Decentralized Decision-making and Social Learning in Sensor Networks
Jan 09, 2011
from 01:00 PM to 02:00 PM
|Where||Shannon Room 54-134 Engineering IV Building|
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University of British Columbia
This talk deals with decentralized information processing and Bayesian social learning in sensor networks. The aim is to show how simple local behavior can result in sophisticated global behavior thereby facilitating decentralized awareness in a sensor network. The talk comprises of three parts. The first part presents recent work in modelling the dynamics of molecular biosensor built out of protein molecules imbedded in a synthetic cell membrane. The second part of the talk illustrates how the theory of global games gives a useful method for analyzing sensor activation algorithms in dense sensor networks. It is shown that the Bayesian Nash equilibrium has a simple threshold structure. The final part of the talk deals with social learning protocols and how quickest time change detection can be achieved. We examine how such local decisions affects global decision making. The unifying theme of the talk is how simple local behavior can result in sophisticated global behavior, and how local and global decision makers interact.
Vikram Krishnamurthy is currently a professor and holds the Canada Research Chair in Statistical Signal Processing at the Department of Electrical Engineering, University of British Columbia, Vancouver, Canada. Prior to 2002, he was a chaired professor at the Department of Electrical and Electronic Engineering, University of Melbourne, Australia. His current research interests include statistical signal processing, computational game theory, dynamical systems for modeling of biological ion channels and stochastic optimization and scheduling.
In 2009 and 2010 he served as Distinguished lecturer for the IEEE signal processing society. From 2010 he serves as Editor in Chief of IEEE Journal Selected Topics in Signal Processing.
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