
of this key by a pair of devices is equivalent to their logical association (pairing).
The last stage is authentication of pairing devices – Figure 2.
Figure 2. Authentication process.
It involves the generation of SRES’ word, which is compared with the SRES
recalculated locally (the original). The authentication process is as follows:
1. Station A (authenticator), sends 128-bits pseudo-random sequence as a clear
text to station B AU_RANDA (authenticated).
2. Station B using the E1 algorithm calculates the 32-bits SRES’ word based
on its hardware address (the remaining 96 bits (ACO) are used to calculate
the encryption key), KLINK cryptographic key and the AU_RANDA word.
3. Station B sends the SRES’ to station A.
4. Station A calculates locally the SRES word and compares it with
SRES’ – ? SRES=SRES’.
5. The positive result of comparison indicates a positive result of authentication station B by station A.
6. The process is repeated but station B is the authenticator.
C. IEEE 802.16
WiMAX (Worldwide Interoperability for Microwave Access – 802.16) is intended for the building of urban networks and MANs, were designed as part
of the last mile users access. WiMAX security architecture includes elements and
mechanisms such as stations digital signature (X.509), the security associations (SA),
encryption protocols, key management protocols (also responsible for confirming
the identity of co-operating stations).
Security Association is a logical link between two terminals (stations) and
consist of safety parameters, such as cryptographic keys, certificates, etc. Due to
the three-phased secure data exchange process between stations, there are two
basic types of association: authentication security association (Authorization
Security Association) and the data exchange security association (Data Security
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Association). The phases of secure data exchange process are: authentication, negotiation of cryptographic keys and encryption.
Authentication security associations are used during station authentication
stage and includes security parameters used for this stage:
• X.509 certificate used to identify the station,
• authorization key (AK – Authorization Key) used to generate the KEK
(Key Encryption Key) – KEK is a cryptographic key to encrypt the TEK
negotiation process (Traffic Encryption Key),
• hash – computed by HMAC-Digest function, used to data integrity check
during the negotiation of cryptographic material,
• sequence number of AK,
• the period validity of AK,
• association descriptor.
The station authorization phase is performed in four steps:
1. The connecting station sends authentication informations in AuthenticationInfMess, containing a manufacturer certificate, to verify its credibility.
2. Simultaneously, the station sends an authentication request in a AuthorizationReqMess message, which contains a request of authentication key
and security association descriptor. Also, there are send informations
of supported cryptographic algorithms, data authentication mechanisms
and connection identifier BCID (Basic Connection ID). BCID is assigned
by the station B during the initial phase of connection establishing (Initial
Ranging). Station A is authorized after its certificate confirmation.
3. Station B sets security association descriptors (the initial association
properties / Primary SA / and existing static association / Static SA /).
Then activates the authentication key and sends the response to station
A in the AuthorizationRepMess, containing :
a. AK key (encrypted by public key of station A),
b. 4-bits AK key number used to distinguish the following keys,
c. validity period of AK key,
d. SA descriptors – previously established
4. In the last phase is calculated the cryptographic key KEK and message authentication keys (HMAC_Key_D and HMAC_Key_U) based on key AK.
They will be used during the negotiation phase of TEK keys. Because the AK
key has a limited period of validity than the station should periodically
renew the authorization process by sending an AuthenticationReqMess,
before the AK validity period expires.
The figure 3 shows that the station identification and authentication process
is carried out in two stages of the symmetric and asymmetric encryption, using
PKM protocol version 2. This protocol is responsible for normal and cyclic station
authentication processes and exchange of cryptographic material. This protocol
operates in a client/server mode. The station identification is performed basing
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on X.509 certificates. Each WiMAX station has the X.509 certificate issued and
implemented by the equipment manufacturer.
Figure 3. IEEE 802.16 authorization phase
IV. Adaptative ad-hoc technologies
Network centric operations requires such Ad-hoc networks organization that
provide the conditions for data exchange as far as possible to guarantee reliable delivery of informations and ensure accuracy of the transmissions.
In order to fulfill these conditions, technologies used for wireless networks
must meet two fundamental conditions:
– Support as far as possible creating and maintaining a Ad-hoc network
topology;
– Offer the mechanisms of adaptation to terrain conditions, propagation, the nature of the end-user services and quality requirements for
these services and types of users of such networks (mobile, nomadic,
stationary).
With regard to the abovementioned factors was carried out analysis of network techniques enabling the building of Ad-hoc networks and adaptation to
the changing conditions in a radio channel. The analysis concerned the network
technologies standardized within the IEEE and covered standards for the Personal
Area (WPAN), Local (LAN) and Metropolitan Network (WMAN).
A. IEEE 802.11
WLAN wireless technologies standardized within the IEEE 802.11 group is currently the most prevalent. WLANs based on IEEE 802.11 can be found in a number
of private and commercial installations.
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Originally, it was developed version labeled as 802.11 operating in 2,4 GHz
ISM band. However, due to the growing needs of the telecommunications market
and technological development have been designed subsequent versions of this
technique. Currently, as WLAN solutions are used four base protocols prepared by
IEEE 802.11 group (a, b, g, n) specifying the encoding of radio signals, the operating
bands and offered rate. Original version is currently not used.
Table I. IEEE 802.11 standards [3]
Name
802.11
Max.
Bandwidth
bitrate
[GHz]
[Mbit/s]
2
2,4
Modulation
FHSS, DSSS, IR
Supported
Range
MIMO
[m]
streams
1
100
Publication/
comments
June 1997
802.11a
54
5
OFDM
1
120
September 1999
802.11b
11
2,4
HR-DSSS, CCK
1
140
September 1999
802.11g
54
2,4
HR-DSSS, CCK,
OFDM
1
140
June 2003
compatible downwards
with 802.11b,
802.11n
540
2,4 or 5
OFDM
4
250
November 2009
Significant role for high transmission rate ensuring have OFDM modulation,
which allows more efficient use of radio spectrum. The IEEE 802.11n extension
allows obtaining higher speeds and transmission ranges by using MIMO technology (Multiple-Input Multiple-Output). For MIMO technology are used more
than one antenna transmission or reception and a special encoding techniques.
WLANs based on 802.11 can run in infrastructure mode, where users
communicate with each other through the AP (Access Point) or independent,
in Ad-hoc mode. The main directions of development of the 802.11 series
of standards focused on solutions for network infrastructure, while working
in Ad-hoc mode was not considered or undertaken works in this direction are
on a limited scale.
Prevalence of use cards compliant with the IEEE 802.11 standard for office
and home has made the necessary research on the use of these technologies for
building and organization of wireless networks in Ad-hoc mode.
Comprehensive solution for this type of network is proposed in extension IEEE
802.11s – “ESS – Extended Service Set Mesh Networking”. The solution assumes
the possibility of creating a self-configuring 802.11 wireless network operating
in Ad-hoc mode, where each device can have several interfaces. One of the basic
assumptions of the developed solution was to provide extensibility and flexibility,
consisting of applying a variety of mechanisms to fulfill the same functions in different nodes of the network.
For IEEE 802.11s standard assumed use of the underlying mechanisms for
the physical layer, the security of information and access to the transmission
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medium used in previous versions of IEEE 802.11. Additionally, assumes the use
of new, additional solutions. The required functionality described by the IEEE
802.11s includes:
a) PHY – Physical Layer, based on the IEEE 802.11a /b/g/n.
b)Medium Access Coordination – mechanisms for medium access control,
based on standard solutions adopted for the network compatible with IEEE
802.11 with QoS extensions described in the IEEE 802.11e. To further
use are expected new features such as the ability to dynamically change
the operating channel.
c) Mesh Security – authentication of users, nodes and trunks cryptographic
protection, in large part based on the extension of EEE 802.11i.
d)Mesh Configuration & Management – mechanisms for automatic configuration of radio parameters (operating channel frequency selection,
transmit power, etc.), QoS management policies.
e) Discovery & Asscociation – mechanisms for detecting the presence of neighboring nodes and mesh networks and to identify parameters of mesh network, the procedures for connecting to the network nodes and the statement
of logical links with its neighbors.
f) Mesh Topology, Learning, Routing & Forwarding – a group of mechanisms
and protocols for the control and the creation of the current topology, and
data forwarding, it is assumed the use the Hybrid Wireless Mesh Protocol
(HWMPA) for routing, which is a combination of mandatory reactive
protocol mechanisms Ad-hoc Radio Metric on-demand Distance Vector
(RM-AODV), and optional proactive mechanisms – Tree Based Routing
(TBR). It is also assumed to use a proactive routing protocol Metric Radio
Optimized Link State Routing (RM-OLSR).
g)Internetworking – mechanisms for ensuring mesh network cooperation
with external 802 series networks, including the wired networks.
h)Mesh Measurement – mechanisms and procedures for monitoring the mesh
network and radio environment, e.g. for setting routes in mesh network.
In this standard was assumed using of several types of mesh network nodes,
which can change operation mode during its functionality. They are:
a) Mesh Point (MP) – this is the basic type of the node in the network 802.11s.
It is used to carry out the procedures for establishing and maintaining
networks. Arranges and maintains the logical links of detected neighbors
and forward transit traffic.
b)Mesh Access Point (MAP) – an extended version of the node MP, also
supports the function of an access point (AP) to support standard WLAN
client station (not supporting 802.11s standard).
c)Mesh Portal Point (MPP) – used to work as node in mesh networks to
the external networks compatible with the IEEE 802 (wired, wireless,
and also compatible with the 802.11s). In addition to features for the MP
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can perform the functions of the bridge (network bridge) and support
a proactive routing within their own mesh network.
d)Lightweight Mesh Point (LWMP) – nodes implementing the functionality
of a Mesh Point node only in an environment of mutual audibility of nodes
(as the classic WLAN nodes in Ad-hoc mode).
e) Client Station (STA) – a classic client station designed to work with the AP
and mechanisms not supporting 802.11s standard.
For each of these nodes is assumed that it will work as terminal and offer users’ access to services at the OSI application layer.
B. IEEE 802.16
Standard for wireless metropolitan structures WMAN defined by a group
of IEEE 802.16. Mobile WiMAX provides support for stationary equipment,
nomadic and full mobility for the devices carried or transported at speeds up to
120 km/h. This functionality of WiMAX technology is extremely important for
dynamic operation conducted with the use of vehicles.
Assuming the possibility of using battery-powered portable devices IEEE
802.16e authors have developed the two modes operation of subscriber stations
for energy conservation, that are: idle and sleep mode.
IEEE 802.16e introduces the possibility of scalable bandwidth utilization
of the radio channel from 1.25 MHz to 20 MHz, with modulation SOFDMA
(Scalable Orthogonal Frequency Division Multiple Access), where the number
of subcarriers varies with the width of the channel. This property, greatly increases
the feasibility of efficient transmission with high speeds.
Mobile WiMAX is ready to use multipath reception. For this purpose are
used OFDM modulation and MIMO (Multiple Input – Multiple Output) solutions,
involving the use of multiple antennas on the receiving and transmitting sites.
It has a great importance in the case of communication systems in urban areas,
where the multipath phenomenon is the most noticeable.
In response to the possibility of variable propagation conditions experienced
by mobile subscribers, the IEEE 802.16e introduces the hybrid automatic repeat
request H-ARQ (Hybrid Automatic Repeat called Request), where the first correction coding is used, the detection coding (used for ARQ) can be used, if necessary,
in the next step.
IEEE 802.16e also implies the possibility of using smart antennas, where
the adaptive gain control of selected direction of the transmission is used. It can increase the useful range and reduce interference.
Current implementation of WiMAX technology in the great majority basing
on the solutions with the use of base stations. The creators of the IEEE 802.16 standard established the possibility of network functioning without using base stations.
Devices working in cooperation mode on the network (without the use of WiMAX
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301
base stations) has been identified as a “mesh” mode and presented in the extension of IEEE 802.16, and then included in the version IEEE 802.16-2004 (extension “a”
has been incorporated into the version of 2004). Mesh mode can also be used in cooperation of base stations. In this mode, client stations are referred as MSS (mesh
subscriber station), and base stations as MBS (mesh base station). In the mesh
mode, unlike the communication mode managed by the base station, where
in the transmission frame structure is distinguished direction from the base station (downlink) and up to her (uplink), transmission between mesh networks
elements (MSS or BSS) is carried out as bi-directional which is established during
initialization of the SS.
These links are described by an 8-bits connection identifier (Link ID). If the data
is transmitted in the direction to the MSS located closer to the MBS, traffic is treated
as uplink traffic, otherwise it is seen as the downlink channel. The mesh mode uses
three methods of data transmission known as a method of scheduling:
• coordinated distributed scheduling;
• uncoordinated distributed scheduling;
• centralized scheduling.
In the case of distributed scheduling, each node transmits the current and
proposed schedule of data transmission to neighboring nodes in one hop distance.
If the destination node provides support for data transfer according to this schedule,
it responds to the source node in the control subframe slot. Ultimately the source
node sends to destination node acknowledgment of receipt of approval for transmission according to the desired schedule. For distributed coordinated scheduling,
the scheduling messages are sent with using a scheme providing collision avoidance.
Centralized scheduling method is similar to mode where the transmission
is directly managed by the base station. In this mode, the MSS points are used only
as relay stations to the nearest MBS.
Described in IEEE 802.16-2004 mesh mode is not included in the extension
known as the IEEE 802.16e. Due to the fact that the use of mobile WiMAX technology requires other devices than based on the IEEE 802.16d, mesh mode is not
supported by network equipment manufacturers for the IEEE 802.16e. However,
are created theoretical attempts to modify the IEEE 802.16e devices for mesh mode.
One of the proposals to use mesh mode in a mobile version of WiMAX has been
presented for the tactical communications networks in [4].
The most recent proposal to improve the functionality of mobile WiMAX
subscriber station is in extension IEEE 802.16j, which introduces the concept
of a relay station – MRS (multihop relay station). MRS perform a similar function
as the MSS in a centralized mode, passing data from other SS subscriber station to
base station BS, in the case of absence of direct contact SS to BS. MRS is used also
in the IEEE 802.16m version (Advanced Air Interface with data rates of 100 Mbit/s
mobile & 1 Gbit/s fixed), known as WiMAX2 designed to provide transmission
rates up to 1 Gbit/s.
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C. IEEE 802.15
Personal Area Networks (WPAN) are created to provide a wireless user access
to facilities and services provided by them and co-operation devices in the network.
Due to their use, a network device must be simple to use, characterized by small
size and weight and low power consumption. The most popular standard for WPAN
is defined in the IEEE 802.15 group. These are the techniques: Bluetooth (IEEE
802.15.1). UWB (IEEE 802.15.3), ZigBee (802.15.4). In Table II, developed in [1],
are presented the most important properties of these techniques.
Table II. Properties of WPAN technologies
Bluetooth
Bandwidth
2,4 GHz
Modulation
GFSK,
π/4-DQPSK,
8DPSK
Channel
Access
Bitrate
Range [m]
802.15.3
UWB Forum
3,1-10,6 GHz
3,1-4,85 GHz,
6,2-9,7 GHz
QPSK, DQPSK, 16-QAM,
32-QAM, 64-QAM
Polling, masterCSMA-CA,
OFDM,
-slave, TDD (Time
Division Duplex) optionally TDD
21 kbit/s do
40 Mbit/s
802.15.4
WiMedia
53,3, 80, 110,
160, 200, 320,
400, 480 Mbit/s
standard
ZigBee
2,4 GHz and 868/915 MHz
DSSS with BPSK or MSK
No data
CSMA/CA and guaranteed
time slots GTS in superframes
< 2 Gbit/s
2-250 kbit/s
0,1-100
< 10
1-100
Transmiter
Power
< 100 mW
Bandwidth depended
<0,074 mW/GHz
> 1 mW
Network
topologies
Piconet,
scatternet, mesh
Piconet, cluster,
peer-to-peer, mesh
QoS
SDP
(Service Discovery Guaranteed time slots (CFP)
Protocol)
Security
SAFER+,
authentication,
encryption
Usability
Dedicated
applications
AES-128,
key negotiation
No data
Consumer electronics without
wiring, home networks
Star,
peer-to-peer,
Star,
cluster, mesh
Guaranteed time slots
AES-128
AES-128,
authentication
Sensor networks,
automation systems
1) UWB (IEEE 802.15.3)
The IEEE 802.15.3 specification defines the physical layer and data link of high
speed (high rate) WPANs (Wireless Personal Area Network), that offers above
20 Mbit/s data rate.
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The basic element of the IEEE 802.15.3 wireless network is piconet, defined
as a wireless communication system, operating in Ad-hoc mode, which allows
mutual communication of many independent devices. It is assumed that the size
of a typical piconet covers an area with a radius of 10 m. The IEEE 802.15.3 standard defines several functional elements in piconet structure, shown in Figure 4.
Figure 4. IEEE 802.15.3 piconet elements [11]
The basic component of this structure is the data transfer object – DEV, which
in general should be identified as a terminal. DEV can also be a group of devices
in one location. In piconet, the one DEV element must act as PNC (Piconet Coordinator). PNC is responsible for synchronizing the piconet elements (using
“beacon” messages), the management of QoS parameters, power saving modes,
and network access control. PNC also performs security functions relating to all
piconet members.
Piconet is not a spatial structure, but rather logical. Crucial in the piconet
creation have PNC. The precondition, which is adopted for the piconet existence
is DEV act as PNC sending a messages “beacon” containing the information required to piconet maintain. The process of piconet creation is done by sending
joining notification to the PNC, by the DEV in the surrounding area, interested
in piconet membership. Standard assumes dynamic piconet membership for DEV.
DEV can attach and detach during the transmission. In situations where the device
acts as a PNC leaves piconet, must be designated the new PNC.
Standard provides parallel operation of different piconets. DEV can belong
to the parent piconet, where have guaranteed access to bandwidth, offspring
piconet (where piconet is managed by the unit included in the home network),
and the neighboring piconet when it is managed by the device, that there have no
contact with home piconet.
Messages between piconets are sent in superframe, in frames transmitted
during the transmission time labeled CFP (Contention Free Period). As addresses
in the so-constructed network are proposed to use the PNID (Piconet ID) identifiers,
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and device IDs DevID (Device ID). To avoid ambiguity in the allocation of these
addresses is assumed to use the parent element defined as MC (mesh PNC).
2) Bluetooth (IEEE 802.15.1)
Bluetooth is a standard for wireless networks described by the recommendation
IEEE 802.15.1. To work using unlicensed 2.4 GHz ISM band. Provides transmission
range in open space defined by three transmit power classes respectively: 100, 10
and 1 meter. Depending on the version of the standard offer bit rate of 21 kbit/s
for 1.0 to 40 Mbit/s for version 3.1.
The technique enables “point-to-point” and “point-to-multipoint” connection
in Ad-hoc mode for devices on a small area. The 802.15.1 standard define Bluetooth
profiles i.e. scenarios for the using Bluetooth technology. Profile supports handling
application-layer data and voice, print data, access to local and global networks,
in uniform way.
The standard assumes that the communication between Bluetooth devices
is carried out in the logical structure as for the IEEE 802.15.3 i.e. piconet. It is
assumed that at the same time can be active seven slaves and one master. Status
of the master unit performs machine that initiates the process of creating a network.
Data exchange can take place only between the parent node (master) and slave
(slave). Direct data exchange between slave devices is not possible.
In addition, devices can participate in piconet in power save mode-Park, except
seven active slaves. By enabling and disabling the device in this mode, the master
can provide communication for multiple devices. To ensure cooperation between
piconets the ability of working in bridge mode was adopted. Thus, piconets can be
combined into larger structures called scatternet. The IEEE 802.15.1 standard does
not define the principles of scatternet creation. There were designed only custom
proposals including Bluetrees, Bluestar, Bluemesh.
3) ZigBee (IEEE 802.15.4)
IEEE 802.15.4 was developed for sensor networks. In relation to the Bluetooth
and UWB offers a relatively low data rate (up to 250 kbit/s). The standards assumed
the ability to create complex network topologies. IEEE 802.15.4 standard defines
the format of the physical and data link layer. Based on these recommendations,
the organization ZigBee Alliance (assembling more than 150 companies) extended
the functionality of this solution by introducing a standard named ZigBee, which
also includes recommendations for network and application layers.
ZigBee defines three types of devices that can be used to create network connections. These include the following types:
• The coordinator – the parent node of ZigBee network;
• Router – a node that can be used to create network and realizing routing
and transit data functions;
• Terminal – devices that do not offer the data relay functionality.
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The standard defines the following elements:
• Application Object (APO) – endpoints (processes) that work together and
carrying out certain tasks utility. It was assumed ability to run up to 240
objects in each of the nodes. APOs can work together within a single node,
and through the network infrastructure;
• ZigBee Default Object (ZDO) – the objects responsible for network maintaining. Determines the type of device (coordinator, router, terminal
equipment), discovers the environment, network management, creates
associations between the APO at different nodes;
• Application Support Sublayer (APS) – responsible for APOs communication
in the network and maintain established (using ZDO) network connections
between the APOs;
• Network Layer (NWK) – uses the functions provided by the data link
layer. NWK is used in the processes for the network addresses allocation,
routing, attaching and removal of network nodes.
• Security Service Provider (SSP) – provides mechanisms for the cryptographic protection for APS and NWK layer;
In the network, ZigBee addresses are determined hierarchically, from the coordinator. ZigBee network routes are designated in reactive manner. Source node
sends request about the destination node to the nearest neighboring node. The request is advertised until it reaches the destination. Routing informations are stored
in intermediate nodes, and can be used for future transmissions.
It may be noted that the greatest support for the operation of wireless devices
in a network organized in Ad-hoc mode are WPANs networks. Due to their short
range and requirements to reduce the cost of specialized devices, do not use superior
devices specialized in managing the wireless infrastructure.
V. Conclusions
In article are presented the mechanisms and communication technologies of Ad-hoc networks using for net centric operations. The analysis indicates that the most appropriate is IEEE802.11s technology with MIMO
antennas and modern coding techniques. Taking into account the applications of this technique – NCW, it is necessary to use WPA2-PSK and
Ad-hoc IP Address Authoconfiguration mechanisms. The next step of our work is to
perform simulations experiments and than build a technology demonstrator.
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References
[1] K. Gierłowski, T. Klajbor, J. Woźniak, “Analiza sieci bezprzewodowych serii IEEE
802.15.x – Bluetooth (BT), UWB i ZigBee – z transmisją wieloetapową. Część I”,
Przegląd Telekomunikacyjny. Wiadomości Telekomunikacyjne. 2008, nr 10.
[2] K. Gierłowski, T. Klajbor, J. Woźniak, “Analiza sieci bezprzewodowych serii IEEE
802.15.x – Bluetooth (BT), UWB i ZigBee – z transmisją wieloetapową. Część II”,
Przegląd Telekomunikacyjny. Wiadomości Telekomunikacyjne. 2008, nr 11.
[3] K. Gierłowski, J. Woźniak, “Analiza szerokopasmowych sieci bezprzewodowych
serii IEEE 802.11 i 16 (WiFi i WiMAX) z transmisją wieloetapową”, Przegląd
Telekomunikacyjny. Wiadomości Telekomunikacyjne. 2008, nr 8-9.
[4] BRYON KEITH HARTZOG, “WiMAX potential commercial off – the – shelf solution
for tactical mobile mesh communications”, Military Communications Conference,
2006. MILCOM 2006.
[5] Evren Eren, Kai-Oliver Detken, “WiMAX-Security – Assessment of the Security
Mechanisms in IEEE 802.16d/e”, WMSCI 2008.
[6] IEEE Std 802.15.3b-2005, “Wireless Medium Access Control (MAC) and Physical
Layer (PHY) Specifications for High Rate Wireless Personal Area Networks (WPANs)”.
[7] Naveen Sastry, David Wagner, “Security Considerations for IEEE 802.15.4
Networks”, University of California.
[8] J. Jeong, J. Park, H. Kim and D. Kim, “Ad Hoc IP Address Autoconfiguration”,
I-D draft-jeong-adhoc-ip-addr-autoconf-02.txt, February 2004.
[9] P. Paakkonen, M. Rantonen and J. Latvakoski, “IPv6 addressing in a heterogeneous
MANET-network”, I-D draft-paakkonen-addressing-htr-manet-00.txt, December
2003.
[10] C. Jelger, T. Noel, and A. Frey, “Gateway and address autoconfiguration for IPv6
adhoc networks”, I-D draft-jelger-manet-gateway-autoconf-v6-02.txt, April 2004.
[11] IEEE Standards, “IEEE Standard for Information technology – Telecommunications
and information exchange between systems – Local and metropolitan area networks
– Specific requirements. Part 15.3: Wireless Medium Access Control (MAC) and
Physical Layer (PHY). Specifications for High Rate Wireless Personal Area Networks
(WPANs).”, IEEE Std 802.15.3™-2003, 29 September 2003.
Using Network Coding in 6LoWPAN WSNs
Jarosław Krygier
Military University of Technology, Faculty of Electronics,
Telecommunications Institute, Warsaw, Poland, [email protected]
Abstract: The low power wireless sensor devices which usually uses the low power wireless private
area network (IEEE 802.15.4) standard are being widely deployed for various purposes and in different scenarios both in civil and military world. IPv6 low power wireless private area network
(6LoWPAN) was adopted as part of the IETF standard for the wireless sensor devices in order to
decrease the IP overhead and to adapt big IPv6 packets to small maximum transmission unit (MTU)
offered by the IEEE 802.15.4 standard. This paper is focused on the adoption of the network coding
to 6LoWPAN/IEEE 802.15.4 network. The solution based on COPE mechanism is proposed and
described here. Also the implementation problems are discussed.
Keywords: component; network codding, WSN, 6LoWPAN
I. Introduction
Tactical communication networks are evolving toward complex heterogeneous
ad-hoc networks where mobile nodes can simultaneously embed very different kinds
of communication technologies such as HF or VHF tactical radios (including legacy
systems), UHF (microwave) ad-hoc radios, lightweight satellite ground stations and
also wireless sensor networks (WSNs) [1]. Considering the mobile and temporary
nature of military missions, developing sensor networks that operate in wireless
mode are a necessity. These conditions pose a number of challenges comprising
sensor hardware platforms, communications and networks, and information management systems. In order to cope with a wide heterogeneity of hardware devices
as well as to meet the requirements on the end-to-end communication (including
sensor networks), the Internet Protocol (IP) should be applied in the network elements – also in highly mobile and sensor part of the network [9].
WSNs are currently the most common type of so called Low-power Wireless
Personal Area Network LoWPAN (LoWPAN), which are self-configuring ad-hoc
networks composed of wirelessly connected, autonomous devices, usually characterized by constrained computational and memory resources and low consumption.
On the other hand, the LoWPAN tiny nodes have to be equipped with the IP protocol. Unfortunately, the IP traffic causes significant overhead, which is especially
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relevant in WSNs and military mobile ad-hoc networks (in low data rates modes).
In order to adapt the IP to the LoWPANs some solutions are proposed, from which
the most commonly used are: uIP (micro IP), nano IP and IPv6 low power wireless
private area network (6LoWPAN). 6LoWPAN was adopted as the part of the IETF
standard for the sensor devices [2]. Although the 6LoWPAN is not restricted to
the IEEE 802.15.4-based devices, but it is commonly used with such standard. It is
also proposed for tactical networks in [3].
The 6LoWPAN significantly reduces the IPv6 overhead in the network using
header compression mechanisms, but additional reduction is required in order
to transfer high amount of data across the sensor network effectively. Meanwhile,
network coding has recently emerged as an effective strategy to provide significant
performance improvements in wireless networks. Many researchers have shown
that by applying network coding in wireless networks we can improve the network
throughput and reliability, minimize energy and decrease the network congestion [4] [5] [6]. Most of the network coding solutions have been verified using
mathematical analysis and by the simulation experiments, while modest amount
have been implemented and tested in limited network environment. Moreover,
the network coding theory can be applied practically in each layer of the communications protocols stack. Also most of the solutions concern the physical (PHY),
medium access control (MAC) and the application (APPL) layers. This paper
is focused on application of the network coding theory in the 6LoWPAN-based
system, taking into account the network (NET), 6LoWPAN adaptation (AL) and
MAC layers, while the wireless links are built using the IEEE 802.15.4 standard.
Proposed solution is tailored to the Contiki 6LoWPAN and MAC implementation
run on the AVR RAVEN sensor motes [10].
The remainder of the paper is organized as follows. Section II presents the concept of the network coding mechanism for 6LoWPAN. Section III explains the implementation details. Description of the tests and the results discussion is performed
in section IV. General conclusions and future work plans are given in section V.
II. Concept of network coding for 6LoWPAN/IEEE 802.15.4
network
Network coding is known to improve network throughput by mixing information from different flows and conveying more information in each transmission. Though the idea of network coding is not new, in the past, it has been
applied mainly in the context of multicasting in traditional wired networks. It is
because the data packets are sent using multicast and broadcast addresses and they
can visit many intermediate nodes in the same time, and than some streams often
meet together and can be mixed (coded) and decoded in other nodes. Wireless
networks are equipped with similar features, where sent frames can be received by
many nodes simultaneously, because of the broadcast nature of the transmission
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
309
medium. In order to shortly explain the idea of the network coding in a wireless multihop network let us assume simple scenario composed of three wireless
nodes illustrated in Fig. 1 [8]. Nodes 1 and 3 have to send their packets (‘a’ and ‘b’
respectively). Packet ‘a’ has to be delivered to node 3 and packet ‘b’ has to reach
node 1. The left picture shows transfer of the packets without network coding. Each
node performs standard packet transmission, know as store and forward. It means
that to transfer those two packets from the source to the destination, four wireless
transmissions are required. If the network coding is employed (right picture), after
receiving both packets (‘a’ and ‘b’) by node 2, it can transmit a single coded packet
(a Å b) in such a manner that both destination nodes can receive it. It is possible
because of the broadcast nature of the wireless network. Two packets are coded
using simple xor function. It means that in order to extract one native (original)
packet from coded packet the receiver needs the second packet. For example,
the node 3 is able to extract packet ‘a’ from packet ‘a Å b’ if it stores the packet ‘b’
(a = aÅbÅb). This exchange reduces the total number of wireless transmission
from 4 to 3. Generally, network coding can be based on linear composition of two
or more native packets [7].
Figure 1. An example of information exchange with network coding
A practical scheme, referred to as COPE (Coding Opportunistically), based
on the network coding for wireless networks is proposed in [6]. The solution tailored to the 6LoWPAN network presented in this paper is based on COPE. Each
node in the network has to store transmitted and received packets during predefined
time as shown at the network in Fig. 2. For example, node 2 should store transmitted packet P4 and received packets P3 and P5 (marked in rectangles).
Figure 2. Collecting of sent and received packets
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For simplicity, it was also assumed that packets will be coded using simple xor function and they can be extracted just in the next hope nodes. The native packets hidden in the coded packet can be extracted in each destination
node if the node has stored n-1 packets (where n – number of native packets included in the coded packet). It means that each node has also to store the table with
information about the packets stored in the neighboring nodes. Fig. 3. shows that
node 1 keeps the neighbor table. The entries in this table are collected based on
the packets identifiers constructed from the native packets source addresses and
sent in coded packets in additional header. If the data packets are not in the network
during some time, signaling packets should be used to inform neighboring nodes
about stored packets. If the sender sent a coded packet which is a composition of too
many native packets, some receivers could not extract their missing native packet
and they should be resent by the sender. Such operation increases the network load
and decreases the network coding gain.
Figure 3. Storing of network coding neighbor table in each node
The network coding gain in opportunistic coding scheme is achievable due
to, as it has been already mentioned, the broadcasting nature of the wireless network. Majority of MAC and PHY radio-based protocols make possible to transfer
and receive the broadcast frames. The same, considered IEEE 802.15.4 standard
gives an opportunity to broadcast traffic handling. Assuming that node 1 from
Fig. 4 wants to transfer the coded packet P1ÅP2ÅP4ÅP5, it ought to broadcast
the packet.
Unfortunately, broadcast transmission is not confirmed by the receivers in MAC
layer, than coded packet transmission is not reliable. Even if some mechanism
was employed in the network layer that can confirm the coded packets, it would
be significant, additional signaling traffic. If the transmission reliability is not rel-
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
311
evant or ensured by the upper layer protocols the coded packets can be sent using
broadcasting technique (as in Fig. 4).
Figure 4. Sending coded packet in broadcasting mode
As an alternative, the coded packet can be sent in unicast transmission to one
of the next hope node which confirms its reception using MAC acknowledgment
(MAC ACK) and the other nodes can receive the packet, interpret it but they cannot acknowledge its correct reception (as in Fig. 5).
Figure 5. Sending coded packet in unicast mode with MAC ACK
The third possibility is to confirm the coded packet on the fly. Fig. 6. shows
that node 2 confirms the coded packet using MAC ACK, but nodes 3 and 4 confirm reception of given coded packet while transferring its native or other coded
packets (NC_ACK).
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Figure 6. Sending coded packet in unicast mode with mixed acknowledgment
Tailoring the network coding scheme explained above to the 6LoWPAN/IEEE
802.15.4 network some assumptions are taken. Since the solution is dedicated to
sensor motes, it should not consume too much memory during implementation and
operation. It also should use both IEEE 802.15.4 MAC and network layer features
(6LoWPAN, uIP6 – microIPv6, RPL – Routing for Low Power and Lossy Networks,
IPv6 ND – Neighbor Discovery). Fig. 7 presents typical multihop transmission
of 6LoWPAN datagrams (compressed or fragmented packets) between node 1
and 3. Depending on the implementation, the 6LoWPAN packets are equipped with
the addresses (‘Src’, ‘Dest’) which comes from the IPv6 layer and can be carried inline
or can be extracted from the MAC addresses. Thus, from node 1 the 6LoWPAN
packet is sent to the IP ‘Dest’ address of the node 3, but the IEEE 802.15.4 frames
are equipped with the MAC ‘Src’ and ‘Dest’ addresses of node 1 and 2 respectively.
Both the data and ACK frames are also overheard by the neighboring nodes.
Figure 7. Typical multihop transmission in 6LoWPAN
Also depending on the 6LoWPAN implementation, the packets can be routed
in the IPv6 layer or forwarded in the 6LoWPAN layer. The first case is shown in Fig. 8,
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
313
where three nodes are represented by the layered architecture similar to those used
by the 6LoWPAN implementation in the Contiki operating system [10]. The application (Appl), routing (RPL) and neighbor discovery segments are sent using TCP/
IPv6 protocol (uIP6 implementation) and routed in the intermediate node using
tiny IPv6 routing table, collected by the RPL protocol. The 6LoWPAN layer is supported also by the IPv6 neighbor discovery which delivers the neighboring nodes
MAC addresses and IPv6 addresses (string them in the ND cache).
Figure 8. Routing over 6LoWPAN
The 6LoWPAN defines also the mesh routing (Fig. 9.). It means that routing
is performed using the 6LoWPAN mesh header. Moreover, if fragmentation is performed, each fragment is routed from source to the destination independently, what
is different than during routing over the 6LoWPAN, where all fragments have to
be collected in the intermediate node before subsequent forwarding.
Figure 9. 6LoWPAN mesh routing
The network coding operations in 6LoWPAN depends on the routing scheme.
If the forwarding mechanism is located in the IPv6 layer the coding has to be
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performed after header decompression and all fragments collection. This is because
the 6LoWPAN fragments are not equipped with IP destination address (except the first
one, if the address is carried inline). If the mesh forwarding is used, the coding operation can be located in 6LoWPAN layer before decompression and fragments collection.
During network coding operation two or more native packets are mixed using linear combination. But the native packets can differ in lengths. It is confirmed
in [6], that coding of native packets with significantly different lengths gives small
gain. It means that it is required to collect the native packets with similar lengths.
A format of the coded packet is shown in Fig. 10. Example two 6LoWPAN packets with header compression (IPHC – IP header compression) and IP addresses
carried inline (s – source, d – destination) are xored to receive the coded packet
with additional network coding header (NC Header). The coded packet is sent to
an appropriate MAC address, depending on the transmission rule. The example
in Fig. 10 shows that coded packet is sent to broadcast address. During network
coding operation it is required to ensure an appropriate length of the native packets
in order to do not exceed the MAX IEEE 802.15.4 MAC SDU.
Figure 10. Format of coded packet
Figure 11. Network coding header format (NCi – NC identifier ‘0001’, A – address type,
N – number of MAC addresses, Pad L – Pad length)
The network coding header format is presented in Fig. 11. It is composed
of the Dispatch field and destination nodes identifiers (MAC addresses) which in-
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
315
dicate the destination nodes responsible for decoding the native packets. Dispatch
field begins from the NC identifier which is set to binary 000 and is allocated by
the 6LoWPAN recommendation [2] to Non-IPv6 packets.
III. Implementation
The network coding proposal presented in section II was implemented in Contiki operating system (OS) [10]. The Contiki OS is an open source operating system
for networked embedded systems in general, and wireless sensor nodes in particular. It is developed by a team of developers from the industry and academia.
The Contiki system incorporates uIPv6 – the IPv6 protocols stack. Both the Contiki
system and applications for the system are implemented in the C programming
language. Contiki has been ported to a number of microcontroller architectures,
including the Texas Instruments MSP430 and the Atmel AVR [11]. The network
coding implementation was tested on the 8-bit Amtel AVR motes.
Currently, the Contiki OS allows running the 6LoWPAN implementation
working on the IEEE 802.15-based MAC, but with some limitations. The most
important is that the 6LoWPAN implementation is limited to IP routing. It means
that all fragments have to be collected in each node before transferring the packet to
the next node. It is shown in Fig. 12. This limitation is very important from network
coding point of view. Since the sensor motes have memory limitations, they cannot
allocate to big memory for stored packets. It is because the remaining memory must
be used to store the fragments before the node rebuild a full IPv6 packet.
Figure 12. Collecting the fragmented packets in Contiki 6LoWPAN implementation
The network coding functions (net_coding(), net_decoding()) are located
in functions defined in sicslowpan.c file, and they allows the network coding before
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the packets reach the uIP6 layer. A location of these functions is shown in Fig. 13
against the other Contiki protocols stack functions.
Figure 13. Location of the net_coding() and net_decoding() functions in contiki protocols stack
(routing over 6LoWPAN)
IV. Verification results discussion
Implemented network coding mechanisms were verified in a simple testbed
based on the Atmel evaluation and starter kit that includes two AVR Raven boards
with a 2.4 GHz transceiver, on-board picoPower AVR application processors with
LCD display, and one USB stick with a 2.4 GHz transceiver to allow USB connections to a PC. The maximum transmission power of the nodes were decreased
and the nodes were deployed in such a way that using the RPL routing protocol
the network were configured to the structure presented in Fig. 7, where node 1
is the USB stick connected to a PC. Such configuration corresponds to the real sensor networks in which the nodes transmit the sensor data to the gateway (node 1).
Instead of the real sensor data generated by the node 3, the IPv6 Ping streams
(30 packets in each stream) with packet intervals from 0.4 to 0.45 s were transmitted between nodes 1 and 3. The length of the Ping packets were set up to 10 Bytes.
The number of streams were changed from 1 to 7. Also a number of native pack-
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
317
ets that can be buffered in each node were changed during verification experiments from 0 (without network coding) to 5. Each native packet were buffered
in the node by 0.5 second. Fig. 14 shows the overall traffic captured in the air
depending on the number of streams and number of buffered native packets
in the node (0 – means without network coding).
Figure 14. Overall traffic captured in the air
For clarity, detailed values of overall traffic are presented for the network
without network coding procedures and while 5 native packets can be buffered
in the node for coding purposes. The results confirm that using network coding
the overall traffic in the air is decreased. Additionally, the level of traffic decreasing
is directly dependent on the traffic generated by the nodes and the network coding
buffers capacity. The cost of the network coding in the network was the end-to-end
packet delay increasing. In the case of presented network the end-to-end packet
delay was increased by about 0.5 s (native packet buffering time). By increasing
the number of buffered native packets in the node the we can attain additional
gain but the limitation is a memory of the sensor motes.
V. Conclusions and future work
The paper described the possibility of using the network coding mechanisms
in 6LoWPAN wireless sensor networks. The proposal is based on relatively simple
mechanism relying on the linear combination of two or more 6LoWPAN packets
and on transmission coded packet to one hop neighbours using broadcasted nature
of WSN. The proposed mechanisms are tailored to the Contiki 6LoWPAN and
MAC implementation run on the AVR RAVEN sensor motes.
The first verification results confirm that using network coding in presented
network the overall traffic can be decreased. This endeavor would require more
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work in the future, especially in terms of implementation of all described here routing cases and performing detailed tests in the wider network with more realistic
traffic generated by the nodes.
References
[1] EDA IAP4 Raoadmap, “Future IAP4 European Defense stratEgic Research Agenda”,
EDA Final Report, September 2011.
[2] G. Montenegro, N. Kushalnagar, Intel Corp, J. Hui, D. Culler, “Transmission
of IPv6 Packets over IEEE 802.15.4 Networks”, Request for Comments: 4944, September
2007.
[3] H. Song, S.H. Lee, S. Lee, H.S. Lee, “6LoWpan-based tactical wireless sensor network
architecture for remote large-scale random deployment scenarios”, IEEE Military
Communications Conference, MILCOM, October 2009.
[4] R. Ahlswede, N. Cai, S.-Y.R. Li, and R.W. Yeung, “Network information flow,”
IEEE Transactions on Information Theory, vol. 46, no. 4, pp. 1204-1216, 2000.
[5] Y. Chen, G. Feng, L. Zhou, “Using network coding to improve robustness and
persistence for data transmission in sensor networks”, 6th International ICST
Conference on Communications and Networking in China, CINACOM, August 2011.
[6] S. Katti, H. Rahul, H. Wenjun, D. Katabi, M. Medard, J. Crowcroft, “XORs
in the Air: Practical Wireless Network Coding “, IEEE/ACM Transactions on
Networking, vol. 16, Issue 3, pp. 497-510, June 2008.
[7] S.-Y.R. Li, R.W. Yeung, and N. Cai, “Linear network coding”. IEEE Transactions on
Information Theory, February, 2003.
[8] B. Ni, N. Santhapuri, Z. Zhong, S. Nelakuditi, “Routing with opportunistically
coded exchanges in wireless mesh networks”, Proceedings of the 2nd IEEE Workshop
on Wireless Mesh Networks (WiMesh 2006) (2006), pp. 157-159.
[9] J. Krygier, M. Bednarczyk, K. Maślanka, Integration of MAC Relaying and
IP Routing Protocols in Ad-Hoc Networks: Multimetric Approach, Military CIS
Conference MCC’2010, Wroclaw, Poland, September 2010.
[10] A. Dunkels, B. Grönvall, T. Voigt, “Contiki – a lightweight and flexible operating
system for tiny networked sensors”, Proceedings of the First IEEE Workshop on
Embedded Networked Sensors (Emnets-I), Tampa, Florida, USA, November 2004.
[11] Thang Vu Chien, Hung Nguyen Chan, and Thanh Nguyen Huu, “A Comparative
Study on Operating System for Wireless Sensor Networks”, International Conference
on Advance Computer Science and Information System, ICACSIS 2011, Jakarta 2011.
Testbed Implementation of Energy Aware Wireless
Sensor Network
Ewa Niewiadomska-Szynkiewicz1, 2, Michał Marks1, 2, Filip Nabrdalik2
1
Research and Academic Computer Network (NASK), Warsaw, Poland,
[email protected], [email protected]
2
Institute of Control and Computation Engineering,
Warsaw University of Technology, Warsaw, Poland,
[email protected], [email protected], [email protected]
Abstract: Wireless sensor networks (WSNs) are autonomous ad hoc networks designed and developed for potential applications in monitoring, surveillance, security, etc. The sensor devices that are
battery powered should have lifetime of months or years. Therefore, energy efficiency is a crucial
design challenge in WSN. In this paper the energy efficient communication techniques, i.e., activity
control and power control protocols are presented and discussed. We focus on the implementation
of energy aware algorithm for WSN – Geographical Adaptive Fidelity (GAF) – in our testbed network
formed by the Maxfor devices. The results of experiments confirm significant energy savings that
lead to network lifetime increase.
Keywords: wireless sensor networks; WSN; energy aware communication; activity protocols; power
control protocols
I. Introduction to WSN
The last decade has seen tremendous interest in all aspects of wireless sensor
networks (WSNs) – distributed systems composed of numerous smart, embedded and
inexpensive sensor devices deployed densely in a sensing area [1], [2], [15]. The nodes
of a network equipped with CPU, battery, sensing units and radio transceiver, networked
through wireless links can be used in applications, in which traditional networks are
inadequate. The important property of WSN is the ability to operate in harsh and hostile
environments, in which human monitoring is risky, and often impossible. The lack
of fixed network infrastructure components allows creating unique topologies and
enables the dynamic adjustment of individual nodes to the current network structure
in order to execute assigned tasks. Wireless sensor networks are deployed in various environments and are used in large number of practical applications concerned
with monitoring, rescue missions and military actions. Ad hoc architecture of WSN
has many benefits, however its flexibility come at a price. A number of complexities and
design constraints are concerned with the characteristics of wireless communication,
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i.e., limited transmission range and throughput, limited QoS, limited resources, and
multihop nature of a network. Moreover, the quality of wireless transmission depends
on numerous external factors, like weather conditions or landform features. Most
of these factors can change in time.
For these reasons, WSNs need some special treatment. The most important
constraint is concerned with limited power source. Nodes forming the network are
often small battery-fed devices. Each device, participating in WSN needs to manage
its power in order to perform duties as long and as effective as possible. Moreover,
devices are often deployed in remote locations where replacing batteries is difficult
or even impossible, and usually a high disparity between expected and real power
drawn can be observed. Thus, power management is very important. A numerous
energy aware communication methods and strategies have been developed, and
are presented in literature [2], [12], [14]. In general, power management is a topic
that has been a subject of intensive research in ad hoc networking in recent years.
In this paper we discuss the approaches to design energy aware WSN topologies. The main contribution of our work is to show the benefits of application
of the energy aware communication protocol in real network system. We have
implemented and verified the modified version of commonly known protocol GAF
in our testbed network formed by the Maxfor devices. The results of tests in our
laboratory were compared with simulation results presented by Xu et al. in [16].
In section II, we describe the energy consumption characteristics in WSN. In section III, we investigate some energy aware methods and algorithms, in section IV,
the GAF protocol is described. The results of the performance evaluation of GAF
through testbed implementation are presented and discussed in section V.
II. Energy consumption in WSN
A lifetime of WSN is measured by the time interval before all devices have
been drained out of their battery power or WSN is unserviceable – no longer
provides an acceptable event detection ratio. To maximize the lifetime, all aspects
such as architecture, circuits and communication protocols must be made energy
efficient. Let us focus on the energy consumption characteristics of a sensor device
(WSN node). Three main components that consume the energy are: microprocessor, sensing circuit and radio transceiver.
The energy consumed by microprocessor is determined by the sum of dynamic Pd
and static Ps power, i.e., P = Pd + Ps. Many techniques have been developed to
minimize the energy consumption, such as: dynamic voltage scaling, modulation
scaling, and energy aware embedded, event driven operating systems.
The objective of a sensing unit is to translate physical measurements to electrical signals. Several sources of energy consumption can be listed: signal sampling,
conversion of physical signals to electrical ones, analog to digital conversion, signal
conditioning. The energy usage in this unit is relatively constant. In some applica-
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
321
tions the detectors can work in the event driven mode, and can be switched off for
some time to save energy.
The communication system is the major energy consumer during wireless
network operation. The radio transceiver unit can operate in one of four modes,
which differ in the consumption of energy: transmission – signal is transmitted to
other nodes (greatest power consumption), receiving – message from other nodes
is received (medium power consumption), stand-by (idle) – inactive transceiver,
turned on and ready to change to transmission or receiving mode (low power
consumption) and sleep – transceiver is switched off. In order to extend the lifetime of a network, it is frequent practice that radio transceivers of some nodes are
deactivated. The nodes remain inactive for most time and are activated only to
transmit or receive messages from the network.
The energy consumption strongly depends on the transmission range and
modulation parameters. Generally, short transmissions in a network are desired.
They involve smaller power usage and cause less interference in a network, simultaneously effected transmissions, thus increasing the network throughput.
III. Energy aware communication
A. Methods and algorithms
In this paper we focus on energy aware protocols for IEEE 802.15.4 (ZigBee)
based networks. The protocol used by the node of WSN consists of the application, transport, network, data link and physical layers [1]. The energy management
in WSN is emphasized in data link and network layers. The MAC protocol guarantees efficient access to the transmission media while carefully managing the energy
allotted to the nodes. Typically, this objective is achieved by switching the radio to
a low-power mode based on the current transmission schedule.
Energy efficiency is considered mostly by protocols provided by network
layer. Energy aware routing ensures the survivability of low energy networks.
In commonly used wireless senor networks it is assumed that the receiver is not
located within the transmitter’s range. The transmitter must transmit data to the receiver by means of intermediate nodes. Thus, the natural communication method
in WSNs is a multi-hop routing. This is a certain limitation, but on the other hand
it enables the construction of network of greater capacity – a multi-hop network
enables simultaneous transmission via many independent routes, and managing
available energy. Moreover, each node of a network can attribute the level of power
used to send a message to the other node in order to minimize the amount of energy received from the battery, while at the same time maintaining the coherence
of the network. Due to a significant node redundancy (nodes are densely deployed),
and the assumption that each node of WSN has impact on the power used to transmit a message numerous low energy consumption communication methods and
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energy conservation techniques have been developed, and are described in literature.
In general, two main approaches for power saving in WSN can be distinguished:
• power control protocols,
• activity control protocols.
B. Power control protocols
The popular approach to energy efficient communication is controlling a transmit power of each node in a network. In general, short transmissions involve smaller
power consumption and cause less interference and latency. Power control protocols
(PCs) are responsible for providing the routing protocols with the list of the nodes’
neighbors, and making decisions about the ranges of transmission power utilized
in each transmission. Therefore, the PC protocols are placed partially in the OSI
network layer and the OSI data link layer [14]. A numerous PC protocols have
been developed and are described in literature. They utilize various information
about a network and network nodes, i.e., location of nodes and its neighbors or
direction from which the signal was received. Based on these information power
control techniques may be divided into following groups: location-based (nodes
are able to determine their exact positions), direction-based (relay on the ability
of all nodes to estimate relative directions of their neighbors) and neighbor-based
(determine all neighbors within the maximal transmission range). The detailed
survey of PC protocols can be found in [14]. The results of performance evaluation
of selected techniques through simulation are presented in [12].
C. Activity control protocols
The other approach to energy efficient communication is controlling a number
of active nodes in a network. Due to a significant node redundancy and multiple
paths between nodes we can turn off selected intermediate nodes while still guarantee full connectivity and maximum link utilization constraints. Dynamic power
management is an efficient approach to reduce system power consumption and
extend the lifetime of individual node without significantly degrading the network performance. The basic idea is to deactivate the radio transceiver of selected
nodes when not needed and wake them up when necessary. Hence, radio devices
remain inactive for most working time and are activated only to transmit or receive
messages from other nodes. In general, dynamic power management is a complex
problem. It can involve the limitation of accessible band, and can also interrupt
the data transfer in the network. Therefore, implementing the correct policy for
radio switching and estimating the optimal value of radio transceiver’s switch-off
time are critical for a network performance.
The activity control protocols (AC) employ dynamic management of radio
devices. The objective is to limit the power consumption while simultaneously
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323
minimizing the negative impact on the network throughput and on the efficiency of data transmission routing. Different types of AC protocols are presented
in literature, and are applied to WSN systems. Similarly to PC protocols, they
utilize various information about a network and network nodes, i.e., location
of nodes in a network, nodes density, connectivity, etc. Many of them implement clustering techniques. It was observed that grouping nodes into clusters
can reduce the overall energy usage in a network. Based on utilized information activity control techniques may be divided into several groups: locationbased (GAF [16], GeRaF [6]), connectivity-based (Span [7]), clustering-based
([LEACH [9], HEED [17], PANEL [5], PRWST [3]) and hierarchical (EEHC [4],
CGPS [12]).
It should be pointed that activity control protocols should be capable of buffering traffic destined to the sleeping nodes and forwarding data in the partial network
defined by the covering set. The covering set membership needs to be rotated between all nodes in the network in order to maximize the lifetime of the network.
IV. GAF algorithm
The Geographic Adaptive Fidelity (GAF) algorithm developed by Y. Xu et al.,
and described in [16] selects nodes responsible for relaying traffic in the network
based on their geographical position estimated using the GPS system or calculated
using any other location system [2], [11]. GAF assumes covering the network deployment area with a virtual grid. The location information is employed to form clusters.
The GAF protocol relies on the concept of ‘’node equivalence’’. Two nodes are
equivalent when they are equally useful as relays in communication between other
nodes. Problem of selecting equivalent nodes is nontrivial. It can be easily observed
that network nodes equivalent in communication between a given pair of nodes do
not have to be equivalent in communication between other pairs of nodes.
To select equivalent nodes GAF divides a spatial domain, where nodes are
distributed into cells that form a grid, see Fig. 1. The size d of each cell is calculated
due to transmission ranges of nodes, i.e., d  r / 5 , where r denotes the maximal
transmission range assigned to network nodes. It is assumed that each node in a cell
is in transmission range of all other nodes within adjacent cell. The construction
of such a grid allows to preserve the network connectivity. All nodes in a network
may switch between one of three states: active, discovery and sleep. In the active
state a node is responsible for relaying traffic on behalf of its cell. In the discovery
state nodes exchange discovery messages, trying to detect other nodes with higher
energy in the same cell. However, the overhead due to discovery messages is not
very high. The following load balancing energy usage is proposed. After spending
a fixed amount of time TA in the active state, a node switches to the discovery state,
and another node from the same cell switches to the active state. After spending
a fixed amount of time TD in the discovery state, the node backs to the active state.
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Whenever a node changes the state, it sends a message containing its identifier (ID)
and its ranking value (RV). The ranking value is used to select the relay node to
transmission a message. Several rules are proposed to determine a node’s RV. In general, a node that is in the active state has a higher rank than a node in the discovery
state, and nodes with longer expected lifetimes (calculated with respect to energy
consumption characteristics) have higher rank than the others.
Figure 1. Communication using GAF. The data transmission from the source to the target
is realized using different relay nodes for different time slots
Nodes in the active and discovery states may switch to the sleep state whenever
they find a node in the same cell with higher RV. When a node enters the sleep state,
it cancels all pending timers, and powers down the radio. After spending the fixed
amount of time TS in the sleep state the node turns on its radio and switches to
the discovery state. The sole concept of GAF is to maintain only one node with its
radio transceiver turned on per cell, see Fig. 1. The mentioned parameters, i.e., TA,
TD, TS, are used to tune the algorithm. In our improved version of GAF (GAF-M)
the value of the interval TD is estimated independently for each cell, and depends
on the number of nodes that form a given cell. The bigger the number of nodes
the shorter TD time. Moreover, we prohibit switching between different states
the nodes with very low battery level.
The GAF algorithm was designed for IEEE 802.11 networks. It can run over
any routing protocol for ad hoc networks. Y. Xu et al., discuss the performance
of GAF combined with two reactive routing protocols AODV (Ad-hoc On-Demand
Distance Vector) [13] and DSR (Dynamic Source Routing) [10]. We adopted GAF
to work in 802.15.4 networks. In our implementation we used DYMO (Dynamic
Manet On-demand) [8] routing protocol that is a successor of AODV. DYMO
shares many benefits of AODV but is slightly easier to implement.
Y. Xu et al. claim that GAF provides longer lifetime of WSN with minimal loss
in data delivery rates compared to pure AODV and DSR protocols. The simulation results presented in [16] confirm the good performance of the algorithm. It is
worth to note that simulation results show that GAF extends the network lifetime
proportionally to the increase of nodes density in the deployment area.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
325
V. GAF performance in testbed network
The main objective of our research was to implement and validate the GAF
algorithm using testbed implementation in our laboratory formed by physical
sensor devices.
A. Testbed WSN – hardware and software
The experiments in our WSN laboratory were performed using testbed implementation involving MTM-CM5000 motes (http://www.maxfor.co.kr/eng/
en_sub5_1.html) manufactured by Maxfor (see Fig. 2).
Figure 2. The MTM-CM5000 mote
The MTM-CM5000 mote is IEEE 802.15.4 compliant wireless sensor node
based on the original open-source “TelosB” platform design, developed and published by the University of California, Berkeley. The mote’s architecture is presented
in Fig. 3 and the general specification is given in Table I.
Figure 3. Architecture of the MTM-CM5000 mote
The testbed networks were formed by three to eleven MTM-CM5000 motes and
one base station, all operating under TinyOS system. TinyOS (http://www.tinyos.net/)
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is an open source, highly portable operating system that can be used to on-line
operation of WSN formed of real low-power wireless devices. Our application
was written in nesC (Network Embedded Systems C) language provided by TinyOS
system. TYMO (http://tymo.sourceforge.net/) – the implementation of DYMO protocol on the TinyOS system was used to work with GAF. The preliminary version
of our implementation was done in TOSSIM simulator, and next transformed into
physical network. TOSSIM (http://docs.tinyos.net/tinywiki/ index.php/TOSSIM)
is a discrete-events simulator for TinyOS wireless sensor networks. By exploiting
the sensor network domain and TinyOS’s design, TOSSIM can capture network
behavior at a high fidelity while scaling to thousands of nodes. The same code
can be used for simulation and real WSN operating in TinyOS.
Table I. Specification of MTM-CM5000 Mote
Processor
TI MSP430F1611
RF chip
TI CC2420
RF power
–25 dBm~0 dBm
Power supply
2.1 V~3.6 V – (AA or AAA battery)
Antenna
Dipole antenna / PCB antenna
Receive mode:
RF current draw
18.8 mA
Transmit mode:17.4 mA
Sleep mode:
1.0 μA
Range
~150 m (outdoor), 20~30 m (indoor)
Sensors
Light, humidity, temperature
B. Results of experiments
Multiple experiments were performed in our laboratory. The goal of the first
series of experiments was to evaluate the performance of the GAF algorithm
in a physical wireless network. The wireless sensor networks implementing GAF
and DYMO protocols were compared with networks with no power capabilities at
all (implementing pure DYMO). The key metric for evaluating examined networks
was the lifetime of the network. To compare the performance of a given network
we used the following characteristic:
LTI = LifetimeGAF+DYMO / LifetimeDYMO
(1)
where LifetimeGAF+DYMO denotes the lifetime of a network implementing GAF and
DYMO protocols, and LifetimeDYMO is the lifetime of a network using only DYMO.
The goal of the second series of experiments was to test the influence
of the nodes density into a lifetime of a given network.
In this paper the results of experiments performed for nine network configurations, i.e., examples E1-E9 describing different model size and topology are
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
327
presented and discussed. The detailed description of examined networks is given
in Table II. The variable N_cells denotes the number of cells that form a grid covering a deployment area, and S_cell the number of motes in each cell. Figures 4 and 5
show the exampled network configurations.
Table II. Specificatiojn of Eight Testbed Networks
Testbed Networks (examples)
E1
E2
E3
E4
E5
E6
E7
E8
E9
N_cells
1
1
1
1
1
2
2
2
3
S_cell
2
3
4
5
6
2
3
4
3
Figure 4. Network E4 formed by 7 motes (source, sink and one cell of 5 nodes)
Figure 5. Network E9 formed by 11 motes (source, sink and three cells of 3 nodes each)
During the tests, we calculated the lifetime of a network measured by the time
interval before WSN was unserviceable (all motes in one cell were drained out
of their battery power). The performance of examined algorithms GAF+DYMO
and pure DYMO for networks E4 and E9 are presented in Fig. 6 and 7. The summary of results – extensions of network lifetimes using GAF and DYMO over pure
DYMO for eight experiments is presented in Table III.
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Figure 6. Extension of WSN lifetime using GAF; network E4
Figure 7. Extension of WSN lifetime using GAF; network E9
Table III. Summary of Results (Values of LTI For All Testbed Networks)
Method
DYMO
GAF+DYMO
Testbed Networks (examples)
E1
E2
E3
E4
E5
E6
E7
E8
E9
1
1
1
1
1
1
1
1
1
1.78
2.18
3.28
5.18
5.90
1.71
2.57
3.40
2.56
The goal of the third series of experiments was to compare the performance
of the original version of GAF with the modified version GAF-M implementing our
scheme for TD time interval calculation. The performance of examined algorithms
GAF-M and pure DYMO for network E4 is presented in Fig. 8.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
329
Figure 8. Extension of WSN lifetime using GAF-M; network E4
The summary of results for this case study are presented in Table IV and Fig. 9.
The modified GAF-M allows to extend the network lifetime up to 30%.
Table IV. Summary of Results (Values of LTI For 5 Testbed Networks)
Method
Testbed Networks (examples)
E1
E2
E3
E4
E5
1
1
1
1
1
GAF+DYMO
1.78
2.18
3.28
5.18
5.90
GAF-M + DYMO
1.93
2.78
4.40
5.48
6.50
DYMO
Figure 9. Sensitivity to network density
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The results of experiments confirm the strength of the GAF algorithm. GAF
provides longer lifetime compared to pure DYMO routing protocol. The results
of tests performed in our testbed networks formed by the physical devices
were not worse than simulation results described in [16]. We can even say that
the combination of GAF and DYMO provides better results than the application
that combines GAF and AODV. Similarly to the conclusions presented in [16],
we could observe that GAF extends the network lifetime proportionally to
the increase of nodes density in the deployment area. Tables III and IV, and Fig. 9
show that time of accurate network operation strongly depends on the number
of nodes that form each cell of a grid covering the deployment region. Moreover,
our modifications of GAF improve the performance of the algorithm that leads
to network lifetime increase.
VI. Summary
Many challenges arise from ad hoc networking and development of real life
wireless sensor systems. In this paper we focused on energy aware networks. We
described a functionality of the popular activity control protocol GAF for power
saving in WSNs, and our testbed implementation that combines GAF and routing
protocol DYMO. We evaluated the performance of our application in the laboratory, and improved the algorithm performance w.r.t. original version. The results
of experiments confirm good performance of GAF in real life networks. In our
future work we plan to make experiments with higher dimension networks and
compare GAF with other activity control protocols described in literature.
As a final observation we can say that the design of wireless sensor networks
should account for trade-offs between several attributes such energy consumption
(due to mobility, sensing, and communication), reliability, fault-tolerance, data
collection latency, and quality of information, and their impact on mission objectives. Therefore, strategies and techniques for energy efficient, reliable and secure
communication in wireless sensor network has become a hot debate nowadays.
Acknowledgment
This work was partially supported by National Science Centre grant
NN514 672940.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
331
References
[1] I.F. Akyildiz, W. Su, Y. Sankasubramaniam, and E. Cayirci, “Wireless sensor
networks. A survey”, Computer Networks, vol. 38(4), pp. 393-422, 2002.
[2] I. Akyildiz, and M. Vuran, “Wireless sensor networks”, John Wiley & Sons, Ltd,
West, Sussex, UK, 2010.
[3] N. Asiam, W. Philips, W. Robertson, and S. Sivakumar, “A multi-criterion
optimization technique for energy efficient cluster formation in wireless sensor
networks”, Information Fusion, vol. 12(3), pp. 202-212, 2011.
[4] S. Bandyopadhyay, and E. Coyle, “Minimizing communication costs in hierarchicallyclustered networks of wireless sensors”, Computer Networks, vol. 44(1), pp. 1-16, 2004.
[5] L. Buttyan, and P. Schaer, “Position-based aggregator node election in wireless sensor
networks”, International Journal of Distributed Sensor Networks, pp. 1-15, 2010.
[6] M. Casari, A. Marcucci, M. Nati, C. Petrioli, and M. Zorzi, “A detailed simulation
study of geographic random forwarding”, Proc. of Military Communications Conference
MILCOM. vol. 1, pp. 59-68, 2005.
[7] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, “Span: An energy-efficient
coordination algorithm for topology maintenance in ad hoc wireless networks”, ACM
Wireless Networks, vol. 8(5), pp. 481-494, 2002.
[8] I. Chakeres and C. Perkins, “Dynamic manet on-demand (dymo) routing”, 2006,
http://www.ietf.org/internet-drafts/draft-ietfmanet-dymo-05.txt.
[9] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficiet
communication protocol for wireless sensor networks”, Proc. of the 33rd Hawaii
International Conference on System Sciences. pp. 1-10, 2000.
[10] D.B. Johnson, “Routing in Ad Hoc Networks of Mobile Hosts”, Proc. of the IEEE
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[11] E. Niewiadomska-Szynkiewicz, and M. Marks, “Optimization schemes for wireless
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[12] E. Niewiadomska-Szynkiewicz, P. Kwaśniewski, and I. Windyga, “Comparative
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[13] C.E. Perkins and E.M. Royer, “The ad hoc on-demand distance vector protocol”
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[14] P. Santi, “Topology control in wireless ad hoc and sensor networks”, John Wiley
& Sons, Ltd, West Sussex, UK, 2006.
[15] R. Verdone, D. Dardari, G. Mazzini, and A. Conti, “Wireless sensor networks
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[16] Y. Xu, J. Heidemann, and D. Estrin, “Geography-informed energy conservation
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[17] O. Younis, and S. Fahmy, “Distributed clustering in ad-hoc sensor networks: A hybrid,
energy-efficient approach”, Proc. of the IEEE INFOCOM, vol. 1, pp. 629-640, 2004.
An Energy Aware Self-Configured Wireless
Sensor Network
Marcin Wawryszczuk, Marek Amanowicz
Electronics Faculty, Military University of Technology, Warsaw, Poland,
[email protected], [email protected]
Abstract: Recent years have shown that wireless communication are becoming more popular
in professional, academic and every day applications. A wireless sensor node is usually autonomous,
advanced device with limited power source. This limited power source makes the topology control
a crucial technique, which allow the network to obtain energy efficiency without affecting the network’s connectivity and sensing coverage. The article demonstrates the energy efficient topology
control mechanism, which allow a majority of network nodes stay in sleeping mode and do not affect
the connectivity and sensing coverage. The result in energy consumption of the nodes and network
lifetime are exposed and compared to different approaches.
Keywords: wireless sensor network, topology control, energy efficiency
I. Introduction
Wireless sensor networks have become an emerging technology that represents
the next evolutionary step in environment monitoring, traffic control, objects detection and tracking, etc. Typically, a network consists of a large number of nodes,
distributed over some specific area and organized into multi hop system. A node
of wireless sensor network is a simple device equipped with control, communication, sensing units as well as a power supply. The power supply is usually a small,
low capacity battery which is very hard to replace. It makes the energy factor
of the sensor node a critical one. As a result, topology control mechanisms of sensor network have to be equipped with efficient power management procedures.
The network designer is responsible for these power management procedures to
protect the energy resources what imply that the network lifetime is elongated. On
the other hand, the network has to realize the primary objectives what is often out
of whack with the energy conservation. The suitable compromise between both
is called “topology control” (TC). It adjusts desired network’s parameters (like connectivity, coverage, etc.) while reducing energy consumption.
There are two approaches to the topology control in wireless sensor networks.
One of them is the nodes activity control, where only the small subgroup of nodes
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is operative (active) and the rest of them run in sleep mode. The other method
is called sleep/wake-up algorithms. The nodes with implemented sleep/wake-up
algorithm stay active for a small period of time and then turn into sleep mode,
where the communication, sensing and control units are deactivated. The active
and inactive cycles are repeated during the whole node lifecycle. This approach
minimize the energy consumption of every single node, but it does not guarantee
the connectivity and/or coverage at any time of the network life. Obviously, these
methods reduce the global energy consumption, as a result of all nodes energy conservation. Both methods are capable of elongating the network lifetime significantly.
II. Related work
Much of the related research are addressed to WSN that are dense, battery
powered and self configured. Because of these requirements, most of the authors
are concentrated on finding solution at various levels of the communication protocol, including extremely energy efficient aspects [1]. Some of the most popular
solutions in energy efficient topology control are stated below.
• GAF [2] (Geographical Adaptive Fidelity) is the nodes activity control
method. This approach groups nodes in virtual grids. Each grid is defined
such that, for any two adjacent grids A and B, all nodes in grid A are capable of communicating with any node in grid B, and vice-versa. The nodes
in the same grid are equivalent in terms of routing, consequently just
one node at a time needs to be active. The rest of the nodes can switch to
the sleep mode and save the energy in this way.
• GeRaF [3] (Geographic Random Forwarding) is the nodes activity control
method. In this method, each node posses the information about the neighborhood. Each node follows to the sleep/active pattern, starting from
channel listening. The data transmission starts when the source node ws
has any packet to send. The source node broadcasts the data with the location of itself and the location of the destination node wd. The node, which
is inside the communication range of ws and is closest to the destination
node wd simultaneously, continues the process of the data forwarding.
The process is accomplished when the end point wd is reached. GeRaF
protocol can cause a lot of collisions in a network [3][4].
• SPAN [5] is the nodes activity control method. In the method, only a small
group of coordinators stay active, while the rest of nodes switch into sleep
mode and only periodically check if they are requested to wake up and become the coordinators. The coordinators are responsible for passing the data
from a source node to a sink node. To guarantee a sufficient number of coordinators, SPAN uses so called coordinator eligibility rule: if two neighbors
of a non-coordinator node cannot communicate with each other directly
or via coordinators, it means that the node should become a coordinator.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
335
However, it may happen that several nodes discover the lack of a coordinator
at the same time and, thus, they all decide to become a coordinators.
• ASCENT [6] (Adaptive Self Configuring Sensor Network Protocol)
is the node activity control method. In ASCENT a node decides when to
join the network or stay dormant. The decision is taken based on the connectivity and packet loss ratio measured locally.
• STEM [7] (Sparse Topology and Energy Measurement) is the sleep/wakeup
method. This approach exploits two different communication units: one for
wakeup signaling and other for data transmission. The wakeup radio is not
a low power consumption unit. Both radios work with the same communication range. Periodically each node turns its wakeup radio on for Tactive
time, every T duration. When a source node wants to communicate with
a neighboring node, it sends a stream of signals via the wakeup channel.
As soon as the destination node receives the signal message, it acknowledges
that fact and turns the data transmission unit on.
• FSP [8] (Fully Synchronized Pattern) is the sleep/wakeup method. In this
approach, all nodes in network wake up at the same time, following
the pattern. It means that each node goes periodically into active mode
for Tactive time, every Twakeup. After that, nodes switch to the sleep mode
until the next Twakeup.
• SWP [9] (Staggered Wakeup Pattern) is the sleep/wakeup method. In this
approach nodes create the specific data acquisition tree structure. The nodes
located at different levels of data gathering tree, wake up in a different time.
Obviously, the active time Tactive of the adjacent tree levels must partially
overlap to guarantee that children and parent nodes in the mentioned tree
structure are able to communicate.
Figure 1 shows the mechanism of SWP, where Tactive is the time the node stays
active, Tsleep is the time the node stays dormant, T is the sum of Tactive and Tsleep.
Figure 1. Staggered sleep/wakeup pattern
• 802.11 PSM [10] is the sleep/wakeup method. In the approach, when two
nodes want to communicate, their activity times Tactive, have to overlap.
The method uses backoff mechanism for signaling and sampling the channel. When a node has a packet to send, it waits random period of time,
assigned by backoff mechanism, before the action is performed.
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Comparing the methods described above, GAF and SPAN can reduce the energy
consumption 3 times [11], GeRaF 2-3 times [4], ASCENT even more than 5 times [11].
In group of sleep/wakeup methods, the energy conservation depends on
the Tactive to T ratio. Therefore, it is difficult to estimate the exact energy conservation. Definitely STEM method is worse than the rest of the methods, because
the method requires two communication units. For small Tactive/T ratio, the energy
consumption is decreased to 1-2% of the regular energy consumption (without any
method implemented).
III. Assumptions
In our work, we assume that the sensor network is dense and composed with
MicaZ nodes, equipped with one communication module Chipcon CC2420. Nodes
positions are known and every node is aware of its neighborhood in 2-hops distance. These information can be acquired through procedures introduced in [12].
Communication and sensing ranges are modeled as a uniform disks. The radiuses
of the disks are labeled as rc and rs respectively. The communication radius is not
greater than the sensing radius. The nodes are placed on a flat area, so the network
is considered in two dimensional perspective.
IV. Problem statement
We would like to consider the network purposed to detect ground moving
objects and track them afterwards. In this case, it is necessary to monitor only
the border layer of the network. Accordingly, the devices placed on the network
edge have to maintain their sensing units active, while the rest of the devices
can turn them off. They adhere to the set SE (external layer). The nodes localized on the network edge, can drowse communication units temporally to
conserve the energy. The nodes placed inside the network are mainly inactive
(all modules are switched off) and their control and communication units are
active only for a short period of time. These nodes belong to the set SI (internal
layer). The time when some node units are switched off is called T S. The time
when the units are active and the device is capable of communicating with
others is called TA. The sum of TA and T S is called cycle duration T. The TA to
T ratio is called duty cycling:
DC =
TA
100% (1)
T
It is obvious that the lower value of DC implies the lower energy consumption
in particular node [12].
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
337
The set of all nodes participating in the network structures S is defined as:
S S E  S I (2)
Obviously the nodes shaping the internal layer consume significantly less
energy in comparison with the external layer nodes. What is more, it can exist kmax
disjoint sub-layers of environment at the same time. Each external layer node wE
belongs to only one sub-layer:
wE  S E S Ei : wE  S Ei ,
i
1,..., k max (3)
where: i is the sub-layer number, i=1,…,kmax. Each layer has to follow the consistency rule:
 wz  S Ei
d ( wz , w j )  rc  d ( wz , wl )  rc (4)
z
where: wz is a node, which belongs to the external layer, w j, wl are the closest
nodes to the node wz, SEi is the i-th sub-layer, i is the number of the sub-layer,
i=1,…,kmax. The consistency rule guarantees that the distance between any two
nodes in a sub-layer is lower that the communication range (and the sensing
range as well).
The external sub-layers are active interchangeably. This approach allows
the network to work longer in initial size of deployment.
In the following chapters we answer the question how to assign the nodes to
the proper layer and how to assure the consistency in external sub-layers during
the whole network lifecycle in fully distributed manner.
Figure 2. One of the sub-layers of external layers
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V. Energy consumption
The overall energy consumption is defined as temporal energy consumption
of control unit EMC, communication unit ER and sensing unit ES in specified period
of time Δt:
Et 
te
te
te
 EMC dt   ER dt   ES dt (5)
ts
ts
ts
where: EΔt is the energy consumption in the analyzed period of time Δt,
EMC is energy consumption of the control unit, ER is energy consumption of the communication unit, ES is the energy consumption of the sensing unit, ts is the start
time, tk is the end time, Δt = tk-ts.
The energy consumption of the control unit is defined as:
E MC  TMC  I MC V (6)
where: TMC is the time of measurement, IMC is a current supply, V is a voltage.
Obviously, EMC consists of:
A
S
E MC
 E MC
 E MC
(7)
where: EAMC is energy consumption in active mode and ESMC is energy consumption in sleep mode.
The energy consumption of communication unit is defined as:
E R  TR  I R V (8)
where: TR is time of measurement, IR is current supply, V is voltage. On the other
hand, ER consists of energy consumption in four different states of communication
module: sleep ESR, listening ELR, transmission ETXR and reception ERXR:
E R  E RS  E RL  E RTX  E RRX (9)
The energy consumption of the sensing unit is defined as:
E D  TD  I D V (10)
where: TD is the time, when the communication unit stays active, ID is a current
supply, V is a voltage.
VI. Procedures description
To configure the network structures, self-configured algorithms were developed to classify the nodes to the internal layer or one of the external layers.
Each node w in a startup phase of the network lifetime, checks if the conditions
described by the equation (11) are met to determine whether the node belongs
initially to the external layer:
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
339
wk  Ss : xk  x  yk  y 
wk  S s : xk  x  yk  y 
(11)
wk  S s : xk  x  yk  y 
wk  S s : xk  x  yk  y 
where: SS is a set of neighborhood nodes in 2-hops distance, wk is a node in SS,
x is x coordinate of the node w, y is y coordinate of the node w, xk is x coordinate
of the node wk, yk is y coordinate of the node wk. The procedure below demonstrates
how the sub-layer number one is shaped. The procedure is performed by each node
in the network.
Shaping of sub-layer one of external layer
1: If condition (11) is fulfilled then
2:
label as a sub-layer one node
3:
broadcast the information about sub-layer affiliation
4: wait Δtwi
5: If kmax > 1 then
6:
broadcast the request for formation the next sub-layer
7: End-if
8: Else
9:
label as the internal layer node
10:End-if
In this way the first sub-layer of external layer is developed. It was mentioned
that it can exist kmax sub-layers because of the density of the network. The process
of shaping the next sub-layer is always initiated by the previous sub-layer. The procedure of forming next sub-layers is described by the procedure below.
Shaping of next sub-layer of the external layer
1: If request for formation the sub-layer i received then
2: wait Δtwi
3: If the equation (11) is fulfilled then
4:label as a sub-layer i node
5:
broadcast the information about sub-layer i affiliation
6:wait Δtwj
7: If i+1<kmax then
8: broadcast the request for creating the sub-layer i+1
9: End-if
10: End-if
11:End-if
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In the procedures two timeouts: Δtwi and Δtwj Are used. The first one guarantees
that the node is waiting long enough to receive the information from all nodes shaping previous sub-layer. The second timeout guarantees that the node will wait long
enough to collect the information about the nodes at the same sub-layer.
After the sub-layer is established, the correction procedure is performed. The correction procedure is responsible for securing the consistency in a sub-layer and switching redundant nodes off. The sub-layer consistency is assured by the consistency rule
(eq. 4). Therefore, the local consistency of the sub-layer between any two nodes wj
and wl is guaranteed. It means that the node evaluates the consistency rule and selects
the set Sdi of nodes among all neighboring nodes. It can exist several sets Sdi, satisfying
the consistency rule requirement, but the desired one is with the lowest cardinality.
To switch the redundant nodes off, the procedure exploits the redundancy rule:
d ( wz , w j )  rc  d ( wz , wl )  rc  d ( w j , wl )  rc (12)
where: wz is the node checking whether it is redundant, wj and wl are the closest
nodes to the node wz. The procedure is presented below.
Sub-layer correction
1: If the information about creation the sub-layer sent then
2: wait Δtwj
3: If the consistency rule is unfulfilled (eq. 4) then
4:
engage additional nodes
5: End-if
6: If the redundancy rule fulfilled (eq. 12) then
7:
label as the internal layer node
8:
broadcast the information about the internal layer affiliation
9: End-if
10: End-if
The external layer is composed of one or several sub-layers. Among the sublayers, only the one is active (in case of sensing the environment). After certain time,
the active sub-layer i activates the sub-layer i+1 (or sub-layer 1) and swaps to inactive state. After that, only the one sub-layer (number i+1) is still active.
When the network structures are established, the sub-layer consistency procedures run. The procedures are responsible for sub-layers reconfiguration in case
of the sub-layer inconsistency detection and when the inconsistency is at risk.
In the first case each node exchange their neighborhood table Sng with the neighbors
within the communication range rc. This neighborhood tables synchronization take
place every Δta. In case one of the closest nodes do not answer in synchronization
process, and both of them belong to the same sub-layer, the node which has not
received the information starts the sub-layer reconfiguration. The algorithm below
describes this situation (from external sub-layer node perspective).
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341
Sub-layer reconfiguration in case of inconsistency detection
1: If the consistency rule is not fulfilled (eq. 4) then
2: engage the additional neighbor nodes to assure the consistency of the sub-layer
3: End-if
The second group of the reconfiguration procedures run when the inconsistency can appear. It can happen when the power source capacity of the sub-layer
node is at risk. When the node discovers that:
CZ t   CG (13)
where: CZ(t) is the power source capacity, CG is the border power source capacity,
it starts reconfiguration process. The algorithm above describes this situation.
Sub-layer reconfiguration in case of inconsistency detection
1: If the power source capacity is lower than the border power source
capacity (eq. 13) then
2:
engage the additional neighbor nodes capable to replaceitself
3: End-if
VII. Simulation results
We evaluated the performance of proposed solution. The model of node used
in the simulation is previously mentioned MicaZ node, equipped with ZigBee
protocols stack. The energy consumption parameters of micaZ node are shown
in table 1and described in more details in [13] and [14].
Table 1. The energy consumption of MicaZ node
Control unit
Active
8 mA
Sleep
15 μA
Memory write
15 mA
Memory read
4 mA
Receive (RX)
19.7 mA
Send (TX) (at 0 dBm)
17.4
Sleep
1 μA
Active
5,5 mA
Sleep
0 mA
Communication unit
Sensing unit
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The simulation was run for three types of nodes deployment:
A)Nodes with antennas put 7 cm above the ground covered by grass vegetation,
rc = 10.5 m, the network size is equal 190 m × 190 m,
B)Nodes with antennas put 30 cm above the ground covered by grass vegetation
l, rc = 50 m, the network size is equal 730 m × 730 m,
C)Nodes with antennas put 30 cm above the ground covered by forest vegetation
l, rc = 30 m, the network size is equal 300 m × 300 m.
The duty cycling (DC) is set differently for the external and internal layers
nodes. External layer nodes work with DC = 25% and internal layer nodes work
with DC = 10%. What is more, during the activity time TA, the external layer nodes,
actively sensing the environment have control, communication and sensing units
switched on. During the sleep time TU, the communication module of these nodes
is switched off, but the sensing function is maintained. The external layer nodes,
which are not responsible for sensing and the internal layer nodes, retain the control and communication units active at time TA. At the sleep time TS, both groups
of nodes have all units switched off.
Table 2 shows the number of nodes in the network with external layer consisting of three sub-layers in comparison with the number of all nodes in the network.
The result is split by deployment type and the sub-layer number.
Table 2. The reduction of active nodes
Deployment type
Sub-layer number
# of sub-layer nodes
A)
1
80
B)
2
83
C)
3
82
A)
1
62
B)
2
60
C)
3
63
A)
1
41
B)
2
43
C)
3
42
# of all nodes
1474
920
412
The average energy consumption of the nodes is shown in table 3.
As a result, the average lifetime of the external layer nodes is about 21 days
(having three sub-layers switching every one hour) and internal layer nodes lifetime
is about 100 days.
Figure 3 depicts the lifetime of the networks: without any topology control
mechanism implemented, one of the node activity control mechanism presented
in section II and the topology control procedure introduced in the article. The approaches are evaluated according to two criterions: global lifetime and the time
while the network consistency is guaranteed. In our approach the internal layer
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
343
nodes consume significantly less energy in comparison with the external layer
nodes. When the external layer nodes run out of the energy, they are replaced by
the internal layer nodes. In the earlier mentioned methods, the active nodes cover
the whole network. Therefore, after certain period of time some active nodes run
out of energy. It means, it is possible that some uncovered areas inside the network
will appear.
Table 3. Average energy consumption in mWh
Active sub-layer node
Inactive sub-layer node
Internal layer node
25.2
5.1
2.15
Figure 3. The comparison of networks lifetimes
We also checked the reconfiguration procedures in the simulations (if do
they work and how effective, from the reconfiguration time perspective, they are).
The average times needed to reinstate the sub-layers trec are evaluated for either:
reconfiguration in case of inconsistency detection and when the inconsistency
is jeopardized. The result is shown on picture 4.
Figure 4. The time of a sublayer reconfiguration
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Maximal time the sub-layer can be inconsistent is equal to:
sec (14)
ta  trek
where: Δta is the time when the nodes exchange the information about their
neighborhood (mentioned above), trek is the time necessary to accomplish the reconfiguration. Assuming the Δta time equal to 180 sec, the time of the sub-layer
inconsistency is round 181.5 sec.
Demonstrated layer configuration elongate the network lifetime in the initial
size of deployment. The simulation experiment showed that the network shrinks
to inside at the borders (due to higher energy consumption of the external layer
nodes). As a result, after 100 days of work, the network size is reduced by rc meters
at each border.
VIII. Conclusion and future work
The article demonstrates the self-configured, energy aware method of topology control. The procedures of network structures establishment and maintenance
are provided and explained. The methods are evaluated in terms of energy consumption, the network lifetime and ability to guarantee the sub-layers consistency and repair. The network shaped in this way is capable of detecting object
moving on the ground. It gives excellent basis for the creation of such a system,
because the coverage consistency and maintenance are assured. What is more,
the network lifetime is significantly improved, even 10 times, in comparison with
a network without any topology control mechanism. The value of DC parameter
is adjusted to the network purposes. The greater values of DC parameter used
in the simulation has no serious impact on the communication effectiveness.
Evidently, there is a possibility to use lower values for DC parameter, but it would
degrade the communication performance and could have an impact on the detection operation.
There exist still many areas to explore within this research engagement.
It would be worth to check the network performance in different types of the nodes
deployment and environments of deployment. Verification the ability of the network to objects tracking is another very desired research which we plan to practice
in near future.
Chapter 7: Mobile Ad-hoc and Wireless Sensor Networks
345
References
[1] C. Patel, S.M. Chai, S. Yalamanchili, and D.E. Schimmel, Power/Performance Tradeoffs for Direct Networks. In Parallel Computer Routing & CommunicationWorkshop,
pages 193-206, July 1997.
[2] Y. Xu, J. Heidemann, D. Estrin, Geography informed Energy Conservation for Ad Hoc.
Proc. ACM MobiCom 2001, pp. 70-84. Rome, 2001.
[3] P. Vasari, A. Marcucci, M. Nati, C. Petrioli, M. Zorzi, A Detailed Simulation
Study of Geographic Random Forwarding (GeRaF) in Wireless Sensor Networks. Proc.
of Military Communications Conference 2005 (MILCOM 2005), vol. 1, pp. 59-68,
17-20 Oct. 2005.
[4] M. Zorzi, R.R. Rao, Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor
Networks: Multihop Performance. IEEE Transactions Mobile Computing, vol. 2, no. 4,
pp. 337-348, 2003.
[5] B. Chen, K. Jamieson, H. Balakrishnan, R. Morris, Span: An Energy-Efficient
Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks. ACM
Wireless Networks, vol. 8, no. 5, September 2002.
[6] A. Cerpa, D. Estrin, Ascent: Adaptive Self-Configuring Sensor Network Topologies.
Proc. IEEE INFOCOM 2002.
[7] C. Schurgers, V. Tsiatsis, M.B. Srivastava, STEM: Topology Management for
Energy Efficient Sensor Networks. IEEE Aerospace Conference ’02, Big Sky, MT,
March 10-15, 2002.
[8] J. Ansari, D. Pankin, P. Mähönen, Radio-Triggered Wake-ups with Addressing
Capabilities for Extremely Low Power Sensor Network Applications. Proc. of the 5th
European conference on Wireless Sensor Networks (EWSN 2008), Bologna (Italy),
Jan. 30 – Feb. 1, 2008.
[9] A. Keshavarzian, H. Lee, L. Venkatraman, Wakeup Scheduling in Wireless Sensor
Networks. Proc. ACM MobiHoc 2006, pp. 322-333, Florence (Italy), May 2006.
[10] IEEE 802.11, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer
(PHY) Specifications. [online], document dostępny na: http://standards.ieee.org/about/
get/802/802.11.html
[11] A. Arora, Topology Control. [online], dokument dostępny na: www.cse.ohio-state.
edu/~anish/.../Lecture5.ppt
[12] M. Wawryszczuk, The Method of Self-organizing Wireless Sensor Network
Implementation with Extended Durability (in Polish). PhD thesis, Military University
of Technology, September 2011.
[13] MicaZ Datasheet, [online]. The article accessible on: https://www.eol.ucar.edu/rtf/
facilities/isa/internal/CrossBow/Doc/MPR-MIB_Series_User_Manual_7430-002105_A.pdf
[14] Xmesh User’s Manual, [online]. The article accessible on: http://www.memsic.
com/support/documentation/wireless-sensor-networks/category/6-user-manuals.
html?download=95%3Axmesh-user-s-manual
Chapter 8
Localization Techniques
Enhanced Location Tracking
for Tactical MANETs Based on Particle Filters
and Additional Information Sources
Peter Ebinger*1, Arjan Kuijper2, Stephen D. Wolthusen3
1
AGT Group (R&D) GmbH, Darmstadt, Germany, [email protected]
2
Fraunhofer IGD, Darmstadt, Germany, [email protected]
3
Information Security Group, Department of Mathematics, Royal Holloway, U. of London,
Norwegian Information Security Lab., Gjøvik University College, Norway,
[email protected]
Abstract: The networking capabilities of tactical mobile ad-hoc networks (MANETs) provide the basis
to enhance robustness and accuracy of Blue Force Tracking (BFT) where existing BFT mechanisms are
unavailable, unreliable, or simply not sufficiently accurate (owing to factors such as update frequencies
and the need for back-link availability). BFT is an essential element to any tactical environment given
its ability to contribute to situational awareness at all levels.
Tactical environments are characterized by spectrum contention, jamming and other factors limiting
the ability of naïve approaches, e.g. in urban environments and broken terrain. Unlike previous work
this paper aims to provide MANET-based BFT without the requirement of line-of-sight (LOS) links
or back-end infrastructure which is robust against temporal disruption of network connectivity. These
results are achieved by distributedly fusing sensor data and additional information sources across
the tactical MANET using techniques also employed in robotics and object tracking.
Our contribution is the provision of enhanced BFT mechanisms exploiting networking capabilities
of tactical MANETs and data fusion mechanisms based on Sequential Monte Carlo methods, specifically particle filters, incorporating additional information such as mission information (e.g. mobility
models) and topographic data. We demonstrate that the use of these techniques enhances both accuracy and robustness as compared to standard BFT by using a simulation environment with various
mobility and radio propagation characteristics.
Keywords: Mobile Ad hoc Networks, Location Tracking, Blue Force Tracking
I. Introduction
Blue Force Tracking (BFT) is a tactical term for a localization system based
on Global Positioning System (GPS) sensors with the objective to provide location
estimates of friendly military forces (blue forces). BFT is an essential element to
*Work performed while working at Competence Center for Identification and Biometrics, Fraunhofer IGD,
Darmstadt, Germany.
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any tactical environment given its ability to contribute to situational awareness at
all levels [1]. Decreasing the friction in complex geopolitical contexts, particularly
in urban environments, may help to support decision-makers and lower the risk
of errors [2]. This has not only been incorporated into the doctrine of modern
forces’ in support of Command and Control (C2), but has also resulted in an overall
reduction of causalities [3], [1].
There are several challenges to BFT that render it a complex problem: Nodes
(military units) are typically scattered on the operation area, the environment
changes dynamically due to node mobility and operations need to be coordinated
not only within the armed forced of a specific country, but also with allies. For
rapidly evolving tactical situations in confined areas (such as those encountered
in urban areas) existing mechanisms (e.g. satellite-based systems) are limited by
several factors including update frequency, accuracy, and robustness to communication channel disruption (due to jamming or capacity limitations).
Satellite-based BFT systems highly depend on the availability of a complex
infrastructure and backend. They are therefore vulnerable to attacks on the global
infrastructure, e.g. jamming or denial of service (DoS) attacks. BFT systems based
on mobile ad hoc networks (MANETs) that are locally deployed reduce the dependencies and can improve this situation.
MANETs in conjunction with sensors and information sources found
in mobile computing components provide redundant, mutually supporting
information on individual node locations. Data sources comprise geolocation
(e.g. GPS) receivers, electronic compasses and gyroscopes, but also the ability
to perform trilateration based on radio-frequency signals used for communication [4]. The types of terrain and radio frequency environments to be expected
in tactical MANETs implies that one cannot assume continuous connectivity
across the MANET, but rather that the MANET will be partitioned frequently
in an arbitrary manner [5].
Although MANET-based BFT systems have been proposed [6], [7], these are
typically limited to basic communication mechanisms that do not provide additional means to increase the robustness and the accuracy of BFT and are therefore
inappropriate for challenging environments, e.g. urban operations or similarly
constrained terrain.
In this paper we propose a mechanism which can be used as a supplement
or even substitute for existing infrastructure-based BFT systems but which is robust against the aforementioned problems affecting naïve designs for MANETs or
similar mesh-type networks. Robustness and accuracy of location estimation are
increased within the proposed BFT system utilizing additional information sources
such as mission information (group structure and tactical mobility patterns) and
topographic data. Employing a combination of mobility models adapted to the tactical domain, the interpolation of likely node positions in the absence of immediate
updates becomes possible.
Chapter 8: Localization Techniques
351
It is important to note that a key requirement for robust BFT in the tactical
domain is that each node should retain information on current and likely positions
of other network nodes. Our proposal outlined in this paper employs Sequential
Monte Carlo (SMC) methods in a distributed manner to achieve this purpose.
In this environment Particle Filters (PF) are particularly suitable as they provide
a natural, scalable mechanism for mutual updates of location and motion estimations which decay gracefully in cases of missing updates [8]. Equally important
is their high tolerance level regarding faulty or deliberately incorrect data whilst
permitting the inclusion of additional information.
In the remainder of the paper we therefore briefly outline related work on BFT
and geolocation tracking and review background terminology for particle filters.
This provides the basis for discussion on several assumptions used in the design
of the BFT algorithm. An elaboration of the proposed particle filter algorithm
(SMC-BFT), first conceptually and then at the algorithmic level, precedes a discussion of a simulative evaluation of the algorithm based on the network simulator
developed by [9]. A comparison to a naïve baseline algorithm and a global motion
estimation algorithm concludes the paper, demonstrating significantly improved
results of the outlined algorithm with respect to the average estimation error.
II. Related work
Riblett and Wiseman [6] present a MANET-based system for tactical environments named TacNet. They point out that a MANET-based communication
approach provides the capabilities to the network to dynamically self-heal and
to overcome some line-of-sight constraints that are typical limitations of radio
networks. The TacNet system provides secure access to critical data such as realtime maps of resource positions and could be used as an underlying communication network for Blue Force tracking. However, no enhanced data fusion and
location estimation mechanisms are proposed and no evaluation of the system
performance is given [7].
Suri et al. [1] describe how BFT can benefit from a distributed approach
in comparison to centralized architectures due to the dynamic, geographically
sensitive nature of most tactical situations. They present observations from several
tactical networking experiments and discuss the requirements for and the advantages of peer-to-peer (P2P) approaches for tactical network environments. Even
in cases where connectivity to a central BFT server is lost, nodes in close proximity
continue to communicate in order to share critical location information.
A number of research papers have been published about localization schemes
for MANETs, in particular, for scenarios where no GPS sensors are available or where
only a subset of (anchor) nodes are equipped with a GPS sensor. Biaz and Ji [10]
present a survey and comparison on localization algorithms for wireless ad hoc networks, e.g. based on radio signal characteristics such as time of arrival (TOA), time
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difference of arrival (TDOA), angle of arrival (AOA) and received signal strength
indicator (RSSI). However, most of these approaches address a different scenario
in comparison to BFT as in tactical MANETs it is typically assumed that all nodes are
equipped with GPS sensors. The challenge of BFT is not to determine the location
of a local node but to have accurate location estimations for all other network nodes.
Some MANET localization approaches based on SMC are presented in the following, although they also address different scenarios in comparison to BFT. Hu
and Evans [9] introduce a SMC localization method for a MANET setup where only
a small number of seed nodes know their locations. The other nodes estimate their
location based on location messages that they receive from seed nodes. The evaluation results of the SMC-based approach are promising, even in scenarios where
network transmissions are highly irregular and severe computation and memory
limits are in place. Huang et al. [11] present a MANET localization framework
based on particle filters based on AoA and RSSI. They study how different types
of sensor data can be incorporated into a common localization framework and
claim that they provide the first localization framework based on particle-filters
that integrates heterogeneous sensor types.
Ristic et al. [12] outline the concept of Terrain-Aided Tracking. They propose
to exploit knowledge of the environment or limitations in the dynamic motion
of the target to produce more accurate location estimation results. They show
an example how additional information sources can be used in order to improve
the accuracy of state estimation based on particle filters. However, they do not address
the communication and distributed processing challenges of a MANET scenario.
Rosencrantz et al. [13] present a decentralized state estimation architecture based on particle filters for a robotic system. They address the problem that
arises in a distributed estimation systems if observation arrive that refer to a time
in the past. They introduce the concept of re-simulation in order to reduce the variance of importance weights. In cases of re-simulation part of the history of an agent’s
particle filter is erased and then re-simulated to the current time, incorporating all
collected observations at appropriate time intervals.
III. Background: Sequential Monte Carlo models / particle filters
In this section we outline the mathematical background of state estimation
using particle filters [8] – also known as sequential Monte Carlo (SMC) methods
– and how they can be applied to our application scenario:
A. System state
Current properties and status of a tactical MANET can be described using a set of state variables. For simplicity the number of nodes in the MANET
is assumed to be fixed and denoted as .
Chapter 8: Localization Techniques
353
Notation: The current state of a node at time
is denoted as 
The state is (at least partially) not directly observable but can be inferred from
sensor data. The measurements (related to target state) at time
are denoted as 
. The sequence of all available measurements up to time
is denoted
as z1:k  {z t , t  1,, k } .
The posterior probability distribution function (pdf) regarding all available measurements up to time
is denoted as p(xk |z1:k ) and is also known
as filtering distribution.
State information including BFT tracking location and other information
is collected, evaluated and transmitted to other network nodes. The current state
information at time includes the location x pos
and the velocity x kvel of all other
k
nodes. Sensors estimate the GPS coordinates of a node and the speed and direction of movement.
B. State evolution and estimation problem
Assumptions The target state evolves according to a discrete-time stochastic
model defined by the following equation:
x k  fk ( xk1 , vk1 ) (1)
where fk :  n x   n   n x is a known, possibly nonlinear function of the state
and
and

a process noise sequence.
Objective: The overall objective of our architecture is to recursively estimate
based on measurements 
that are related to the target state via the measurement equation:
z k  h k ( xk , wk ) (2)
where hk :  n x   n   nz is a known, possibly nonlinear function of the state
and the measurement noise sequence

.
From a Bayesian perspective the objective is to recursively quantify some
degree of belief in the state
at time given data z1:k up to time . Thus it is
required to construct the posterior pdf p(xk |z1:k ) which can be obtained recursively
in two stages: prediction and update.
C. Particle filters
In the following a short overview of the basic concept of particle filters including importance (re-)sampling is given [14], [12]. The key concept of particle filters
is to represent probability density functions by a set of samples (also referred to
as particles) and their associated weights. The pdf p(xk |z1:k ) is therefore approximated with an empirical density function:
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N
p (xk |z1:k )  qki   xk  xki  ,
N
q
i
k

1, i : qki  0 (3)
i 1 i 1
where is the Dirac delta function and
denotes the weight associated with
particle .
Monte Carlo Integration: Monte Carlo (MC) integration is the basis for SMC
methods. Its objective is the numerical evaluation of a multidimensional integral:
I  g  g  x  dx (4)
where 
.
Monte Carlo methods factorize 
g  x  f  x     x  in such a way that   x 
is interpreted as a probability density satisfying   x   0 and   x  , dx 
1.
The assumptions is that it is possible to draw
samples {x i , i  1,, N }
distributed according to   x  . An approximation of the integral
is the sample mean:

I g
 f  x    x  dx (5)
N
1
I N  g    f  xi . (6)
N i1
Importance Sampling: Ideally we want to generate samples directly from   x 
and estimate using equation (6). Suppose we can only generate samples from
a density q  x  which is similar to   x  , then a correct weighting of samples
makes SMC estimation still possible. The pdf q  x  is referred to as the importance
or proposal density, where q  x  and   x  have the same support. Equation (5)
can be rewritten as:
I g
  x
 f  x   x dx  f  x  q  x  q  x  dx (7)
  x
provided that
is upper bounded.
q  x
A Monte Carlo estimate of can be computed by generating
independent samples { x i , i  1,, N } distributed according to q  x  and forming
the weighted sum:
N
 {x i 
1
i
i
i









IN g

f
{
x
q
{
x
,
q
{
x
(8)

N i1
q {xi 
are the importance weights.
Chapter 8: Localization Techniques
355
A problem for consideration – in particular for distributed and cooperative state estimation – is that measurements can only be combined if they refer
to the same state, and therefore the same instance in time. Only simultaneously
measured observations can be directly combined. In the following section we
introduce our novel SMC-based BFT that overcomes these issues using resimulation whenever measurements need to be combined that refer to different
moments in time.
IV. SMC-Based BFT (SMC-BFT)
In the following we describe some basic assumptions and present some examples of additional information that can be incorporated in order to enhance BFT
in tactical MANETs. We show how re-simulation can be used to apply particle
filters in distributed environments and finally present the concept of the proposed
SMC-based BFT (SMC-BFT) in detail.
A. Basic assumptions
The overall objective of our proposed SMC-BFT approach is to derive a probabilistic estimation of a node’s location and velocity for enhanced BFT. The posterior
distribution for
is represented by a set of weighted samples {x , i  1,, N }
which are periodically updated using importance sampling. Time is divided into
discrete time units (as described in section III) where the current state of a node
at time is denoted as . Node movement and messages exchange are modeled
as discrete steps within each time unit.
SMC-BFT State Evolution Model: Location and velocity state variables evolve
according to a discrete-time stochastic model defined by equation (1) where the sysvel
tem state variable represents both node location x loc
k and velocity x k .
GPS Measurement Equation: Sensor measurements of GPS sensors are modeled according to equation (2) where the measurement
represents both location
vel
measurement z loc
and
velocity
measurement
z
of the GPS
sensor. The meask
k
urements noise sequences

are modeled as Gaussian white noise with
vel
mean zero and standard deviations  loc
GPS and  GPS respectively.
B. Incorporation of additional information sources
An important tenet of the SMC-BFT concept is the inclusion of additional
knowledge sources such as information about the tactical mission and a topographical model of the environment.
Mission Information: Knowledge of mission objectives, participants and group
structure of a specific mission are promising information sources for improving BFT.
Typical formation patterns and typical (minimum, average and maximum) velocities
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Military Communications and Information Technology...
can be derived from this form of information. Such properties may either belong
to specific nodes or reflect a specific tactical situation.
In section V-C2 an example of how group structure and movement formation
information can be used for enhanced BFT (Reference Point Group Mobility Model).
Topographical Model: A topographical model of the environment can be used
to improve estimation of node movement. The direction of movement is probabilistically dependent on properties of the area of movement [12]. There are areas with
restricted movement (e.g. water, mountains), areas of free movement (e.g. open
field) and areas which are characterized with a high likelihood of following specific
paths (e.g. roads, pathways, or stairs).
In section V-C2 an example of how such information can be used to significantly enhance BFT in a setup where nodes follow specific paths or streets (Manhattan Grid Model) is demonstrated.
Additional information sources enable the preference of more likely state
values and the exclusion of implausible estimations. For example, impossible
locations can be removed from the set of potential locations based on node type
and topography model, e.g. lakes or buildings for regular cars. Additionally impossible velocity values can be excluded based on node properties and physical laws,
e.g. based on upper bounds for acceleration.
C. Re-simulation
An important aspect of distributed data fusion is their temporal and causal
relationship: how can measurements that refer to different nodes be combined and
integrated if they were performed at different times?
The sequence of events must be considered in order to arrange measurements
of different nodes in a distributed system. Observations retrieved from other nodes
at a specific time may refer to a moment that is several steps in the past, each node
therefore records current measurement values as well as a history of received and
local measurements. This allows node’s particle filter to periodically re-simulated
when data arrives at time that refers to an earlier time tl tk (cf. [13]), which
is more recent than previously received measurements.
When this occurs a section of the filter’s history is erased and re-run to the current time , incorporating all collected observations at appropriate intervals. Measurements that refer to a moment in the past that are outside the history window
( tk tl  thistory ) are discarded. This leads to improved re-sampling of particles
and improved performance of the particle filter.
D. SMC-BFT concept
The basic concept of SMC-BFT is that each network node utilizes a set of particle filters to probabilistically model the location information of the other network
Chapter 8: Localization Techniques
357
nodes. Each particle filter is initialized when first measurement arrive and continuously updated afterwards.
The following actions performed in each round, on each node for each
of the other nodes:
1) Initialization (when first measurements arrive)
2) Prediction and Filtering
An overview of these actions is shown in figure 1 and the individual steps
of each action are described in more detail in the following sections.
1) Initialization: The particle filter for BFT of another node is initialized at time
when the first measurements (referring to some time ) of that specific
node arrive:
• Generation of samples  xli , i  1,, N  equally distributed around newly
available measurements , incorporating additional information sources, e.g.
– topography model for deletion of invalid samples or alignment of samples,
– mission information for group movement patterns.
• If required ( t l  t k ): Re-simulation from time to current time for
this node based on system evolution model (cf. equation (1)), incorporating additional information sources.
Figure 1. Basic Concept of SMC-BFT
358
2)
Military Communications and Information Technology...
Prediction and Filtering: All newly arriving incoming location messages are
preliminarily processed. The output of this process is a set of new measurements (referring to some time t l  t k in the past). For some nodes new
measurements are available in a specific round and for other nodes not.
Based on the availability of new measurements for another network node
in a specific round the BFT particle filter for a specific node is reset to a moment
in time t l1 in the past or not. Subsequently the system state is updated according
to the procedure described below. The basic input for the prediction and filtering
step is the set of samples  xli1 , i  1,, N  representing a probabilistic estimation
of the location and velocity of another node at a specific time t l  t k (or if no new
measurements are available is equal to ).
• System update based on previous system state estimation x l1 and system
evolution model (cf. equation (1), incorporating additional information source
(similar to “re-simulation” of one step).
• If new measurements are available:
1) Elimination of impossible system states: Remove all samples that are invalid
due to newly arrived measurement , e.g. that are
– too far away from the measured location,
– in an invalid location according to a topography model or
– too far away from the center of a group formation.
2) If not enough samples are left: Generation of new samples  xli , i  1,, N 
equally distributed around the newly available measurement , incorporating additional information sources.
3) If required ( t l  t k ): Re-Simulation from time to current time for this
node based on system evolution model (cf. equation (1)), incorporating
additional information source.
The goal of re-simulation is to generate new samples for the current time
 xki , i  1,, N  by probabilistically updating all previously calculated samples  x ij , l  j  k , i  1,, N  stored in the history list up to estimation time ,
e.g. applying the mobility model and incorporating mission information and
topographical model.
V. Evaluation
The key metric of BFT is the accuracy of location estimation. Estimation errors
should be minimized while limiting communication overhead to an acceptable level.
In this section we evaluate how our proposed SMC-BFT mechanism performs
and its robustness with respect to various network parameters in comparison to
standard BFT approaches.
Chapter 8: Localization Techniques
359
A. Evaluation setup
A MANET simulator developed in [9] is used as a basis for evaluation (also
used and extended in [15]). The basic setup is that each mobile node contains
a computing device, has GPS sensor capability and topography model and mission
information are available on each node.
Nodes regularly exchange data (geo-location, velocity) to direct neighbors
via multi-hop flooding with all other nodes (e.g. piggyback with routing information). Location measurement and message processing are performed in discrete
time steps (round based simulation) where the duration of each step was set to 1 s.
B. Standard BFT approaches
We compare our newly proposed SMC-BFT method with two standard BFT
mechanisms: A simple straight forward BFT mechanism, which simply takes
a measurement “as is” and a more elaborate mechanism based on dead reckoning
(cf. [16]) considering the progress in time between last measurements and current
time as well as node velocity.
a) Standard BFT (STD-BFT): The most recently received measurement , (GPS
coordinates and velocity) is taken as an estimate of current location and velo­
city of a node:
x k  zl
b)
Velocity Based BFT (VEL-BFT): The location accuracy is improved by predicting the current location based on available measurement data (cf. [2],[17]).
Previous node location, time difference t  tk t between measurement
time and current time as well as node velocity are considered:
xkpos
 zlpos t  zlvel
xkvel  zlvel
C. Experimental setup and simulation parameters
In the following sections we describe the default parameter set used for our
evaluation. It is explicitly mentioned in the description whenever default values
are not used.
1) Basic Parameters: The default setup contains 20 nodes that move within a simulation area of 1000 x 1000 m. Nodes locally determine their location and
velocity using the GPS sensor. The GPS measurement standard deviation for
vel
location is  loc
3.0 m and for velocity and  GPS

1.0 m/s. Every 10 s each
GPS 
node generates a location message and forwards it to all its neighbor nodes.
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Each simulation is carried out for 250 s (i. e. 250 rounds) of which the first 50 s
are left out as initialization and stabilization period and therefore are not considered for the evaluation. Each simulation setup is run in 50 iterations to determine
stable average values. The number of particles for the SMC-BFT approach is 50.
2) Mobility Model: For the evaluation we implemented a mobility model reflecting the topography of the environment and a group based model reflecting
tactical formations.
Manhattan Grid Model: The Manhattan Grid Model [18] is based on road
topology. Roads are located in a grid structure and nodes move in horizontal or
vertical directions on these roads. The model follows a probabilistic approach: each
node probabilistically chooses at each intersection to keep moving in the same
direction or to turn left or right.
For the evaluation we implemented the Manhattan Grid Model based on
the BonnMotion [19] algorithm. This model is used as default mobility model for
the evaluation. There are 10 blocks in each direction (i. e. each block is 100 x 100 m)
and the probability for mobile nodes to turn at a crossing is 0.5. The probability
for a node to change its speed (in a simulation round) is 0.2. The minimum speed
is 0.5 m/s, the mean 2.0 m/s and the standard deviation of a normally distributed
random speed 2.0 m/s. The pause probability is zero.
Reference Point Group Mobility (RPGM): The Reference Point Group Mobility (RPGM) Model [20], [21] can represent tactical relationships among a group
of mobile nodes. Applications of the RPGM model include complex scenarios such
as a military maneuver with joint aircraft, tank and infantry operations or two-level
scenarios, e.g. infantry and helicopters, with slow and fast nodes.
For the evaluation the following default parameter set is used. There is a group
of 20 nodes that move with a common average speed. The minimum group speed
is 4 m/s and the maximum 10 m/s. The overall group movement follows a random
waypoint mobility model. Each node chooses a destination randomly distributed around
a group destination in a distance of up to 25 m from this specified location. In each
step a node may also randomly move up to 30 percent of this maximum distance.
3) Radio Propagation Model: During evaluation a deterministic and a probabilistic
radio propagation model are used.
Free Space Model: The Free Space Model is a basic deterministic model where
only line-of-sight radio transmissions through free space (without any obstacles,
reflection or diffraction disturbances) are taken into account. For the evaluation
we use the available simulator implementation [9].
Shadowing Model: In reality radio propagation is a probabilistic process due
to multipath propagation effects. The shadowing model consists of two distinct
parts: a path loss model (which predicts the mean received power) and a second
that reflects the variation of the received power at a certain distance.
For the evaluation an implementation based on ns-2 [22] is used. This model
is used as default radio propagation model with a radio transmission range of 250 m.
Chapter 8: Localization Techniques
361
The pathloss exponent is set to 4.0 (recommendation for outdoor environment/
shadowed urban area: 2.7 to 5 [22]) and a shadowing deviation  dB  8.0 is used
(recommendation for outdoor environments: 4 to 12 [22]).
D. Accuracy of location estimation
Field Size (Node Number, Node Density)
Estimation Error [m]
40
30
STD-BFT
VEL-BFT
SMC-BFT
20
10
0
250
500
750
1000
Field Size [m]
1250
1500
Figure 2. Average Estimation Error vs. Node Density for Manhattan Grid Model
2)
Field Size, Node Number and Node Density: In this evaluation setup we increase
the field size (incrementally from 250 x 250 m to 1500 x 1500 m) proportional to
the number of nodes (5 to 30). Therefore node density decreases with increasing
field size and node numbers. Figure 2 shows that the estimation error stays on
a similar level as long as node density provides some basic connectivity of all
nodes, but decreases when network connectivity is disturbed. The SMC-BFT
method outperforms other methods in all setups by 16 to 41 percent.
Mobility Model Parameters: In figure 3 and figure 4 simulation results are
shown for different speed settings for Manhattan Grid and RPGM models
respectively. The results for both mobility models show that STD-BFT works
well for scenarios with low mobility.
Node Speed
40
Estimation Error [m]
1)
30
STD-BFT
VEL-BFT
SMC-BFT
20
10
0
1
2
3
Average Speed [m/s]
4
Figure 3. Average Estimation Error vs. Node Speed for Manhattan Grid Model
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Group Speed
Estimation Error [m]
40
30
STD-BFT
VEL-BFT
SMC-BFT
20
10
0
2
4
6
8
Maximum Speed [m/s]
Figure 4. Average Estimation Error vs. Group Speed for RPGM Model
In figure 3 the minimum speed for the Manhattan Grid Model is set to 0.5 m/s
for speeds below 3 m/s and to 1 m/s otherwise. Mean speed as well as the standard
deviation are increased from 1 m/s to 4 m/s. Estimation errors grow relatively
proportional to speed. Average estimation errors of SMC-BFT are 23 to 48 percent
lower than for both standard BFT methods.
In figure 4 maximum group speed of RPGM is increased from 2 m/s to 8 m/s,
minimum group speed is increased proportionally from 0.5 m/s to 2 m/s. Average
estimation errors of SMC-BFT are 12 to 55 percent lower than for the other methods.
Radio Range and Radio Propagation Model
150
STD-BFT: Free Space
VEL-BFT: Free Space
SMC-BFT: Free Space
STD-BFT: Shadowing
VEL-BFT: Shadowing
SMC-BFT: Shadowing
Estimation Error [m]
125
100
75
50
25
0
150
200
300
400
Radio Range [m]
Figure 5. Radio Propagation Model and Radio Range
3)
Radio Propagation Model Parameters: The following setup evaluates the radio
propagation models influence on BFT performance. For these purposes two
radio propagation models (Free Space and Shadowing) are used with varying
radio ranges from 150 m to 400 m where radio range is the maximum transmission range for the Free Space model and the average transmission range for
the Shadowing model. Figure 5 shows that error estimation is significantly lower
Chapter 8: Localization Techniques
363
for the Shadowing model. When even a few messages are transmitted beyond
“normal” (Free Space) radio range it significantly improves location estimation
due to an increase in valuable information that would otherwise not be available to
that part of the network. The results show that the average estimation error is lower
for SMC-BFT for all evaluated setups than for both standard BFT methods.
Accuracy of GPS Messearument
Estimation Error [m]
20
15
10
5
0
STD-BFT
VEL-BFT
SMC-BFT
2
3
4
5
Standard Deviation of GPS Measurements [m]
Figure 6. GPS Measurement Accuracy
4)
GPS Measurement Accuracy: Accuracy of sensor measurements has a direct
impact on BFT performance. Figure 6 shows results for varying GPS precisions
(GPS standard deviation 2 m to 5 m). Estimation error increases are relatively
proportional to GPS accuracy where the estimation error of the proposed
SMC-BFT is 25 to 38 percent than for the other methods.
Table I. Number of Messages and Average Estimation Error vs. Message Generation Interval
Message Generation Interval
Average Number of Generated Messages
Average Number of Forwarded Mess.
5 s
10 s
20 s
481
240
120
33512
16576
8488
Average Estimation Error
STD-BFT
8.4 m
13.7 m
24.5 m
VEL-BFT
6.7 m
10.9 m
20.9 m
SMC-BFT
4.6 m
8.1 m
15.0 m
E. Communication overhead
In this section we evaluate BFT communication overhead and the resulting
location estimation errors in dependency of message generation intervals. As message generation, forwarding and processing do not depend on the data fusion
algorithm, the number of messages that are generated and exchanged are equivalent
for all three methods.
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Table I shows that the quantity of messages significantly decreases when location message generation time intervals are increased. Similarly location estimation
error increases proportionally to a decrease in message generation intervals where
SMC-BFT outperforms the other approaches in all scenarios by 25 to 45 percent.
VI. Conclusion and outlook
In this paper we demonstrated how BFT can be significantly enhanced
by using networking capabilities of tactical MANETs and data fusion based on
particle filters. Using advanced data analysis and fusion mechanisms for multiple
sensor and data sources based on techniques that are employed in robotics and
object tracking, we developed a model for enhanced BFT in tactical MANETs by
incorporating additional information such as mission information (e.g. mobility
models) and topographic data. As shown in the simulation environment using
various mobility and radio propagation characteristics (cf. figure 2 to 6), our
proposed SMC-BFT model enhanced both accuracy and robustness as compared
to existing models.
This paper has outlined mechanisms that can be used to implement more accurate and robust BFT systems for tactical MANETs. Our proposed model can be
used as a replacement if no satellite infrastructure is available, or as a supplement
to an existing backend system thereby improving local precision while maintaining
a coarse global overview, e.g. using a satellite system.
References
[1] N. Suri, G. Benincasa, M. Tortonesi, C. Stefanelli, J. Kovach, R.Winkler,
U.S. Kohler, J. Hanna, L. Pochet, and S. Watson, “Peer-to-Peer Communications
for Tactical Environments: Observations, Requirements, and Experiences,” IEEE
Communications Magazine, vol. 48, pp. 60-69, 2010.
[2] P. Labbe, L. Lamont, Y. Ge, and L. Li, “Creating a Dynamic Picture of Network
Participant Geospatial Information in Complex Terrains,” in Proceedings of the Second
International Conference on Internet Monitoring and Protection. Washington, DC,
USA: IEEE Computer Society, 2007, pp. 39.
[3] K.R. Chevli, P.Y. Kim, A.A. Kagel, D.W. Moy, R.S. Pattay, R.A. Nichols, and
A.D. Goldfinger, “Blue Force Tracking Network Modeling and Simulation,” in IEEE
Military Communications Conference (MILCOM), 2006.
[4] R. Filler, S. Ganop, P. Olson, and S. Sokolowski, “Positioning, Navigation and
Timing: The Foundation of Command and Control,” in Command and Control
Research and Technology Symposium (CCRTS), 2004.
[5] S. Reidt and S.D. Wolthusen, “Connectivity Augmentation in Tactical Mobile
Ad hoc Networks,” in IEEE Military Communication Conference (MILCOM), 2008.
Chapter 8: Localization Techniques
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[6] L. Riblett. and J. Wiseman, “TACNET: Mobile ad hoc Secure Communications
Network,” in 41st Annual IEEE International Carnahan Conference on Security
Technology, 2007.
[7] L. Riblett and J. Wiseman, “TacNet Tracker©: Built-in Capabilities for Situational
Awareness,” in 42nd Annual IEEE International Carnahan Conference on Security
Technology, 2008.
[8] P. Ebinger and S. Wolthusen, “Efficient State Estimation and Byzantine Behavior
Identification in Tactical MANETs,” in IEEE Military Communications Conference
(MILCOM), 2009.
[9] L. Hu and D. Evans, “Localization for Mobile Sensor Networks,” in 10th ACM Annual
International Conference on Mobile Computing and Networking (MobiCom), 2004.
[10] S. Biaz and Y. Ji, “A Survey and Comparison on Localisation Algorithms for Wireless
Ad Hoc Networks,” Int. J. Mob. Commun., vol. 3, pp. 374-410, May 2005.
[11] R. Huang and G.V. Zaruba, “Incorporating Data from Multiple Sensors for Localizing
Nodes in Mobile Ad Hoc Networks,” IEEE Transactions on Mobile Computing, vol. 6,
pp. 1090-1104, September 2007.
[12] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle
Filters for Tracking Applications. Artech House, 2004.
[13] M. Rosencrantz, G. Gordon, and S. Thrun, “Decentralized Sensor Fusion with
Distributed Particle Filters,” in 19th Conference on Uncertainty in Artificial Intelligence
(UAI), 2003.
[14] A. Doucet, N.D. Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods
in Practice. Springer, 2001.
[15] A. Baggio and K. Langendoen, “Monte Carlo Localization for Mobile Wireless
Sensor Networks,” Ad Hoc Networks, vol. 6, no. 5, pp. 718-733, Jul. 2008.
[16] A. Agarwal and S.R. Das, “Dead Reckoning in Mobile Ad Hoc Networks,” in IEEE
Wireless Communications and Networking (WCNC), vol. 3, 2003, pp. 1838-1843.
[17] E. Amar and S. Boumerdassi, “Enhancing Location Services with Prediction,”
in Proceedings of the 2009 International Conference on Wireless Communications
and Mobile Computing: Connecting the World Wirelessly, ser. IWCMC ’09. New
York, NY, USA: ACM, 2009, pp. 1025-1029.
[18] Special Mobile Group (SMG), “Universal Mobile Telecommunicatios System (UMTS) –
Selection Procedures for the Choice of Radio Transmission Technologies of the UMTS,”
European Telecommunications Standards Institute (ETSI), Tech. Rep., 1998.
[19] N. Aschenbruck, R. Ernst, E. Gerhards-Padilla, and M. Schwamborn,
“BonnMotion: A Mobility Scenario Generation and Analysis Tool,” in 3rd International
ICST Conference on Simulation Tools and Techniques, 2010.
[20] X. Hong, M. Gerla, G. Pei, and C.-C. Chiang, “A Group Mobility Model for Ad Hoc
Wireless Networks,” in 2nd ACM International Conference on Modeling, Analysis
and Simulation of Wireless and Mobile Systems (MSWiM ’99), 1999.
[21] T. Camp, J. Boleng, and V. Davies, “A Survey of Mobility Models for Ad Hoc
Network Research,” Wireless Communications and Mobile Computing, vol. 2, no. 5,
pp. 483-502, 2002.
[22] K. Fall and K. Varadhan, The ns Manual, May 2010, available online at
http://www.isi.edu/nsnam/ns/doc/ns_doc.pdf; accessed 2012-07-26.
Spatial Localization of Radio Wave Emission Sources
Using SDF Technology
Jan M. Kelner, Piotr Gajewski, Cezary Ziółkowski
Military University of Technology, Warsaw, Poland,
{jkelner, pgajewski, cziolkowski}@wat.edu.pl
Abstract: The paper is devoted to the question of locating of radio wave emission sources by using
of SDF technology. The generalized algorithm to estimate the radio transmitter coordinates has been
presented. The Doppler characteristics are the basis to determine the location coordinates of a source. They are obtained as the result of measuring of instantaneous frequency of a signal received on
board of an airplane.
Keywords: location techniques, Doppler shift, Signal Doppler Frequency (SDF), 3D localization
I. Introduction
The localization of mobile radio waves transmitters is more and more interesting service both for networks users of and for operators of Electronic Warfare
(EW) systems. Many location techniques have been already studied and described,
mainly [6]-[12]: CoO (Cell of Origin), AoA (Angle of Arrival), RSS (Received Signal
Strength), TOA (Time of Arrival), TDoA (Time Difference of Arrival), GPS/A-GPS
(Assisted GPS), FDoA (Frequency Difference of Arrival) as well as the so-called
hybrid methods.
Above methods have some advantages and disadvantages for using in practical
systems. Mostly, the location in two dimensions (2D) is these methods main limitation. The article presents an idea of the method for location in 3D that use
the values of signal Doppler shift which is measured on the mobile flying platform
(helicopters, planes, drones).
The SDF (Signal Doppler Frequency) technology [2] is based on the analytical
description of the Doppler effect [1]. It uses the distinctive nature of Doppler curves
resulting from the reciprocal location of signal sources and receiver in relation to
the movement of the objects trajectory.
Because of using the Doppler effect, the SDF method is compared to FDoA
one. However, the SDF and FDoA methods present considerably different approaches to the location techniques. The possibility of object spatial 3D localizations
is a main advantage of the SDF technology, which is presented below.
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Military Communications and Information Technology...
Moreover, the main propriety of SDF method is its high precision as well as independency for the time-frequency structure of signals emitted by objects being
localised. It gives the possibility to use the method universally and autonomously
in numerous practical applications. Several applications of the SDF technology have
been presented till now, among others in the area of: reconnaissance and electronic
warfare, spectrum monitoring, sea and land rescue, and navigation.
II. Characteristic of SDF method
The SDF technology bases on the analytical description of the Doppler frequency [1]. The expression describing the variability of the Doppler frequency
has the following form [1]:

k 
x  vt

 f 0 ,(1)
 
f
D x, t
2
2 k 
1 k 
 x  vt   1 k 2  y 2  z 2  

where: k  v c , – the difference of velocity between a signal source and the receiver, – the electromagnetic wave propagation speed in the medium,
– the
carrier frequency of the emitted signal, x   x, y , z  – coordinates of the signal
source location.
Above formula shows that the value of the Doppler frequency shift is a function
of signal source location coordinates. This fact has become the essence of elaborated method is a measurement of the instantaneous value of the signal frequency
by a moving measurement receiver. Basing on (1), particular coordinates x, y, z of the transmitter location can be determined as the functions of the Doppler
frequency shift measured in various time moments. In the case, when the receiver
is moving on a fixed height above the flat terrain, the coordinates are known and
fixed values. Making elementary transformations of the equation (1) we achieve
formulas describing the coordinates x and y of the signal source [2]-[5]:
xv
t1 A t1   t2 A t2 
,(2)
A t1   A t2 
2
where:
1  v t1  t2  A t1  A t2  
y


  z 2 ,(3)
1 k 2 
A t1   A t2 

A t  
1 F 2 t 

, F t 
F t 
f D t  1 k 2
 k .(4)
f0
k
So, in order to determine coordinates of signal source, it is necessary to measure the Doppler frequency value
in two moments and . Therefore, it is
Chapter 8: Localization Techniques
369
possible to determine locations of emitting radio wave sources during the receiver
movement, basing on the measurement of the Doppler frequency shift.
The idea of SDF 3D method is described below.
III. SDF spatial localization method
The algorithm of localization procedure is presented in the Fig. 1. The following simplifications were assumed in the analysed scenario [2]:
• the signal generated by the localised transmitter is received at any point
of the space where the localization stand (LS) is placed;
• the localised signal source is immovable and it is placed on the flat Earth
surface;
• the LS is moving at a defined height over the Earth, what means that only
the direct component is taken into account in the signal coming from
the source to the receiver;
• the operator of the LS knows the frequency of the signal transmitted
by the localised source.
Figure 1. Algorithm of the SDF spatial localization procedure
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Here, the several possible situations of localization using an aircraft are taken
into account. They depend on the knowledge of an approximated direction for
the transmitter. So, Algorithm contents three blocks. Block (A) corresponds to
the situation, when the LS operator knows the approximated direction to the signal source. Block (B) is using if the LS operator does not know the approximated
direction to the signal source, and the stand is moving in the direction, for which
is not possible the coordination determining. And finally block (C) corresponds
to the situation where the LS operator does not know the approximate direction
to signal source, but he is able to determine transmitter coordinates x and y on
the flying route.
The task of part B is to define the precise coordinates of the transmitter using
procedure Z, presented in the Fig. 2 [2].
Figure 2. Illustration of Z-procedure
The Z procedure is brought to define 3-phase flight route of LS between
the points (1)→(2)→(3)→(4). In the first phase between the points (1)→(2), the ap-
Chapter 8: Localization Techniques
371
proximate transmitter’s coordinations can be determined. Knowing the approximated direction to the localised transmitter, it is possible to select a suitable flight
direction within the first phase of the procedure Z. This direction should be established so that the Doppler frequency shift changed [2]. In the point (2), the change
of the movement direction takes place in the OXY plane by the angle
(Fig. 2)
defined by the formula [2]:
y
s
1   xy  s  arctg
 arccos
x  vt
Dxy
(5)
x  vt
s
 arccos
 arccos
,
Dxy
Dxy
2
where: Dxy   x  vt   y 2 , s – the length of the route segment between point (2)
and point (X) (Fig. 2), where the Doppler frequency is equal to zero.
This phase of the Z procedure allows to achieve the coordinate values
of the transmitter position with high accuracy. It is resulted from the suitable selection of the route where almost symmetrical part of the Doppler curve is achieved
around the value of . The point (X) in the Fig. 2 is called the Point of the Closest Approach – PCA [12]. The selection of the length 2s of the route segment between
the points (2)→(3) depends on the distance of the localization stand to the signal
source. The question of selection of the measurement segment 2s is presented
in the references [2,13]. In the point (3) the next change in the movement direction
takes place in the plane OXY by the angle of (Fig. 2) described by formula [2]:

2
y ' 
vt ' x ' 
  arctg
,(6)
y ' 2
y' 
where:  x ', y ', z ' – coordinates of the signal source position determined in the second phase of the procedure Z, – the time measured from the moment of changing
the movement direction in the point (2), y ' y ' – a factor responsible for the sign
of the angle
and providing the proper change of the direction (to the right or
to the left) in the point (3).
Within the third phase of the procedure Z the plane flights in the direction
to the transmitter. The change in the direction for the route (3)→(5) (Fig. 2) is executed similarly to the point (2) of the procedure Z. The precise coordinate values
of the source position are determined in this way.
When the direction to the transmitter is not known and Doppler shift does not
changed, the block (B) is worked out. Here, three situations can be distinguished:
a) the stand moves away from the transmitter that is in a large distant from
the receiver ( f D t    f D max ),
b)the stand moves closer to the transmitter, but is in a large distant from it ( f D t   f D max ),
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c) the stand moves (closer or away) in small distant of the source, but the flight
direction overlaps (with a margin) the direction to the signal source
( f D t   f D max ).
The Fig. 2 presents an example of such situation by moving the LS between
the points (6)→(7). Then, the diametrical change in the movement direction is required in the plane OXY by the angle of ±90° related to the present movement
direction. Next, the procedure Z is executed. The segment (7)→(2) corresponds to
the (1)→(2) one in the procedure Z as for the situation (A).
The last block (C) corresponds to the situation where the LS operator does
not know the approximate direction to signal source, but he is able to determine
coordinates x and y of the source position on the route of his movement (the segment (1)→(2) in the Fig. 2. The coordinate y is then defined ambiguously. It is not
known if the signal source is on the right or on the left side of the LS movement
trajectory (the segment (1)→(2) in the Fig. 2). Therefore, the LS direction change
in the point (2) can be done in the incorrect direction. In such a case, the movement
of the receiver in the direction (2)→(8) will cause that f D t    f D max . The return
(8)→(2) and further continuation of the flight according to the procedure Z is required in such situation.
IV. Simulation results
The SDF method was verified by simulations and measurements for 2D localization. Some results have been presented in [4],[5]. The procedure Z has been
positively verified by simulation tests for the rescue action scenario [14]. Some
simulation results are presented below.
Figure 3. Simulation scenario
Chapter 8: Localization Techniques
373
Fig. 3 shows the simulation scenario and the flight trajectory in 2D. The aircraft
position is in geographical UMT standard. At the top, the positioning accuracy
results obtained for the point F is shown. This is the point with the best accuracy
of location because of the most distinguish change of Doppler frequency (Fig. 4).
Figure 4. Doppler frequency courses
Figure 5. Localization accuracy
The r localization accuracy is shown in Fig. 5. The best results are obtained
during the flight in sector EF, after few changes of flight route.
V. Summary
The paper shows the possibility of using the space platform to spatial localization of the radio emission sources (both stationary and movable).
The SDF method in the spatial localization can find wide applications in air and
sea rescue systems as well as in national security agencies. Installing the localization
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stand on helicopters and aircrafts increases the range and accuracy of the method
in relation to the ground localization.
The exact characteristics of this method, the many experiments should be
worked out. At present, the preparations are in progress to develop the new research
project. Within this grant it is expected to carry out researches and the attempt
to implement the SDF technology on an aircraft from the Navy Sea Rescue Unit.
The expected better precision of the Doppler localization method in the case
of placing the localization stand on aircraft than placed on land vehicles allows
to infer of high capacities of the method, which can be also used for air navigation, particularly within aircraft landing systems in reduced visibility conditions.
The possibility to use the SDF method to navigation purposes is presented, among
others, in [2],[13].
References
[1] J. Rafa, C. Ziółkowski, “Influence of transmitter motion on received signal parameters
– Analysis of the Doppler effect”, Wave Motion, vol. 45, no. 3, pp. 178-190, January
2008.
[2] J.M. Kelner, Analiza dopplerowskiej method lokalizacji źródeł emisji fal radiowych
(Analysis of the Doppler location method of the radio waves emission source),
Ph.D. thesis, Military University of Technology, Warsaw, Poland, 2010, (in Polish).
[3] P. Gajewski, J.M. Kelner, C. Ziółkowski, “Subscriber location in radio communication
nets”, Journal of Telecommunications and Information Technology, no. 2, pp. 88-92,
2008.
[4] J.M. Kelner, C. Ziółkowski, L. Kachel, “The empirical verification of the location
method based on the Doppler effect”, 17th International Conference on Microwaves,
Radar and Wireless Communications MIKON 2008, Wroclaw, Poland, vol. 3, pp. 755-758,
May 2008.
[5] P. Gajewski, C. Ziółkowski, J.M. Kelner, “Mobile location method of radio wave
emission sources”, Progress in Electromagnetics Research Symposium PIERS 2009,
Moscow, Russia, August 2009.
[6] A. Amar, A.J. Weiss, “Localization of narrowband radio emitters based on
Doppler frequency shifts”, IEEE Transactions on Signal Processing, vol. 56, no. 11,
pp. 5500-5508, 2008.
[7] Y. Zhao, “Standardization of mobile phone positioning for 3G systems”, IEEE
Communications Magazine, vol. 40, no. 7, pp. 108-116, 2002.
[8] M. Vossiek, L. Wiebking, P. Gulden, J. Wieghardt, C. Hoffmann, P. Heide,
“Wireless local positioning”, IEEE Microwave Magazine, vol. 4, no. 4, pp. 77-86, 2003.
[9] I.J. Gupta, “Stray signal source location in far-field antenna/RCS ranges”, IEEE
Antennas and Propagation Magazine, vol. 46, no. 3, pp. 20-29, 2004.
[10] A. Küpper, Location-based services. Fundamentals and operation, John Wiley & Sons,
Chichester, UK, 2005.
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375
[11] K.W. Kołodziej, J. Hjelm, Local positioning system. LBS applications and services,
CRC Press, Boca Raton, FL, USA, 2006.
[12] N. Levanon, M. Ben-Zaken, “Random error in ARGOS and SARSAT satellite
positioning systems”, IEEE Transaction on Aerospace and Electronic System, vol. AES-21,
no. 6, pp. 783 790, 1985.
[13] P. Gajewski, C. Ziółkowski, J.M. Kelner, “Influence of length and location
of the measurement route on location accuracy of the radio waves sources using
the SDF method”, Przegląd Telekomunikacyjny i Wiadomości Telekomunikacyjne,
vol. LXXXIII, no. 8-9, pp. 1360-1369, August-September 2010 (in Polish).
[14] C. Ziółkowski, J.M. Kelner, “Using the Doppler methodology for object location
estimation in lifeboat service”, 6th International Conference on: Perspectives and
Development of Rescue, Safety and Defence Systems in the 21st Century RSDS 2008,
Gdansk, Poland, June 2008.
On the Effect of Tuner Phase Noise
on TDOA Measurements
Anders M. Johansson, Patrik Hedström
Swedish Defence Research Agency, Linköping, Sweden,
{ajh, pathed}@foi.se
Abstract: Radio geolocation of unknown signals using time difference of arrival techniques is dependent on signal measurements that are recorded using a time and frequency coherent measurement
system. This paper investigates what effect the phase noise in the reference oscillator inside the tuner
has on the geolocation accuracy, and proposes a model for the phase noise in addition to a method
for counteracting its effect. The results include both real world measurements and computer simulations. Results from a field experiment indicate that the proposed method outperforms traditional
time difference of arrival techniques by a factor between 3 and 5.8 depending on SNR.
1. Introduction
Time difference of arrival (TDOA) techniques for geolocation of unknown
signals has been researched within acoustics since the First World War [1]. In later
years systems for measuring the TDOA on radio signals has been developed and
a few military systems based on TDOA techniques are in use today. One problem
when dealing with narrow band signals or signals with poor signal to noise ratio
(SNR) is that long measurement times are required to reach sufficient TDOA
accuracy. This can easily be seen by studying the Cramer-Rao Bound (CRB),
for details see [2]. In particular, narrow band signals with poor SNR requires
measurement times exceeding one second in order to reach sufficient accuracy.
In this paper we show that phase noise in the reference oscillator used in the radio
receivers impacts negatively on the accuracy of TDOA estimation. Furthermore,
we present a method for modeling the phase noise, and results from a real world
field trial. The results are presented using both real measurements and computer
simulations. A method for counteracting the problems arising from the phase
noise is also presented.
Section 2 presents a signal model and an oscillator model, followed by a description of the TDOA algorithm including modifications to counteract the phase
noise in Section 3. Measurements and simulations are presented in Section 4, and
conclusions in Section 5.
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2. Signal models
Assume a band limited signal s(t ) , where denotes time, transmitted from
a position in space, impinge on a group of receivers placed in the positions
, m  {1,2,, M }, see Figure 1. By comparing the received base band signals xm (t ) ,
the TDOA algorithm can be used to form an estimate ˆ of the transmitter position.
Figure 1. System model
The propagation channel from to
is denoted hm (t ) and is considered to
be stationary over the measurement time . At each receiver, the received signal
is corrupted by noise denoted vm (t ) , which is considered spectrally white, time
invariant and uncorrelated between the receivers. The modulation frequency is denoted
and the signal bandwidth . The local oscillator of the receiver is assumed
to be unstable with the phase noise m (t ) . The signal chain from the transmitter
to receiver
is depicted in Figure 2. The received signal is:

xm (t )  hm (t )e j ( c tm ( t ))  * s (t )  vBm (t ), (1)
where denotes convolution. The above equation shows that the output signal,
xm (t ) , from the receiver is a demodulated version of the system impulse response
convolved with the transmitted base band signal.
The base band noise term, vB m (t ) , in Equation (1) is [3]:

vBm (t ) e j ( ctm ( t ))  vm  jH  vm  , (2)
where H () denotes the Hilbert transform.
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379
Figure 2. Signal model
2.1. Free space propagation
Assuming free space propagation without attenuation, the propagation channel simplifies to a pure delay hm (t )  (t   m ) , where
is the propagation delay
in seconds between the transmitter and receiver , and is calculated according to:
 q  pm 
m 
, (3)
c
where    denotes vector norm, and
the speed of light.
2.2. Oscillator phase noise
The model for phase noise is here defined as flicker noise denoted m (t )
added to the centre frequency of the reference oscillator. Typically this noise is what
causes Allan variation [4].
m (t )  t  f m (t ), (4)
where
is the noise power. The power spectral density (PSD) of the flicker noise
1
m (t ) is proportional to N . Both
and
are oscillator specific constants,
f
where oscillators with high precision typically have a low value of and a high
value of and vice versa. Several numerical methods for simulating flicker noise
have been presented in the literature, the method used here is a fast Fourier transform (FFT) based method called “the discrete spectrum method”, and is described
in [5]. Section 4.1 presents a method for estimating the constants
and
for
a real oscillator.
As the power of the flicker noise is concentrated at very low frequencies, it is
possible to model the oscillator as stable with a phase offset for short measurement
times. A simplified model for the noise for short measurement times is
m (t ) 
 m (t ), (5)
where  m (t ) is a constant over t  Ts / 2  t  t  Ts / 2 for small values of the measurement time . Note that due to the phase noise, the phase offset can be assumed
to vary between measurements.
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3. The TDOA algorithm
The TDOA algorithm estimates the position of the transmitter by identifying
the time difference of arrival between all receiver pairs. This can be done either in the time
domain or in the frequency domain, by analysing the cross correlation or the cross
spectral density (CSD) between the received signals. To study the effect of phase noise,
we have here chosen the frequency domain approach described in [6].
Using Equation (1) the CSD between the receivers
and for free space
propagation is derived as
*
Rxm ,n ( , t0 ) 
 E
 xm ( t ) xn ( t ) 
 (6)
 H m (  c ) H n* (  c ) Rs ( )e j ( m ( t0 )n ( t0 )) 
Rvm n ( ) (7)

Rs ( )e j ( c )( m n )e j ( m ( t0 )n ( t0 ))  Rvm ,n ( ), (8)
for short measurement times. In the above equation,   denotes Fourier transform and E  is the expected value operator, () denotes complex conjugate,
H m ( )  hm (t ) , Rs ( ) is the PSD of the transmitted signal s(t ) and Rvm n ( )
is the CSD between the noise in xm (t ) and xn (t ) .
To estimate the TDOA we remove the influence of constant phase terms according to
Rxm , n ( , t0 )
R xm , n ( , t0 ) 
(9)
Rxm , n (0, t0 )
 e j( m n ) , (10)
where we have made the assumption that Rs ( ) / Rs (0) 
1 without loss of generality,
and the measurement time is sufficient such that the approximation Rvm n ( ) 0
can be made. The above equation shows that the phase reveals the TDOA:
ˆ
 m n   m   n . By calculating estimates 
m n of the TDOA for multiple receiver
pairs it is possible to solve for the transmitter position using Equation (3). Estimating is however not the focus of this paper, the rest of the paper will therefore
concentrate on the two receiver case to highlight the effect of oscillator instability.
In a practical application the CSD is estimated by sampling the time signals
at the sample frequency , xm (t )  xm ( n / Fs ) where  , and splitting them
into
element segments
R xm n
( )
L
X

( k , m) X  ( k , n), (11)
where X  ( k , m ) is the th FFT bin of sensor

and signal segment
.
Chapter 8: Localization Techniques
381
Ts Fs
,
K
the approximation made in Equation (5) holds, and the phase is stable over the segments. A problem does however arise when the SNR is poor or when very
high SNR levels are required. In these cases the value of must be large to reduce
the impact of the noise on the TDOA estimate. From Equation (8) we can see that
by adding components with different phase (  m (t )   n (t ) will be different for different values of ) the effective SNR will drop as increases. As a result the TDOA
estimation accuracy also drops as the measurement time increases.
 (t ) over
The solution to the problem is simply to estimate the TDOA 
mn
multiple short time samples whereby a simple incoherent data fusion (IDF) can be
performed using time averaging:
By studying Equation (11), we find that for low values of , i.e. when L 
ˆ 

m,n
T
1
 (t ) (12)
T t1 m , n
 (t ) , is less
The measurement time used to calculate each TDOA estimate, 
mn
than . The resulting algorithm is here denoted IDF-TDOA.
4. Measurements
4.1. Oscillator modeling
The oscillator parameters
and
were estimated using the experimental
setup depicted in Figure 3. The experiment was designed to make it possible to
measure oscillator parameters without using a stable frequency reference. The input
for the experimental setup was a signal generator and the signal was received and
saved to mass storage using a custom built data acquisition system. The signal
generator was adjusted to a frequency 1 kHz above the tuning frequency for
the two tuners.
A simulation of the measurement setup was made in order to determine
the flicker noise parameters. The PSD of the output signal y ( n ) for the measurement
and the simulation setup is depicted in Figure 4. The measurement time was 30 s.
The oscillator parameters were experimentally determined to be  f 
0.0008
and N = 3.8. The figure clearly show the low frequency characteristic of the flicker
noise in the oscillator.
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Figure 3. Experimental setup for evaluating oscillator stability
Figure 4. PSD for simulation and experiment setup
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383
4.2. TDOA accuracy
In order to investigate the effect of oscillator instability on TDOA estimation
for varying measurement times the signal generator in Figure 3 was replaced by
a vector signal generator generating a 10 kHz wide 8-PSK signal. In the experiment,
independent white noise was added to the signal in the digital domain to study
the effect of varying SNR. In Figure 5, the standard deviation in micro seconds
is displayed as a function of measurement time for SNR levels of 65 dB, 25 dB and
5 dB, along with the CRB and the measurement system accuracy for TDOA and
IDF-TDOA. The constants
and L  32 . The CRB is displayed for 5 dB and
25 dB SNR levels only. The standard deviation is estimated by calculating an estimate of the TDOA on 490 data segments drawn from 400 seconds of recorded data.
The figure shows that the oscillator noise reduces the accuracy for long measurement times for traditional TDOA while the proposed IDF-TDOA is unaffected by
the oscillator instability for long measurement times.
Figure 5. Standard deviation in micro seconds versus measurement time for TDOA and IDF-TDOA
on measured signals. The red line shows the measured accuracy inherent to the system, and the black
dotted lines the CRB. Note that the CRB is only displayed for 5 dB and 25 dB SNR
The experiment described above was repeated in a simulated environment
where the oscillator phase noise parameters estimated in Section 4.1 were used.
The results are displayed in Figure 6. By comparing the results presented in Figure 5
and 6 it becomes obvious that it is the phase noise that is causing the observed
errors, and no other tuner parameter such as linearity or noise.
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4.3. Results from a field experiment
A field experiment was conducted in order to test the IDF-TDOA under real
world conditions. A transmitter emitting a 10 kHz wide 8-PSK modulated pseudo
random sequence centered at 306 MHz was recorded for 15.9 s. The transmitter
output power was varied to achieve four different signal to noise ratios at the receivers. The distance between the receivers and the transmitter was around 5 km,
and the experiment was conducted in an urban environment. The result for TDOA
and IDF-TDOA is tabulated in Table 1 along with the CRB. The table shows that
IDF-TDOA improves the accuracy by a factor of 3 to 5.8 depending on the SNR.
The table also shows that the error is far above the CRB. The reason for this will
however require further scrutiny and is beyond the scope of this paper.
Table 1. TDOA standard deviation in micro seconds versus SNR
SNR
TDOA
IDF-TDOA
CRB
30
0.6
0.2
0.002
20
0.7
0.2
0.005
10
1.5
0.3
0.02
0
8.7
1.5
0.06
5. Conclusions
This paper investigates the effect of oscillators phase noise in tuners used for
TDOA based radio geolocation of unknown signals. The presented theoretical
study shows that the phase noise causes an effective decrease in the SNR if the rate
of frequency change caused by the phase noise exceeds the measurement time. These
results are used to propose a modification to the TDOA algorithm, and the final
algorithm is denoted IDF-TDOA.
Measurements on a tone using two tuners are used to estimate the spectral
contents of the phase noise. The measured spectrogram is used to calibrate an FFT
based phase noise model.
Measurements on noise using the two tuners are used to investigate the effect
of phase noise on TDOA estimation. The measurements are compared to the simulations and to the CRB. A comparison between the simulations and the measurements
shows that it is the phase noise causing performance degradations.
The paper is concluded with results from a field trial showing the effectiveness
of the proposed new IDF-TDOA algorithm under real world conditions. The new
algorithm outperforms traditional TDOA with respect to accuracy by a factor
of 3 to 5.8 depending on the SNR.
Chapter 8: Localization Techniques
385
Figure 6. Standard deviation in micro seconds versus measurement time for TDOA
and IDF-TDOA on simulated signals. The black dotted lines shows the CRB. Note that the CRB
is only displayed for 5 dB and 25 dB SNR
References
[1] D.H. a. D.D.E. Johnsson, Array Signal Processing: Concepts and Techniques, Upper
Saddle River, New Jersey 07458: Prentice-Hall Inc., 1993.
[2] W.R. a. T.S.A. Hahn, ”Optimum Processing for Delay-Vector Estimation in Passive
Signal Arrays,” IEEE Transactions on Information THeory, vol. 19, nr 5, pp. 608-614,
1973.
[3] A. a. C.P.B. a. R.J.C. Carlson, Communication Systems, An introductions to signal
and noise in electrical communication, Fourth edition, Mc Graw Hill, 2002.
[4] W.J. Riley, Handbook of frequency stability analysis, Boulder, COlorado U.S.
Department of Commerce: National Institute od Standards and Technology, 2008.
[5] C.A. Greenhall, ”FFT-Based Methods for Simulatin Flicker (FM),” 34th Annual
Precise Time and Time Interval (PTTI) Meeting, pp. 481-491, November 2002.
[6] C.K. a. G. Carter, ”The generalized correlation method for estimation of time
delay,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 24, nr 4,
pp. 320-327, 2003.
Aircraft Tracking Using Mobile Devices
Michał Andrzejewski1, Radosław Schoeneich2
1
Institute of Control and Computation Engineering, 2 Institute of Telecommunications,
Warsaw University of Technology,
[email protected], [email protected]
Abstract: The following article presents an overview of airship tracking system using mobile devices
equipped with GPS receiver, running under Android operating system. It describes general approach, usage of store, carry and forward paradigm in delay and disruption tolerant networking and
elements of architecture and implementation of presented applications. Shown results represent tests
run in real environment.
Keywords: tracking, monitoring, general aviation, aircraft, disruption tolerant network, DTN
I. Introduction
The dynamic development of mobile devices and navigation systems in recent
years has introduced many new everyday services. Among the most commonly
used are location and remote monitoring. Their potential was quickly spotted by
industries related to the transport and logistics, where knowledge about the current
position and status of objects such as vehicles and packages is often a key factor
in the success of the operation.
The main task of the monitoring system is to provide reliable information
about the position of tracked objects. This information can be interpreted and
used in different ways, depending on current needs. In general, however, it serves
two basic purposes: to increase safety and to improve efficiency. Location data
are widely used in planning and decision support systems, fleet management and
emergency assistance.
Unfortunately, despite the continuous development of systems of various
complexity, there is still no good solution that could be used to monitor general
aviation aircraft. For commercial and military aviation, there are several extensive
technical capabilities, such as primary or secondary surveilance radars or satellite
systems capable to track objects in real time with very high accuracy. However,
the cost of such solutions disqualifies them from usage in general aviation flying
clubs, flight schools and by private users.
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Key idea of this article is to consider popular mobile devices (such as smartphones, tablets, Personal Digital Assistants) as a reliable source of location data
and their usage in real application.
II. Environment
General aviation aircraft are moving in the sky at various altitudes and with
different speeds, depending on the type of an airship, airspace category or purpose
of the flight. However, assumption can be made that the vast majority of flights take
place at an altitude up to 1000 m above the ground level at speeds not exceeding
250 km/h. Flight paths at such altitude can be set freely, but near the airports some
constraints and obligations to accurately proceed between specified points exist.
General aviation flights may run across areas with different levels of urbanization, and at potentially large scale of height above ground level. This means that
the use of GSM mobile network to monitor the aircraft may be inefficient or even
impossible. This is due to cellular antennas settings. Additionally, the level of noise
and interference caused by signals from nearby stations raises with the altitude.
In practice, however, very often even at the height of up to 1500 meters mobile
network is available, and quality of connection allows data transmission. Making
a voice call is however not possible at this altitude. This makes an opportunity
of creating simple tracking system based on GSM-capable devices and data transmission provided by mobile telephony operators. Results obtained in this field are
presented later in this article.
The environment in which flights are made, is a challenge for active position
monitoring systems. Continuity and quality of data transmission becomes a major issue. Direct use of GSM network as a way to transmit information related to
the location may not give expected results in terms of reliability and availability.
This is an area where algorithms developed for use in incoherent networks may be
applied. Their essence is to enable operation in the harsh environment of unknown
characteristics.
III. Subject of research
The main task and also the source of the biggest problems of active monitoring systems is to develop a suitable method for data transmission. Such method,
consisting of software and technology, should provide high availability and reliability. In the perfect case, monitored object should always have the possibility
of an effective data transmission.
The research, which became the basis for this article was focused on:
• exploring the possibilities of using mobile devices and GSM networks to
keep track of general aviation aircraft,
Chapter 8: Localization Techniques
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• testing and evaluating prototype of aircraft tracking system in a real environment,
• identification of advantages and disadvantages of the proposed solution,
• proposing possible directions of development and improvement of the system.
During the developement of an aircraft monitoring system, the natural
choice was to provide transmission using wireless communications. The concept
of the satellite communication solutions was rejected because of their high cost.
Emphasis has been placed on technologies directly offered by mobile devices with
data transmission over GSM network in particular. Fig. 1 presents the basic scenario
where proposed system, in addition to direct communication between the aircraft
and data collection server, uses also the paradigm of data storage and forwarding.
This approach, commonly referred to as a store-carry-forward paradigm, is specific to delay and disruption tolerant networks. The aircraft marked as SP1 is out
of GSM coverage, so it can not send data on the position in a direct way. Using
a wireless network, it communicates and exchanges all necessary information
with the aircraft marked as SP2. The aircraft SP2 can then forward information
regarding both objects to the server collecting data. The system has information
about the positions of both vessels despite the fact that SP1 is not in a GSM range
to establish a direct connection.
Figure 1. Wireless data transmission scenario
Wireless communication channel between objects must meet the following
conditions for its use in the proposed system to be possible:
• time required to estabilish two-way communication channel and its effective range should allow data exchange between aircraft flying in opposite
directions at speeds of around 150 km/h,
• each pair of nodes using appropriate software should be able to connect
automatically in the same way,
• estabilishing the connection shouldn't require any additional operations
from the user.
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Lack of any of these features reduces the area of application of wireless communication between the aircraft and makes it difficult to achieve given functionality.
A. Architecture of aircraft tracking system
The system this research has been based on is divided into two parts, performing distinct functions and embedded in different environments, as shown
in the Fig. 2. It separates two basic areas of functionality – one related to the collection
and transmission of data carried by the mobile application, and the other responsible
for their analysis and visualization using an application server. It was a natural approach, given the purpose and the use of both items supplied in two applications.
Figure 2. Aircraft tracking system architecture
Mobile devices transmit files to an FTP server using the best currently available mean of communication. This can be a 2G/3G mobile network, Wi-Fi or another, depending on the particular device. If estabilishment of a direct connection
to a remote server hosting the data is impossible due to lack of network coverage,
high levels of noise, latency, or other actual problems, the application uses the storecarry-forward paradigm and gathers data locally. At a time when it is again possible
to transfer data to another node, the device acts as discussed before.
An attempt to transfer data to the server is controlled by an event timer –
the expiration of the specified period of time since the last attempt to send or other
parameters associated with the flight.
Before a file is put on the FTP server, its validity is checked, and the transmission is performed only when attempting to send a file containing more data
than the former. This allows nodes that are willing to send data less accurate
than those already stored on the server not to overwrite and lose them.
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In order to simplify whole process and increase the reliability of data exchange
between system elements, it was decided to use one common data model. This allows the system architecture as a whole to remain open, since all it requires is that
the source data have a specific format.
Usage of database management systems was rejected and flat text files in a specified comma separated values format were used instead. This reduces the requirements
for the server, simplifies the implementation and developement of the solution and
allows further analysis using common tools. Text files can still be imported and
stored in a database when necessary.
B. Technical limitations
Available methods for wireless connectivity between mobile devices are limited
to two common standards: IEEE 802.15.1 (Bluetooth) and Wi-Fi.
The first one has too short range in order to be used for communication between
aircraft in flight. For this purpose it is necessary to estabilish an effective connection
between the devices in a distance of at least 300 meters, while Bluetooth allows
efficient transmission at a distance several times smaller. In addition, the process
of finding neighboring nodes using this method can take tens of seconds. The use
of Bluetooth in incoherent networks is possible, but requires distances between
nodes to be small, as well as their relative speeds [1].
Use of Wi-Fi networking was a natural choice. Despite the low power of transmitters mounted in mobile devices, it was possible to connect two nodes within a distance of about 200 meters apart in the open area. One of the devices, the Samsung
Galaxy tablet, worked as an access point, while the second – LG-GT540 mobile
phone – as a client. This combination allowed a stable exchange of data at speeds
of about 100 kbps. Unfortunately, the long time needed to set up an access point and
then to connect the other device (in this particular case – a total of more than 45
seconds) and the need for manual adjustment of connection parameters prevented
this method from the application in the system.
In the course of the work it was discovered that the mobile device platform
capabilities are insufficient to meet all the required functionality. The main limitation was the lack of ability to automatically establish a wireless connection in adhoc mode between devices without user intervention. This feature is essential
for establishing a connection in an incoherent network and the exchange of data
between nodes. Application Programming Interface of Android operating system
used in experiments does not support such mechanism, nor is it possible to emulate.
There is also no documentation on this issue available.
Further studies show that the mechanism for supporting automatic wireless
connections between mobile devices is not available on any other of the popular
platforms.
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Therefore, it was impossible to verify and evaluate inconsistent network
performance in practice. The actual implementation required from the operating
system and the mobile device functionality, which it was not able to provide. Perhaps
in the future, with the development in this field of technology, this will become
possible. After examining the possibility to connect the devices during the flight
(discussed further in the next section) it was decided to abandon the functionality
associated with the use of incoherent network in the proposed system.
IV. Tests and results
An important element of the work was to conduct several series of tests, not
only to check the correctness of the implemented system, but also its usefulness
in proposed applications. Results were used to verify presented concepts and theoretical considerations in actual, real environment.
Tests in the target environment were divided into three stages. In the first,
a prototype of mobile application was used. It was able to collect environmental
data, such as the GSM signal strength, network mode, the height and position
of the airship, etc. In addition, the aircraft was equipped with a second instrument
– Garreht Volkslogger – which is a logger device certified by the International Air
Sports Federation used to record position in gliding competitions. At this stage,
the ability to perform data transmission using the GSM network at different heights
was verified. Another income was the examination of GPS receiver accuracy by
reference to the indications presented by the certified device. This part of the test
was conducted in May and June 2011.
The second step was to test the basic, working implementation of aircraft
monitoring system between July and October 2011. The system was used to monitor the position of student pilots and pilots during training in the area of WarsawBabice airfield.
The last stage completed in November 2011 was to test the operation of the system in the mountainous region. Differences in system’s behavior were identified and
investigated basing on analysis of data collected during the flights in the vicinity
of Bezmiechowa. Some additional opportunities for air-to-ground communications were diagnosed.
A. Collecting data about environment
Fig. 3 shows the altitude above mean sea level and GSM signal strength
during one of the flights. The value of the signal strength equal to -113 dBm
is identified as the inability to connect to the network. Data were sampled with
a period of 30 seconds.
Obtained results differed significantly from what was expected. It was anticipated
that the range of the GSM network will be unavailable for much of the flight because
Chapter 8: Localization Techniques
393
of the altitude and its characteristics. Data collected during the first test flights, however, revealed that GSM network was available up to a height of about 1400 meters
above terrain at urban areas throughout the duration of the flight. This is the result
of reflection and reinforcement of radio waves from the ground, buildings, etc.
Figure 3. Altitude and GSM signal strength correlation
Position samples collected by the mobile device does not differ significantly from those provided by a professional logger. The maximum absolute error
was 8 meters for static measurement and 25 meters for the measurement of motion
on average. Errors associated with determining the height were greater, however
they did not exceed 75 meters.
During one of the flights, an attempt to establish a wireless connection using
Wi-Fi network between two parallel flying aircraft was made. They were flying
at an altitude of 500 meters at speeds of 120 km/h. The distance between objects
was 150 meters. On board one of the aircrafts was a device which acted as an access
point, while the second tried to connect to that network. An access point was seen
by the other device, but the connection could not been established. Performed test
showed that in given conditions it is not possible to create and use Wi-Fi networking.
Results confirmed however, that the use of mobile devices for determining
the position is sufficiently accurate for use in most of the monitoring applications.
The quality of connections over the GSM network was sufficient for efficient transmission of data text files within a given height of 0-1000 meters.
B. Tests in lowland area
Most of the testing was made in the lowlands, because it is an environment
in which the highest number of general aviation flights are performed. The mobile
device was installed on board several aircrafts operating various types of flights
in the northern area of Mazovia region. Position of the aircraft was observed
in visualisation system and verified with reports collected via VHF radio. Between
July and November 2011, 28 such flights were made. Below analysis in Tab. I shows
12 flights selected of them.
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Military Communications and Information Technology...
Table I. Results of tests in lowland area
Flight #
Time in seconds [s]
Flight duration
MTBTa­­
ATBTb
1
3716
231
218
2
6642
212
201
3
823
180
164
4
9144
288
234
5
5323
222
190
6
6006
265
207
7
4571
303
253
8
5701
281
219
9
3122
201
183
10
6717
568
305
11
1559
180
173
12
1287
180
160
a) Maximum Time Between air-to-ground Transmissions
b) Average Time Between air-to-ground Transmissions
The most important parameter of such system is the location data refresh
rate, understood as time between air-to-ground communication. Information
about the successful upload to the server are recorded in the logs, so it is possible
to calculate the time between successive transmissions. During the test, the interval of 180 seconds was set up to make updates of the data as frequent as possible,
but not to excessively exhaust the battery. Tab. I shows a summary of flight time,
the value of the maximum interval between transmissions of data and the average.
Both of these times directly translate to the refresh rate of position data, therefore
it is desired to minimize both of them.
Each of the flights presented in a table has taken place in different weather
conditions, using a different route and at different heights. For this reason it is
impossible to directly compare obtained results. Obtained average values for each
flight lead to the conclusion that despite initial concerns related to the use of GSM
network for data transmission, the frequency of updating information about
the position of the aircraft is sufficient for the intended application.
C. Tests in mountainous area
The usefulness of the system to monitor flights taking place in a mountainous
environment, due to potential differences in performance, was also tested. The research was focused on the influence of the location of GSM network base stations
on the system effectiveness, compared to the results collected before. In the moun-
Chapter 8: Localization Techniques
395
tainous terrain antennas are placed on the tops of hills to embrace the largest area
lying below. It was expected that due to the smaller than in the lowlands height
difference between GSM transmitter and the aircraft, the average interval between
transmissions would be smaller. Because of the weather conditions, only 3 flights
were made, as shown below.
Table II. Results of tests in mountainous area
Flight #
Time in seconds [s]
Flight duration
MTBTc­­
ATBTd
1
1232
180
176
2
747
180
172
3
1338
191
186
c) Maximum Time Between Transmissions
d) Average Time Between Transmissions
During the first flight, it turned out that in close proximity to one of the mountain ranges it is possible to connect to public Wi-Fi network. Its access point
was located near one of the hotels and its range included a substantial portion
of the ridge. The results of the flights are shown in Tab. II taking both transmission
methods into account.
The achieved results confirmed that the air-to-ground communication using
the Wi-Fi standard during the flight is possible, though available only in specific
conditions. Shortening the average interval between transmissions is the result
of virtually uninterrupted stay of the aircraft in a GSM network and usage of availible
Wi-Fi for data transmission.
V. Conclusions
The results of the analyzed system, based on data collected during test flights,
were better than expected. It was possible to obtain better average refresh interval
of location data then initially planned. Value of 300 seconds was thought as satisfactory, while the average value for 12 flights in a lowland area was 209 seconds.
Increasing the frequency of data transmissions could further improve this value,
however, this would be associated with an increased resource consumption.
Area of testing had a significant impact on the results. Analyzed flights were
made up of several dozen kilometers from Warsaw agglomeration and mostly
ran above the urban areas. Execution of test flights over areas characterized by poor
coverage of GSM network is likely to lead to a deterioration of results.
Analysis of the system in mountainous terrain allowed the discovery of additional
options – usage of Wi-Fi networks for data transmission. Thanks to this observation,
capabilities of mobile applications were increased and results could be improved.
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Military Communications and Information Technology...
The biggest obstacle in the development of the system is lack of possibility to
create automatic connections between devices on an ad-hoc basis. This prevents
direct application of the store-carry-forward paradigm, which could significantly
improve performance.
References
[1] M. Liberatore, B.N. Levine, and C. Barakat, “Maximizing transfer opportunities
in bluetooth DTNs”, Proc. CoNEXT, 2006.
Index
A
Adrat Marc
Åkermark Hans
Amanowicz Marek
Andrzejewski Michał
Anton Constantin
Antweiler Markus
Aubrecht Vladimir
81, 171
135
55, 277, 333
387
107
81, 161, 171
67
B
Bloebaum Trude H.
Bronk Krzysztof
Bryś Rafał
9
215
289
C
Caban Przemysław
Chaudhary Muhammad Hafeez
9
247
D
Debbah Mérouane
Dołowski Jerzy
229
55
E
Ebinger Peter
Elders-Boll Harald
349
171
G
Gajewski Piotr
Głowacka Joanna
Goetz Michael
Golan Edward
Grzybkowski Maciej J.
151, 367
277
27
99
215
J
Johansson Anders M.
Johnsen Frank T.
377
9
K
Kaniewski Paweł
Kelner Jan M.
Koutny Tomas
Kraśniewski Adam
Krygier Jarosław
Kuijper Arjan
151
367
67
99
307
349
L
Leduc Jan
Le Martret Christophe J.
Le Nir Vincent
Liedtke Ferdinand
161, 171
229
187, 201
81
M
Mahoney Austin
Marks Michał
Maseng Torleiv
Matyszkiel Robert
Mazăre Alin
135
319
161
151
107
N
Nabrdalik Filip
Niewiadomska-Szynkiewicz Ewa
Niski Rafał
Nissen Ivor
319
319
215
27
O
Osten Tobias
171
289
H
Hedström Patrik
377
I
Idzikowska Ewa
Ionescu Laurenţiu
P
Pszczółkowski Jacek
117
107
R
Romanik Janusz
99
400
Military Communications and Information Technology...
Rose Luca
Ruszkowski Mirosław
229
289
S
Saarnisaari Harri
Scheers Bart
Schenkels Léon
Schoeneich Radosław
Şerban Gheorghe
Singh Sarvpreet
Skarżyński Paweł
Suchański Marek
265
135, 187, 201, 247
9
387
107
81
99
151
Ś
Śliwa Joanna
T
Teguig Djamel
Tschauner Matthias
Turčaník Michal
Tutănescu Ion
U
Urban Robert
9
187
81
45
107
99
V
Vanninen Teemu
265
W
Wawryszczuk Marcin
Wolthusen Stephen D.
333
349
Z
Ziółkowski Cezary
367
Ż
Żurek Jerzy
215