Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test

Distributed self diagnosis is an important problem in wireless sensor networks (WSNs) where each sensor node needs to learn its own fault status. The classical methods for fault finding using mean, median, majority voting and hypothetical test based approaches are not suitable for large scale WSNs due to large deviation in inaccurate data transmission by different faulty sensor nodes. In this paper, a modified three sigma edit test based self fault diagnosis algorithm is proposed which diagnose both hard and soft faulty sensor nodes. The proposed distribute self fault diagnosis (DSFD) algorithm is simulated in NS3 and the performances are compared with the existing distributed fault detection algorithms. The simulation results show that the detection accuracy, false alarm rate and false positive rate performance of the DSFD algorithm is much better in adverse environment where the traditional methods fails to detect the fault. The other parameters such as detection latency, energy consumption and the network life time are also determined.