Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor becoming faulty having adverse effects the network if they not diagnosed inform fault status to other nodes. Fault diagnosis is difficult when behave some times provide good data at times. The intermittent disturbances may be random or kind of spikes either regular irregular intervals. literature, algorithms based on statistical methods using repeated testing machine learning. To avoid more complex time consuming test processes computationally learning methods, we proposed a one shot likelihood ratio (LRT) here determine node. method measures statistics received over certain period then compares with threshold value associated tolerance limit. simulation results real set shows that new provides better detection accuracy (DA) minimum false positive rate (FPR) alarm (FAR) modified three sigma test. LRT hybrid detecting node wireless (WSN) measured 100% DA, 0% FAR FPR probability from exceeds 25%.