Download UBIWARE: "Smart Semantic Middleware for Ubiquitous Users"

UBIWARE: "Smart Semantic Middleware for
Ubiquitous Computing" (2007-2009).
Tekes Project Proposal Executive Summary
© Industrial Ontologies Group, Agora Center, University of Jyvaskyla
e-mail: [email protected], URL: http://www.cs.jyu.fi/ai/OntoGroup/index.html
1. Introduction and Project Motivation
Recent advances in networking, sensor and RFID technologies allow connecting various
physical world objects to the IT infrastructure, which could, ultimately, enable realization
of the “Internet of Things” and the ubiquitous computing visions. Such interconnectivity
of computing and physical systems could, however, become the “nightmare of ubiquitous
computing” in which architects will be unable to anticipate, design and maintain the
complexity of interactions. It is widely acknowledged that as the networks, systems and
services of modern IT and communication infrastructures become increasingly complex,
traditional solutions to manage and control them seem to have reached their limits. In
recent years, self-* principles have raised much interest in integrated management,
networking, distributed systems and software engineering. This interest builds on the
success already encountered by self-organizing and self-adaptive systems in distributed
artificial intelligence, material science, thermodynamics, process industries, etc. Another
problem is heterogeneity of the ubiquitous components, appropriate standards, data
formats, protocols, etc, which makes impossible to provide interoperability among these
components within complex systems. Semantic Web is seen today as a key technology to
resolve the problems of interoperability, automation and integration within heterogeneous
world of interconnected ubiquitous objects and systems.
According to IBM’s vision, it’s time to design and build computing systems capable of
running themselves, adjusting to varying circumstances, and preparing their resources to
handle most efficiently the workloads we put upon them. These autonomic systems must
anticipate needs and allow users to concentrate on what they want to accomplish rather
than figuring how to rig the computing systems to get them there. Autonomous
Computing is the vision on how to enable computing systems to operate in a fully
autonomous manner. No administration, just simple high-level policy statements. This is
an approach to self-managed computing systems with a minimum of human interference.
Autonomy implies the ability to learn and integrate new knowledge, which have so far
proved enormously difficult to achieve. Such systems have the fundamental capacity to
grow and to adapt to the surrounding resource base, and contract when required. To reach
the higher objectives of Autonomous Computing the challenge is also to achieve largescale software systems capable of evolution and self-modification based on feedback and
learning. In particular autonomous maintenance requires the ability to sense/distinguish
self and to maintain functionality in the face of continuous environmental change or
intentional attacks. This implies the ability to specify initial requirements and expect the
system to know how to add further components and tools to achieve the specified goals.
It also implies the ability of a system to delete unnecessary resources and services. The
software agent community has begun experimenting with market based control of service
usage in distributed systems, which is one route to resource control in such complex
systems. Federated service management of autonomous systems may also be an essential
component of future networks.
The domain of ubiquitous computing traditionally includes such devices as embedded
processors, variety of sensors, personal devices and networks, cell phones and computers.
These devices have varied resources in terms of CPU power, memory, communication
capabilities, battery energy and software features. In such heterogeneous and uneven
conditions, application software demands adaptation and cooperation among distributed
hosts. Middleware is the most common solution that is widely used to facilitate
interoperability in the presence of dynamism and heterogeneity. The requirements for
such middleware is becoming even more challenging if to extend the traditional
ubiquitous computing domain by including to it also humans, software, services, etc.
Semantic Web technologies render dynamic, heterogeneous, distributed, shared content
accessible to both a human reader and software agents. Here the vision is to achieve a
synergy with Distributed AI (particularly multi-agent systems) where distributed agents
functioning autonomously provide needed middleware for ubiquitous computing and in
the same time utilize Semantic Web content to gather and aggregate knowledge, reason
and infer new results towards achieving their goals and generating new knowledge. Such
knowledge in turn may be disseminated and used to achieve the shared goal of the agents
system.
2. Project Goals
This project intends to bring the Semantic Web, Distributed AI and Human-Centric
Computing technologies to the industrial cluster of the ubiquitous computing domain. It
aims at designing a new generation middleware platform (UBIWARE) which will allow
creation of self-managed complex industrial systems consisting of mobile, distributed,
heterogeneous, shared and reusable components of different nature. Those components
can be smart machines and devices, sensors, actuators, RFIDs, web-services, software,
information systems, humans, models, processes, organizations, etc. Such middleware
will enable various components to automatically discover each other and to configure a
system with complex functionality based on the atomic functionalities of the components.
We believe that tasks of automatic integration, orchestration and composition of such
complex systems will be impossible with centralized control due to the scalability issue.
Therefore, the components should be to a certain degree autonomous, proactive, and
goal-driven. In other words, utilization of agent technologies is needed to enable flexible
communications, coordination and negotiations among the components. Interoperability
among the components requires use of metadata and ontologies. As the amount of
components can grow dramatically, without their ontological classification and (semi- or
fully-automated) semantic annotation processes the automatic discovery will be
impossible.
UBIWARE will require, among other, the reliable platforms to enable proactivity and
coordination among the components (workpackage 1, see below) and the solutions in
collecting, automatic annotating and intelligent processing of the data from distributed
history blogs and acquiring new knowledge to the system (workpackage 2). It will also
require essentially new solutions towards security, service provisioning and information
integration. Ubiquitous environment demands high but flexible level of security and
privacy in all kinds of interaction (e.g. accessing a control system of a power plant, or a
travel booking web service) based on well-defined and machine-readable policies
(workpackage 3). Dynamic context-aware service and resource composition, based on
combination of Service-Oriented Architecture and agent technologies, should bring
qualitatively new level of service provisioning to the end customers (workpackage 4).
Flexible semantic interfaces/adapters for generating and displaying composed or integral
result should be generated based on reusable patterns for information representation
bound to different domains (workpackage 5). Resource discovery in UBIWARE should
be smart, semantic and enable not only discovery based on centralized registries but also
based on mobile peer-to-peer networks (workpackage 6). Finally the middleware should
be designed, prototyped and tested on real industrial cases to prove the scientific concepts
behind it and facilitate its further utilization (workpackage 7).
In one sense, our intention to apply the concepts of automatic discovery, selection,
composition, orchestration, integration, invocation, execution monitoring, coordination,
communication, negotiation, context awareness, etc (which were, so far, mostly related
only to the Semantic Web-Services domain) to a more general “Semantic Web of
Things” domain. Also we want to expand this list by adding automatic self-management
including (self-*)organization, diagnostics, forecasting, control, configuration,
adaptation, tuning, maintenance, and learning.
In this project, we will naturally integrate the Ubiquitous Computing domain with such
domains as Semantic Web, Proactive Computing, Autonomous Computing, HumanCentric Computing, Distributed AI, Service-Oriented Architecture, Security and Privacy,
and Enterprise Application Integration. We will finish with a real prototype of the
UBIWARE for industrial needs as a key toolset for future "Global Enterprise Resource
Integration" (GERI) Platform. GERI should bring the following features to industrial
partners: Openness, Intelligence, Dynamics, Self-Organization, Usability, ContextAwareness, Semantics, Proactivity, Interoperability, Adaptation, Integration, Automation,
Security, and Privacy.
Utilization of Semantic Web technology should allow:
• Reusable configuration patterns for ubiquitous resource adapters;
• Reusable semantic history blogs for all ubiquitous components;
• Reusable semantic behavior patterns for agents and processes descriptions;
• Reusable coordination, design, integration and composition patterns;
• Reusable decision-making patterns;
•
•
Reusable interface patterns;
Reusable security and privacy policies.
Utilization of Distributed AI technology should allow:
• Proactivity and autonomic behavior
• Communication, coordination, negotiation, contracting
• Self-configuration and self-management
• Learning based-on liveblog histories;
• Distributed data mining and knowledge discovery;
• Dynamic integration;
• Automated diagnostics and prediction;
• Model exchange and sharing.
Utilization of Human-Centric approach enables us to consider human resources in four
possible roles:
• Human as UBIWARE user will get unique access to integrated and adapted
services and information
• Human as UBIWARE service provider will get support in online service
provisioning and benefit as a servicing component in various business processes;
• Human as UBIWARE resource will be able to get online care from integrated
distributed resources and services;
• Human as UBIWARE administrator will be able to launch and configure
UBIWARE for a particular task.
3. Project Workpackages and Expected Results
Research and development within this project will go to the following main directions
(workpackages):
1.
2.
3.
4.
5.
6.
7.
Core DAI platform design (UbiCore);
Managing Distributed Resource Histories (UbiBlog);
Smart Ubiquitous Resource Privacy and Security (SURPAS);
Self-Management, Configurability and Integration (COIN);
Smart Interfaces: Context-aware GUI for Integrated Data (4i technology);
Mobile Peer-to-Peer Middleware (MP2P);
Industrial cases and appropriate prototypes.
3.1. Core DAI Platform has to be designed to make each entity of the industrial Internet
of things ubiquitous and smart in a sense that it can proactively sense, monitor and
control own state, communicate with other entities, compose and utilize own and external
experiences and functionality for intelligent self-diagnostics and self-maintenance. The
platform should provide means for building systems that are flexible and consist of
autonomous components, yet predictable in operation. Two important research directions,
acknowledged in the literature, are: social level characterization of agent-based systems,
and ontological approaches to coordination. The former direction presents the need for a
better understanding of the impact of sociality and organizational context on an
individual’s behavior and of the symbiotic link between the behavior of the individual
agents and that of the overall system. In particular, it requires modeling behavior of an
agent as being defined by the roles the agent plays in one or several organizations. The
latter direction presents the need to enable agents to communicate their intentions with
respect to future activities and resource utilization and to reason about the actions, plans,
and knowledge of each other, in real time. The core DAI platform will provide a solution
advancing into both directions and integrating them.
3.2. Managing Distributed Resource Histories is going to be a set of tools, which will
support each distributed ubiquitous smart entity to collect and semantically markup own
history during the life-cycle, to query when needed own history or external distributed
histories of other entities, to integrate the histories, to make mining (utilizing intelligent
data mining and machine learning techniques) of the histories to discover knowledge, and
finally to manage acquired distributed knowledge. Recent advances in ubiquitous
computing and ICT together with the rapid proliferation of information sources and
services present unprecedented opportunities in integrative and collaborative analysis and
interpretation of distributed, autonomous (and hence, inevitably semantically
heterogeneous) data and knowledge sources and services in virtually every area of human
activity. Fundamental advances in collaborative approaches to knowledge acquisition and
data-driven decision making from distributed, autonomous, semantically heterogeneous
data and knowledge sources require synergistic synthesis of research advances, insights,
algorithms, and results in multiple areas of: Artificial Intelligence - especially machine
learning, data mining, knowledge representation and inference, intelligent agents and
multi-agent systems; Information Systems - especially databases, information integration,
Semantic Web; and Distributed Computing and Software Engineering (e.g., serviceoriented computing).
3.3. SURPAS is going to be a consolidated framework for the security and privacy
management in emerging new types of environments that are highly heterogeneous,
dynamic, open, distributed, autonomous, etc based mainly on advances of Semantic Web,
Agent Technologies and Ubiquitous Computing. The main components of SURPAS are
the conceptual semantics of policies (model-theoretical semantics and ontologies),
functionality of security mechanisms (functional semantics, algorithms, abstract
architecture, reference implementation), adopting applications using and for different
technologies (SOA, Agent Technologies, Web applications, etc) in different business
domains (industrial maintenance, subcontracting management, smart house, etc).
3.4. Self-management, Configurability and Integration will cover the aspects of
evolutionary and temporal changes on the platform as well as resource data integration
and resource composition. In the dynamic environment every resource may modify its
own characteristics either due to cyclic changes which are preprogrammed in the
resource’s behavior or, because of adequate reaction (self-awareness) on the surrounding
environment or internal changes, (e.g. web service performance optimization because of
sudden load in order to optimize the profit). Changes in a resource’s behavior may
influence business process chains, in which the resource is involved. Business process as
a resource composition here is somewhat similar to service composition in SOA world;
however, we need a well-defined resource configurability framework which will define
clear mechanisms of configuration including contracting, re-negotiation and recomposition taking into account agent-driven proactivity and dynamics.
3.5. Smart Interfaces will be developed to support dynamic context-aware A2R (Agentto-Resource) interaction. It extends the vision of our former General Adaptation
Framework by considering an interface (adapter) as a smart resource (i.e. proactive, agent
driven, self-managed). Such interfaces will be able not only to translate one data format
for another one (in our case from heterogeneous resource data format to agent
understandable RDF-like format) but also intelligently select relevant features of the
content to be sent from sender to receiver depending on current context. We will also
study opportunities of M-Language (MIT Data Center) specifically designed to be a
semantic mediator in ubiquitous communications. Additional requirement to smart
interfaces (which is smart visualization) appears when the resource in A2R abbreviation
is human, i.e. A2H. We are using our 4i technology (FOR EYE technology) to deal with
that requirement. 4i is an ensemble of Platform Intelligent GUI Shell and visualization
modules that provide context-dependent representation view of resource data and
integration on two levels. These are: information (data) integration of the resources to be
visualized; and integration of resource representation views with a handy resource
browsing in different dimensions. The technology provides a mechanism for performing
the interoperation and collaboration processes in handy and easy for human/expert way,
possibility to perform autonomous agent-based resource communication via visualization
module’s API. As an open environment for visualization module providers, it is a good
base for different business models that can be built on it.
3.6. Mobile Peer-to-Peer Middleware will be developed for extending the scale of
semantic resource discovery in UBIWARE towards also peer-to-peer discovery.
Spontaneously forming networks between mobile devices can distribute information and
these devices can work as a society of machines – a sort of enhanced social network. This
is a new application for mobile devices and understanding the functioning of such a
network is critical to producers of mobile devices, mobile operators and companies
making software on these kinds of environments. MP2P networks also have higher
requirements than what the current mobile devices can provide (short range radio
technology, processing power, storage space and security). This creates a need to develop
new functionalities to future mobile devices. Existing services can be enhanced with
peer-to-peer technologies. Consider a centralized video-on-demand server, which mobile
customers use as a fixed point to get videos to their mobile device. By adding P2P to the
system the customers can just define what kind of a video they want and the query, by
reaching not only one server, locates all matching videos on all servers providing larger
variety on the content the customers can select from. At the same time the quality of the
video streaming gets better, because some servers can provide the same video much
closer to the mobile device than the centralized one would. During this project period we
will develop several resource discovery algorithms for MP2P networks, study MP2P
networks using prototypes, model and simulate the information diffusion in MP2P
encounter networks. We will experimentally discover the critical amount of mobile
devices needed to participate in the diffusion for reliable work of the system, and how
fast the information can spread in such networks. This is important when designing
different MP2P encounter network services.
3.7. Industrial Cases and Appropriate Prototypes will be developed to prove the
research concepts and find fast ways of its industrial utilization. The prototypes of the
UBIWARE as integration of the above workpackages at different levels of their readiness
will be developed after each year of the project as UBIWARE 1.0, UBIWARE 2.0 and
UBIWARE 3.0. In addition to these several particular cases from partner companies will
be considered, designed and implemented based on the UBIWARE tools.
Important input data to the project will be results of SmartResource Tekes project
performed by our group during 2004-2006. See more information about SmartResource
in: http://www.cs.jyu.fi/ai/OntoGroup/projects.htm .
One of the most essential results of the SmartResource project was creation of the “Smart
Resource Technology” for designing complex software systems. The technology allows
considering each traditional system component as a “smart resource”, i.e. proactive,
agent-driven, self-managing. For example, a system interface can be a smart resource in
the same time, which has its own agent with semantically adapted sensors and actuators
to the environment, history blog, commitments with other resources, semantic selfdescription, self-monitoring, self-diagnostics and self-maintenance activities, and all
these guarantee high level of dynamism and flexibility to the interface. Such approach
has definitely certain advantages comparably to other software technologies, which are
integral parts of it, e.g. OOSE, SOA, Component-Based SE, Agent-Driven SE, Semantic
SE, etc. This approach is also applicable to various conceptual domain models. For
example, some domain ontology can be considered as a smart resource, which allows
having multiple ontologies in the designed system and enable their interoperability, onthe-fly mapping and maintenance due to communication among corresponding agents.
4. International Cooperation
We will also study emerging US initiatives of National Science Foundation, e.g. Global
Environment for Network Innovations (GENI), see http://www.geni.net/ and another one
is NSF Middleware Initiative (NMI), see http://www.nmi-edit.org/index.cfm . These are
targeting the next generation of the Internet (hardware, networking, tools, etc.). Recent
advanced applications of Google (e.g. Google Earth, Google Maps, Wikimapia, etc.),
Intel’s Proactive Computing initiative and IBM’s Autonomous Computing vision will be
incorporated to the UBIWARE. Project group agreed to cooperate with Massachusetts
Institute of Technology (Data Center) on integration of their M-Language and Global
RFID activities to UBIWARE project. Also University of Berkeley, California, agreed to
cooperate with project group on user modeling of activity integrating various sensing (e.g.
microphones, GPS, occupancy sensors, etc.) and building applications on top. Also we
will cooperate with Prof. Matthias Jarke, Information Systems Group, RWTH Aachen
University, Germany on security of mobile web service provisioning. We have also
agreed to cooperate with Prof. Hiroshi Suito (Solid Waste Management Research Center,
Okayama University, Japan) on Smart Interfaces for UBIWARE based on simulation,
visualization and optimization of the content. We will continue cooperation with Prof.
Gregory Levitin (Israel Electric Corporation and Technion) on risk management,
reliability analysis and optimization in ubiquitous environments. Our ongoing
cooperation with Kharkov National University of Radioelectronics (Ukraine) adds to the
project unique experiences in intelligent information processing, machine learning and
data mining. Also all other international contacts of Industrial Ontologies Group (VU
Amsterdam, DERI Ireland/Austria, ITIN France, etc.) will be used for better project
performance and quality. The project group will cooperate with research group of Prof.
Perrti Saariluoma (University of Jyvaskyla) on usability issues with emphasis on humanMAS interaction, with research group of Prof Kimmo Salmenjoki (University of Vaasa)
on process industry challenges for MAS and with other local international researchers
and research groups.
5. Tekes Funding and Industrial Consortium
Place for project research and development: Agora Center, University of Jyvaskyla.
Possible Tekes programs: UBICOM, FENIX or Open Call. Project duration: 3 years
(2007-2009).
The estimated annual funding: 500 000 (100 000 – companies + 400 000 - Tekes),
Estimated companies are:
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ABB
Fingrid
Metso Automation
Metso Paper
Metso Corporation
Nokia
TeliaSonera
6. Project Team
Project Leader: Prof. Vagan Terziyan ([email protected])
Research group:
Industrial Ontologies Group, Agora Center, University of Jyvaskyla
( http://www.cs.jyu.fi/ai/OntoGroup/index.html ).
The project group consists of professional researchers and software developers belonging
to the scientific school of Prof. Vagan Terziyan:
Vagan Terziyan, PhD., 1984 (“Modeling Semantics for Natural Language Processing”),
Dr. (Habil) Tech., 1993 (“Multilevel Knowledge Management Models and their
Industrial Applications”).
Olena Kaykova, PhD., 1989 (“Temporal and Spatial Aspects of Natural Language
Processing”).
Artem Katasonov, PhD., April 2006 ("Dependability Aspects in the Development and
Provision of Location-Based Services").
Nataliya Kohvakko, PhD. Thesis topic: “Context Modeling and Utilization in
Heterogeneous Networks” (ready end 2006).
Dmytro Zhovtobrukh, PhD. Thesis topic: “Context-Aware Web-Service Composition”
(ready end 2006).
Oleksiy Khriyenko, PhD. Thesis topic: “Adaptive Semantic Web-Based Environment for
Web Resources” (ready mid 2007).
Anton Naumenko, PhD. Thesis topic: “Management of Access-Control Policy of an
Alliance using Semantic Web” (ready mid 2007)
Jani Kurhinen, PhD. Thesis topic: “Semantic Routing in Ubiquitous Networks” (ready
end 2007).
Mikko Vapa, PhD. Thesis topic: “Resource Discovery in Peer-to-Peer Networks” (ready
end 2007).
Sergiy Nikitin, PhD. Thesis topic: “Semantically-Configurable Web Services in Semantic
Web Environments” (ready mid 2008)
Yaroslav Tsaruk, PhD. Thesis topic: “Semantic Web based Management for Multiagent
Systems” (ready end 2008).
Andriy Zharko, PhD. Thesis topic: “Active Resources in Semantic Web” (ready mid
2008).
Matthew Weber, PhD. Thesis topic: “Peer-to-Peer Distributed Artificial Intelligence”
(ready end 2008).
Annemari Auvinen, PhD. Thesis topic: “Topology Management Algorithms in Peer-toPeer Networks” (ready mid 2009).
MSc. Yevgeniy Ivanchenko.