A complete diagnosis of faulty sensor modules in a wireless sensor network

Abstract

Wireless sensor network (WSN) is a collection of sensor devices, which collects information from the environment and sends to the base station by single-hop or multi-hop communication towards achieving some predefined objectives. Due to the multi-functional applications in the adventurer environment, the sensor modules are damaged by various external and internal sources, which leads to a failure sensor network. Therefore, automated fault diagnosis and fault tolerance are of utmost importance in WSN. In this article, a complete fault diagnosis methodology is proposed for faulty sensors in the failed WSN. The proposed methodology consists of four phases of functioning such as initialization, fault detection, fault classification, and fault tolerance phase. The hard fault and link fault are detected by checksum and Fletcher’s checksum method. The soft faults (permanent, intermittent, and transient) are detected by Mann-Whitney U statistical test. The Gaussian transformation function with a classification factor is used for soft faults classification. The fault tolerance is attained in the sensor system by the stepwise regressional method. The proposed methodology is implemented in a real testbed experiment with simulation setup. The parameters like fault detection accuracy, false alarm rate, false positive rate, fault classification rate, false classification rate, R-squared value, standard error, fault probability, and degree of sensors are considered for the evaluation of the proposed diagnosis methodology.