Authors: Hichem Snoussi, Cedric Richard
Addresses: ICD/LM2S, University of Technology of Troyes, 12, rue Marie Curie, 10000, France. ' ICD/LM2S, University of Technology of Troyes, 12, rue Marie Curie, 10000, France
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.
Keywords: collaborative sensor networks; online change detection; Rao-Blackwellised particle filter; RB-CPF; collaboration; Bayesian fault diagnosis; jump Markov systems; wireless networks; network lifetime.
International Journal of Sensor Networks, 2007 Vol.2 No.1/2, pp.118 - 127
Published online: 02 Apr 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article