Tutorial on Computational Intelligence for Sensor Networks by techmaster

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									                             Tutorial on
        Swarm Intelligence for Sensor Networks Applications
                                      (Half Day Tutorial)


Abstract:

A sensor network is a network of distributed autonomous devices that can sense or monitor
physical or environmental conditions cooperatively. Sensor networks are used in numerous
applications like environmental monitoring, habitat monitoring, prediction and detection of
natural calamities, medical monitoring and structural health monitoring. Advances in sensor
technology and computer networks have enabled sensor networks to evolve from small
clusters of large sensors to large networks of miniature sensors, from wired communications
to wireless communications, and from static network topology to dynamic topology. In spite of
these technological advances, sensor networks still face the challenges of communication
and processing of large amount of data in resource constrained environments. Algorithms of
swarm intelligence (SI) have been widely used as attractive tools to address the challenges in
sensor networks.

SI is a computational intelligence paradigm based on the collective behavior of decentralized,
self-organized, unsophisticated agents which interact locally with their environment and cause
coherent functional global patterns to emerge. The agents follow very simple rules, and
although there is no centralized control structure dictating how individual agents should
behave, local interactions between such agents lead to the emergence of complex global
behavior. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial
growth, and fish schooling. Successful applications of SI include function optimization, finding
optimal routes, scheduling, structural optimization, and image and data analysis.

This tutorial will introduce SI and its algorithms, and review recently reported SI applications
in sensor networks. Particle swarm optimization, ant colony optimization, and the hybrids of
these algorithms with other computational intelligence paradigms will be covered. Emphasis
will be given on how the algorithms are tailored to address issues in sensor networks, namely,
optimal sensor placement, self coordination, optimal routing, source localization, sensor
aggregation and fusion, and network life extension.


Speakers: Raghavendra V. Kulkarni, Senior Member, IEEE, and Ganesh Kumar
Venayagamoorthy, Senior Member, IEEE, Real-Time Power and Intelligent Systems
Laboratory, Department of Electrical and Computer Engineering, Missouri University of
Science and Technology, Rolla, MO 65409, USA.

Bios:

Raghavendra V. Kulkarni (M’97–SM’05) received the B.E. degree in electronics and
communication engineering from Karnatak University, Dharwad, India, in 1987 and the M.
Tech. degree in electronics engineering from the Institute of Technology, Banaras Hindu
University, Varanasi, India, in 1994. He was an Assistant Professor with Gogte Institute of
Technology, Belgaum, India, prior to August 2006. He is currently pursuing his PhD. degree in
Electrical Engineering in the Missouri University of Science and Technology, Rolla, USA.

His research interests are in the development of computational intelligence tools for real world
applications in sensor networks. He is a life member of Indian Society for Technical Education
(ISTE).
Ganesh Kumar Venayagamoorthy (S’91–M’97–SM’02) received the B.Eng. degree (Hons.)
in electrical and electronics engineering from Abubakar Tafawa Balewa University, Bauchi,
Nigeria, in 1994, and the M.Sc.Eng. and Ph.D. degrees in electrical engineering from the
University of KwaZulu Natal, Durban, South Africa, in 1999 and 2002, respectively. He was a
Senior Lecturer with the Durban University of Technology, Durban, South Africa prior to
joining the Missouri University of Science and Technology (Missouri S&T), Rolla, USA in
2002. Currently, he is an Associate Professor of Electrical and Computer Engineering and
Director of the Real-Time Power and Intelligent Systems Laboratory at Missouri S&T. He was
a Visiting Researcher with ABB Corporate Research, Vasteras, Sweden, in 2007. His
research interests are the development and applications of computational intelligence for real-
world applications, including power systems stability and control, alternative sources of
energy, FACTS devices, power electronics, sensor networks, collective robotic search, signal
processing and evolvable hardware. He has published 2 edited books, 5 book chapters, 55
refereed journals papers, and over 200 refereed international conference proceeding papers.
Dr. Venayagamoorthy was an Associate Editor of the IEEE TRANSACTIONS ON NEURAL
NETWORKS (from 2004 to 2007) and the IEEE TRANSACTIONS ON INSTRUMENTATION
AND MEASUREMENT (2007). He is currently the IEEE St. Louis Computational Intelligence
Society (CIS) and IAS Chapter Chairs. He has organized and chaired several panels, invited
and regular sessions, and tutorials at international conferences and workshops. He is General
Chair of 2008 IEEE Swarm Intelligence Symposium and Program Chair of the 2009 IEEE-
INNS International Joint Conference on Neural Networks. Dr. Venayagamoorthy was a
recipient of the 2007 US Office of Naval Research Young Investigator Program Award, the
2004 NSF CAREER Award, the 2006 IEEE Power Engineering Society Walter Fee
Outstanding Young Engineer Award, the 2006 IEEE St. Louis Section Outstanding Section
Member Award, the 2005 IEEE Industry Applications Society (IAS) Outstanding Young
Member Award, the 2005 SAIEE Young Achievers Award, the 2004 IEEE St. Louis Section
Outstanding Young Engineer Award, the 2003 INNS Young Investigator Award, the 2001
IEEE CIS Walter Karplus Summer Research Award, five prize papers from the IEEE IAS and
IEEE CIS, a 2007 MST Teaching Commendation Award, a 2006 MST School of Engineering
Teaching Excellence Award, and a 2007/2005 MST Faculty Excellence Award.

								
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