Detection and diagnosis of node failure in wireless sensor networks: A multiobjective optimization approach

Detection of intermittent faults in sensor nodes is an important issue in sensor networks. This requires repeated application of test since an intermittent fault will not occur consistently. Optimization of inter test interval and maximum number of tests required is crucial. In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem. The two objectives, i.e., detection latency and energy overhead are taken into consideration. Tuning of detection parameters based on two-lbests based multiobjective particle swarm optimization (2LB-MOPSO) algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A comparative study of the performance of the three algorithms is carried out, which show that the 2LB-MOPSO is a better candidate for solving the multiobjective problem of intermittent fault detection. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.