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WSN06-5: Distributed Bayesian Fault diagnosis in Collaborative Wireless Sensor Networks.

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2 Author(s)
Snoussi, H. ; ISTIT/M2S, Univ. of Technol. of Troyes, Troyes ; Richard, C.

In this contribution, we propose an efficient collaborative strategy for online change detection, in a distributed sensor network. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communication bandwidth. The observed systems are assumed to have each a finite set of states, including the abrupt change behavior. For each discrete state, an observed system is assumed to evolve according to a linear state-space model. An efficient Rao-Blackwellized collaborative particle filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellization procedure combines a sequential Monte Carlo filter with a bank of distributed Kalman filters. Only sufficient statistics are communicated between smart nodes. The spatio-temporal selection of the leader node and its collaborators is based on a trade-off between error propagation, communication constraints and information content complementarity of distributed data.

Published in:

Global Telecommunications Conference, 2006. GLOBECOM '06. IEEE

Date of Conference:

Nov. 27 2006-Dec. 1 2006