Stochastic Approximation Based Tracking of Correlated Equilibria for Game-Theoretic Reconfigurable Sensor Network Deployment

TitleStochastic Approximation Based Tracking of Correlated Equilibria for Game-Theoretic Reconfigurable Sensor Network Deployment
Publication TypeConference Paper
Year of Publication2006
AuthorsKrishnamurthy, V., G. Yin, and M. Maskery
Conference NameDecision and Control, 2006 45th IEEE Conference on
Pagination2051 -2056
Date Publisheddec.
Keywordsapproximation theory, difference equations, differential equation, game theory, global convergence, Lyapunov function, reconfigurable sensor network, regret matching, stochastic approximation, stochastic programming, wireless sensor networks
Abstract

Deployment of wireless sensors to efficiently forward information through a large array is considered from a game-theoretic perspective. Sensors with limited awareness learn to make local decisions (sleep/wake) in order to forward data in a slowly varying environment through a "regret matching" algorithm. With appropriately small smoothing and perturbation, we are able to apply results from stochastic approximation to establish global convergence of this algorithm and for an adaptive variant. The adaptive version allows sensor network connections to be reconfigured as game conditions change. We illustrate this reconfigurability with respect to channel fading, changing network demands, and sensor failure. Instead of the Blackwell approachability method used in previous papers, we give an ordinary differential equation formulation with a Lyapunov function to prove convergence to a correlated equilibrium. Numerical studies show that the algorithms can satisfactorily track correlated equilibria in systems of several thousand sensors, resulting in near competitive optimality at each sensor

URLhttp://dx.doi.org/10.1109/CDC.2006.376889
DOI10.1109/CDC.2006.376889

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