A robust architecture for distributed inference in sensor networks

  • Authors:

  • Mark Paskin;Carlos Guestrin;Jim McFadden

  • Affiliations:

  • Stanford University, Stanford, California;Carnegie Mellon University, Pittsburgh, Pennsylvania;Carnegie Mellon University, Pittsburgh, Pennsylvania

  • Venue:
  • IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
  • Year:
  • 2005

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Abstract

Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class of these problems---including probabilistic inference, regression, and control problems---can be solved by message passing on a data structure called a junction tree. In this paper, we present a distributed architecture for solving these problems that is robust to unreliable communication and node failures. In this architecture, the nodes of the sensor network assemble themselves into a junction tree and exchange messages between neighbors to solve the inference problem efficiently and exactly. A key part of the architecture is an efficient distributed algorithm for optimizing the choice of junction tree to minimize the communication and computation required by inference. We present experimental results from a prototype implementation on a 97-node Mica2 mote network, as well as simulation results for three applications: distributed sensor calibration, optimal control, and sensor field modeling. These experiments demonstrate that our distributed architecture can solve many important inference problems exactly, efficiently, and robustly.