Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks

Abstract

Wireless sensor network (WSN) is a developed wireless network consisting of some connected sensor nodes. The WSN is employed in many fields such as military, industrial, and environmental monitoring applications. These nodes are equipped with sensors for sensing the environmental variables such as temperature, humidity, wind speed, and so on. In most applications, WSN is positioned in remote places and harsh environments, where they are most probably exposed to faults. Hence, faulty sensor identification is one of the most fundamental tasks to be considered in WSN. This study suggests a hybrid methodology based on mutual information change (MIC) and wavelet transform (WT) for faulty sensor identification. The MIC method is suggested to study correlation among sensors, while the WT technique is proposed for self-sensor detection. WT is suitable for analysing non-stationary signals into approximation and detail coefficients. The suggested algorithm performance is investigated by applying a real case study at an arbitrary location close to Cairo, Egypt. The results of each method are compared using the true positive rate (TPR), false negative rate, and accuracy measures. Obtained results have shown that combining MIC and WT techniques can achieve a higher TPR and accuracy reach 100% in most fault types.