Skip to Main Content
Decentralized activation in wireless sensor networks is investigated for energy-efficient monitoring using the theory of global games. Given a large number of sensors which can operate in either an energy-efficient ldquolow-resolutionrdquo monitoring mode, or a more costly ldquohigh-resolutionrdquo mode, the problem of computing and executing a strategy for mode selection is formulated as a global game with diverse utilities and noise conditions. Each sensor measures its environmental conditions in noise, and determines whether to enter a ldquohigh-resolutionrdquo mode based on its expected contribution and energy cost, which relies on Bayesian estimates of others' observations and actions. We formulate Bayes-Nash equilibrium conditions for which a simple threshold strategy is competitively optimal for each sensor, and propose a scheme for decentralized threshold computation. The threshold level and its equilibrium properties depend on the prior probability distribution of environmental conditions and the observation noise, and we give conditions for equilibrium of the threshold strategy as a function of the prior and observation noise. We also illustrate an interesting phase transition property of the Nash equilibrium for high congestion or low noise variance.