Belief propagation distributed estimation in sensor networks: An optimized energy accuracy tradeoff

The estimation error performance of Gaussian belief propagation based distributed estimation in a large sensor network employing random sleep strategies is explicitly evaluated for a simple model using density evolution analysis. Both regular sleep strategies, in which the number of nodes awake at any time instant is fixed, as well as irregular sleep strategies, in which the number of awake nodes may vary, are analyzed. The calculated estimation error is used to study the tradeoff between estimation accuracy and energy consumption, as well as to dictate the optimal parameters for the random sleep strategy.