Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks. (3rd April 2007)

• Record Type: Journal Article
• Authors: Snoussi, Hichem; Richard, Cedric
• Is Part Of: International journal of sensor networks. Volume 2:Number 1/2(2007)
• Page Start: 118
• Page End: 127
• Publication Date: 2007-04-03
• Subjects: collaborative sensor networks -- online change detection -- Rao-Blackwellised particle filter -- RB-CPF -- collaboration -- Bayesian fault diagnosis -- jump Markov systems -- wireless networks -- network lifetime; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

Abstract

A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the system dynamics by a jump Markov system with a finite set of states, including the abrupt change behaviour. For each discrete state, an observed system is assumed to evolve according to a state-space model. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communications bandwith. An efficient Rao-Blackwellised Collaborative Particle Filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellisation procedure combines a Sequential Monte-Carlo (SMC) filter with a bank of distributed Kalman filters. In order to prolong the sensor network lifetime, only few active (leader) nodes are selected according to a spatio-temporal selection protocol. This protocol is based on a trade-off between error propagation, communications constraints and information content complementarity of distributed data. Only sufficient statistics are communicated between leader nodes and their collaborators.