Robust message-passing for statistical inference in sensor networks

Large-scale sensor network applications require in-network processing and data fusion to compute statistically relevant summaries of the sensed measurements. This paper studies distributed message-passing algorithms, in which neighboring nodes in the network pass local information relevant to a global computation, for performing statistical inference. We focus on the class of reweighted belief propagation (RBP) algorithms, which includes as special cases the standard sum-product and max-product algorithms for general networks with cycles, but in contrast to standard algorithms has attractive theoretical properties (uniqueness of fixed points, convergence, and robustness). Our main contribution is to design and implement a practical and modular architecture for implementing RBP algorithms in real networks. In addition, we show how intelligent scheduling of RBP messages can be used to minimize communication between motes and prolong the lifetime of the network. Our simulation and Mica2...

• Type: Conference
• Year: 2007
• Where: IPSN
• Authors: Jeremy Schiff, Dominic Antonelli, Alexandros G. Dimakis, David Chu, Martin J. Wainwright
• Keywords: Algorithms, IPSN 2007, Message-passing Algorithms, RBP Algorithms, Sensor Networks