Decentralized Adaptive Filtering Algorithms for Sensor Activation in an Unattended Ground Sensor Network

We present decentralized adaptive filtering algorithms for sensor activation control in an unattended ground sensor network (UGSN) comprised of ZigBee-enabled nodes. Nodes monitor their environment in a low-power "sleep" mode, until an intruder is detected, then must decide whether to enter a full-power monitoring and transmission mode if their estimated average performance for activation outweighs their energy cost. The tradeoff is formulated in terms of the energy required to transmit data using the ZigBee protocol, probability of successful transmission, and the expected marginal increase in global utility resulting from a report, all of which depend on the activity of nearby sensor nodes. Since activation control is decentralized, and utilities are codependent, the adaptive filtering/stochastic approximation algorithms that we propose for sensor activation are based on game theoretic principles. We show that if each sensor operates according to this algorithm, the entire network is capable of actively tracking the correlated equilibrium set of the underlying game, which varies with target motion, node failures, or intentional parameter adjustments. We analyze the convergence and tracking properties of the adaptive filtering algorithms using differential inclusions.