Download implementation of trust modeling scheme for artificial

IMPLEMENTATION OF TRUST MODELING SCHEME FOR ARTIFICIAL
INTELLIGENCE IN COGNITIVE RADIO NETWORKS
G.PONMANI[1]
S.RAMANI[2]
ABSTRACT
INTRODUCTION
A basic problem facing the future in
wireless systems is where to find suitable
spectrum bands to fulfill the demand of future
services. To overcome this problem and improve
the spectrum utilization cognitive radio concept
has been evolved. Wireless communication, in
which a transmitter and receiver can detect
intelligently communication channels that are in
use and those which are not in use are known as
Cognitive Radio and it can move to unused
channels. In this Project using recent advances in
uncertain reasoning originated from artificial
intelligence community, to propose a unified trust
management scheme that enhances the security in
Cognitive Radio. The trust model has two
components such as Direct and Indirect
Observation. Combining these two components in
the trust model, we can obtain more accurate trust
values of the observed nodes in Cognitive radio.
Cognitive radio scenario positively supports the
effectiveness and performance, which improves
throughput and packet delivery ratio considerably,
with slightly increased average end-to-end delay
and overhead of messages. Based on the proposed
scheme, more accurate trust can be obtained by
considering different types of packet such as data
packets and control packets.
G.Ponmani.,M.E(Communication Systems)
Varuvan Vadivelan Institute of Technology
Email id: [email protected]
Cognitive radio offers the promise of
intelligent radios that can learn from and adapt to
their environment. Much research is currently
underway developing various reasoning and
learning algorithms that allow cognitive radios to
operate optimally in a large variety of different
situations. However, as with many new
technologies, initial research has not focused on
security aspects of cognitive radio.
Typically security is always “bolted on” after
the fact by adding some sort of link authentication
and encryption. This typically works well for data
traversing a wireless network, but not necessarily
for things fundamental to the operation of the
wireless link itself. Since cognitive radios can
adapt to their environment and change how they
communicate, it’s crucial that they select optimal,
secure means of communications. Data integrity
and confidentiality can be handled by higher-layer
cryptographic security, so here to focus on attacks
fundamental to the cognitive radio itself, and
independent of its higher-layer communications
techniques. By putting artificial intelligence (AI)
engines in charge of our wireless devices, need to
be aware that these engines can be provided false
sensory input by adversaries, and this false input
affects its beliefs and behaviour.
WIRELESS AD HOC NETWORKS
Wireless Ad Hoc Network is a collection of
two or more devices or nodes or terminals with
wireless Communications and networking
capability that communicate with each other
without the aid of any centralized administrator
also the wireless nodes that can dynamically form
a network to exchange information without using
any existing fixed network infrastructure. And it’s
an autonomous system in which mobile hosts
connected by wireless links are free to be
dynamically and some time act as routers at the
same time. All nodes in a wireless ad hoc network
act as a router and host as well as the network
topology is in dynamically, because the
connectivity between the nodes may vary with
time due to some of the node departures and new
node arrivals.
Nodes mover randomly in different direction and
different speeds. In the past few years, the people
became realized to use all the technology so
widely and the people’s future living
environments are emerging, based on information
resource provided by the connections of different
communication networks for clients also we have
seen a rapid expansion in the field of Mobile
Computing because the proliferation not
expensive, widely available wireless devices . A
new small devices such as personal
communication like cell phones, laptops, Personal
Digital Assistants (PDAs),handhelds, and also
there’s a lot of traditional home appliances such
as a digital cameras, cooking ovens, washing
machines, refrigerators and thermostats, with
computing and communicating powers attached.
Mobile Ad Hoc Protocols
For the Ad Hoc network there are more
than 13 kinds of the above routing protocol have
been proposed, following the more representative
for several separate presentation, and to compare
between them, and for more dilates about existing
ad hoc network protocols.
Destination-Sequence Distance-Vector Routing
(DSDV)
Destination-Sequenced Distance-Vector
Routing is based on traditional Bellman-Ford
routing algorithms were developed by the
improvement, and a routing table-based protocol.
Each node in an operation must be stored a
routing table, which records all the possible links
with the nodes in the node and the distance like
the number of hops, routing table within each
record also contains a sequence number, which is
used to determine are there any more old path in
order to avoid routing table generation. DSDV is
basically on the Internet Distance-Vector Routing
the same, but more destination sequence number
of the record, makes the Distance-Vector Routing
more in line with this dynamic network MANET
needs, In addition, when network topology
changes are less frequent when the routing table
does not need to exchange all the information,
DSDV, within each node, together with a table, is
used to record the routing table changes from the
last part of the exchange so far, if you change a lot
of the conduct of all the information The
exchange, known as the full dump packets, if the
change very little, it is only for the part of the
exchange, known as the incremental packet. In
this protocol, each node in an operation must be
stored a routing table, which records all the
possible links with the nodes.
RELATED WORK
EXISTING SYSTEM
The design of network protocols for
these networks is a complex issue. Regardless of
the application, MANETs need efficient
distributed algorithms to determine network
organization, link scheduling, and routing.
However, determining viable routing paths and
delivering messages in a decentralized
environment where network topology fluctuates is
not a well-defined problem. While the shortest
path (based on a given cost function) from a
source to a destination in a static network is
usually the optimal route, this idea is not easily
extended to MANETs. Factors such as variable
wireless link quality, propagation path loss,
fading, multiuser interference, power expended,
and topological changes, become relevant issues.
The network should be able to adaptively alter the
routing paths to alleviate any of these effects.
Moreover, in a military environment, preservation
of security, latency, reliability, intentional
jamming, and recovery from failure are significant
concerns. Military networks are designed to
maintain a low probability of intercept and/or a
low probability of detection. Hence, nodes prefer
to radiate as little power as necessary and transmit
as infrequently as possible, thus decreasing the
probability of detection or interception. A lapse in
any of these requirements may degrade the
performance and dependability of the network.
The results demonstrate that the proposed scheme
has a lower routing load because of the higher
number of packets received correctly by the
destination node. As the number of nodes
increases, the routing load of the existing and
proposed schemes climb up due to the nature of
proactive routing protocol: periodical generation
of control messages in every node.
PROPOSED SYSTEM
A CR "monitors its own performance
continuously", in addition to "reading the radio's
outputs"; it then uses this information to
"determine the RF environment, channel
conditions, link performance, etc.", and adjusts
the "radio's settings to deliver the required
quality of service subject to an appropriate
combination of user requirements, operational
limitations, and regulatory constraints".
Some
"smart
radio"
proposals
combine wireless mesh network dynamically
changing the path messages take between two
given nodes using cooperative diversity;
cognitive radio dynamically changing the
frequency band used by messages between two
consecutive nodes on the path; and softwaredefined radio dynamically changing the protocol
used by message between two consecutive nodes.
In this proposed system an uncertain
reasoning theory from artificial intelligence to
evaluate the trust of nodes in cognitive
networks. Unified trust management using
Bayesian Interference and Dempster-Shafer
theory combining these two components in the
trust model, we can obtain more accurate trust
values of the observed nodes in cognitive
networks and then evaluate our scheme under
the scenario of cognitive networks. Extensive
simulation results show the effectiveness of
the proposed scheme. These components are
similar to those used in direction observation
trust, an observer estimates the trust of his
one-hop neighbor based on its own opinion.
Cognitive Radio (CR), which provides the
capability to harness the potential of
unused/underutilized spectrum (spectrum
holes) in an opportunistic manner, is a key
enabling technology for dynamic spectrum
access. An illustration of the cognitive radio
technology is presented which it is easy to
observe that CR can significantly improve the
overall spectrum utilization when the CR
users are allowed to utilize the spectrum holes.
A cognitive radio network typically involves
two types of users: primary users (PUs), who
are incumbent licensed users of the spectrum,
and CR users (also known as secondary
users), who try to opportunistically access the
unused licensed spectrum as long as the
harmful interference to primary users is
limited.
According to Merriam Webster’s
Dictionary, trust is defined as ”assured
reliance on the character, ability, strength, or
truth of someone or something.” Despite the
subjective nature of trust, the concept of trust
has been very attractive to network security
protocol designers because of its diverse
applicability as a decision making mechanism.
5.2.1 Trust in sociology
Gambetta describes the nature of trust
as subjectivity, an indicator for future actions,
and dynamicity based on continuous
interactions between two entities. Luhmann
also emphasized the importance of trust in
society as a mechanism for building
cooperation among people to extend human
interactions for future collaboration.
Cognitive Radio Functions
In an introduction of reconfigurable
logic and the coining of the term software
defined radio (SDR), the dominant
implementation architecture used for
RF Front-Ends (FEs) was the superheterodyne architecture. The following
graphic shows how a cognitive radio Network
operates in relation to its environment:
TRUST MODELING
5.2.2 Trust in economics
In economics, trust is
represented as an expectation that applies to
situations in which trustors take risky actions
under uncertainty or information
incompleteness.
FRAMEWORK
CONCLUSION AND FUTURE
WORK
This paper uncertain reasoning,
Bayesian inference and Dempster-Shafer
theory, the trust values of observed nodes in
MANETs is derived.. The results of MANET
routing scenario which improves throughput
and packet delivery ratio considerably, with
slightly increased average end-to-end delay
and overhead of messages. Although some
excellent work has been done on detectionbased approaches based on trust in MANETs,
most of existing approaches do not exploit
direct and indirect observation at the same
time to evaluate the trust of an observed node.
Moreover, indirect observation in most
approaches is only used to assess the
reliability of nodes, which are not in the range
of the observer node. Therefore, inaccurate
trust values may be derived and also most
methods of trust evaluation from direct
observation do not differentiate data packets
and control packets in MANETs.
The cognitive radio networks are
developed to solve the current existing
problems in the wireless communications. In
this also to derive the trust value of nodes by
using a unified trust management scheme to
improve the security in Cognitive Radio.
Using recent advances in uncertain reasoning,
Bayesian inference and Dempster-Shafer
theory, to evaluate the trust values of observed
nodes in Cognitive radio. The results of
Cognitive radio positively support the
effectiveness and performance of our scheme,
which improves throughput and packet
delivery ratio. Abundant simulation results
have been conducted and validated that the
proposed scheme outperforms the existing
ones under the impact of different attack
patterns and different number of malicious
nodes.
REFERENCES
[1]
Akbar I.A and TranteW.H, (2013) “Dynamic
spectrum allocation in cognitive radio using
hidden Markov models: Poisson distributed
case,” in Proceedings of IEEE Southeastcon,
2007, pp. 196–201.
[2]
Bellman R.E (2011), “Can Artificial
Intelligence meet the Cognitive Networking
Challenge?” (San Francisco, CA: Boyd &
Fraser Publishing Company).
[3]
Chapin J and Chan V.W(2011), “The next 10
years of DoD wireless networking research,”
in Proc. IEEE Milcom’11, (Baltimore, MD,
USA).
[4]
Charles Clancy T, Nathan Goergen (2012),
“Security In Cognitive Radio Networks:
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Threats and Mitigation,”
Chen. Z, Guo. N, Z. Hu, and Qiu R (2012),
“Channel state prediction in cognitive radio,
part ii: Single-user prediction,” in Proceedings
of IEEE Southeastcon, pp. 50 –54.
[6]
Domenico A.D(2010), “A survey on MAC
strategies for cognitive radio networks,” IEEE
Communication Surveys & Tutorials, June
2010.
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Guan Q, Yu F.R, Jiang S, and Leung V(2012),
“Joint topology control and authentication
design in mobile ad hoc networks with
cooperative communications,” IEEE Trans.
Veh. Tech., vol. 61, pp. 2674 –2685.
G.Ponmani .,II year M.E (Communication Systems)
Varuvan Vadivelan Institute of Technology
Dharmapuri-636703