Underlying and Persistence Fault Diagnosis in Wireless Sensor Networks Using Majority Neighbors Co-ordination Approach

The faults in wireless sensor network are classified according to the underlying causes, behavior, and persistence with respect to the observation time. Due to underlying causes, faults are classified as fail and stop, crash, omission, timing, and incorrect computation fault. Due to behavior, faults are classified as hard and soft fault. Due to persistence, faults are classified as permanent, intermittent, and transient fault. As the recent state-of-art fault diagnosis is a significant requirement for each application of wireless sensor network. In this research paper, we have proposed a fault diagnosis protocol using majority neighbors coordination based approach for wireless sensor network. Precisely, a multiple-hop data received technique, timeout period mechanism, timeout request and response message exchange, timeout early and delay message exchange, and degree of belongingness using Gaussian function mechanism are used for the detection of faults such as fail and stop, crash, omission, timing, and incorrect computation. The mean difference and standard error comparison with different threshold condition are used for soft (permanent, intermittent, and transient) fault detection, and timeout response mechanism with different threshold condition is used for hard (permanent, intermittent, and transient) fault detection. After fault detection, the actual fault status of the sensor node is confirmed by the one-hop majority neighbor sensor nodes. For validation of the proposed fault detection algorithms, simulation experiments are conducted by the network simulator NS-2.35. The experimental results show the substantially parameters performance such as fault detection accuracy, false alarm rate, false positive rate, and false classification rate with increasing the fault probability for different average degree of the sensor nodes in the network.