Sensor failure detection and recovery mechanism based on support vector and genetic algorithm

Abstract

The main role of wireless sensor networks is to collect environmental data. As the sensor nodes are vulnerable and work in unpredictable environments, sensors are possible to fail and return unexpected response. Therefore, fault detection and recovery are important in wireless sensor networks. In this paper, we propose a fault detection algorithm based on support vector regression, which predicts the measurements of sensor nodes by using historical data. Credit levels of sensor nodes will be determined by a contrast between predictions and actual measured values. In this paper we also propose a fault recovery algorithm according to the node credit levels combined with genetic algorithm. The simulation results demonstrate that the algorithms we propose work well in failure detection rate, fault recovery speed and energy consumption.