Fault tolerant spatio-temporal fusion for moving vehicle classification in wireless sensor networks

Abstract

Wireless sensor networks (WSNs) can implement complicated tasks through collaboration among multiple sensor nodes. The low-cost sensors in WSNs often generate noisy and even faulty measurements, which will degrade the network performance. Therefore developing collaborative signal processing (CSP) algorithms that has high fault tolerance ability is necessary for the increasingly deployed WSNs. In this study, the authors propose a novel fault tolerant fusion scheme to implement reliable vehicle classification by integrating fault detection and correction with a spatio-temporal fusion structure. Sensor faults are detected at the fusion centre and then the fault detection results are fed back to the local sensors to update subsequent classification results. A Dempster-Shafer theory based fault correction strategy is devised to utilise the fusion centre feedback. Simulation results demonstrate that the proposed scheme ensures more than 95- of the classification results to be correct when no larger than 30- of the sensors are faulty and the scheme achieves improved fault detection rate and false alarm rate than the optimum threshold Bayesian fault detection scheme.