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

Abstract : 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.
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Contributor : Jean-Baptiste Vu Van <>
Submitted on : Friday, November 15, 2019 - 5:17:20 PM
Last modification on : Tuesday, June 16, 2020 - 4:04:02 PM





Hichem Snoussi, Cédric Richard. WSN06-5: Distributed Bayesian Fault diagnosis in Collaborative Wireless Sensor Networks.. IEEE Globecom 2006, Nov 2006, San Francisco, United States. pp.1-6, ⟨10.1109/GLOCOM.2006.498⟩. ⟨hal-02366196⟩



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