Optimization of decentralized random field estimation networks under communication constraints through Monte Carlo methods ?
We propose a new methodology for designing decentralized random field estimation strategies for sensor networks. We take the trade-off between the estimation accuracy and the cost of communications into account. A multi-objective Bayesian risk is used and feasible strategies are constrained by the communication link topology. Our solution is a message-passing algorithm based on a team decision theoretic approach and Monte Carlo methods. We demonstrate graceful degradation of the estimation accuracy as the communication becomes more costly.
random fields monte carlo methods decentralized estimation communication constrained inference message passing algorithms wireless sensor networks