Distributed Estimation in Wireless Sensor Networks Via Variational Message Passing

In this paper, a variational message passing framework is proposed for Markov random fields. Analogous to the traditional belief propagation algorithm, variational message passing is performed by only exchanging messages between adjacent nodes in a graph and updating local estimations, but with more energy and computation saving achieved. Explicit forms for distributions in the exponential family are derived and applied to a distributed estimation problem in wireless sensor networks. Furthermore, structured variational methods are explored to improve the estimation accuracy, whose performance is elaborated in a Gaussian Markov random field, by both theoretical analysis and simulation results.