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

Automated summary

Sensor nodes deployed in hostile environments for military and commercial applications need fault diagnosis to inform other nodes of their status. However, diagnosing faults becomes difficult when nodes behave inconsistently. To address this, a one shot likelihood ratio test is proposed to determine the fault status of a sensor node by comparing statistics of received data with a threshold value. Simulation results show that this method provides better detection accuracy with lower false alarm rates compared to existing tests. The proposed method achieves a 100% detection accuracy, 0% false alarm rate, and 0% false positive rate for data from faulty nodes with a probability exceeding 25%.