DISTRIBUTED INTERMITTENT FAULT DIAGNOSIS IN WSN USING SPARSE NETWORK

Abstract

In WSN, fault occurrence is a general phenomenon and common procedure due to the network environment. To avoid the services of the sensor network due to faults and get an accurate result was used the distributed self-fault analysis approach. In order to prevent network degradation, faults must be found and fixed in large-scale wireless sensor networks. The distributed intermittent fault diagnosis in sparse networks (DIFDSN) that we present in this paper works effectively in a sparse network. It is primarily dependent on the comparison of cluster nodes within themselves and the distribution of test results to the remaining sensors. Additionally, using the K-mean algorithm, the interval is used to diagnose the intermittent failure in the cluster network's sensing and communication. The suggested approach has a low false alarm rate and good accuracy in detection, according to simulation findings.