A unifying review of linear Gaussian models
Neural Computation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
International Journal of Sensor Networks
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
Hi-index | 0.00 |
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.