Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

Abstract

In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.