A Method for Fault Detection in Wireless Sensor Network Based on Pearson’s Correlation Coefficient and Support Vector Machine Classification

Sensor nodes are tiny low-cost devices prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson’s correlation coefficient and the Support Vector Machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson’s correlation coefficient is used to measure correlation. Then SVM was used to classify faulty readings from normal readings. After classification, faulty readings are discarded. Here each sensor nodes periodically collects environmental features and sends them to their associated cluster heads. Each cluster head analyze collected data using the classification algorithm to detect whether any fault is present or not. Network simulator NS-2.35 and Matlab are used for evaluation of our proposed method. The fault detection algorithm was evaluated using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, \(F_1\) Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC). Performance evaluation shows, the proposed method performs well for high fault percentages.