A key issue in the wireless sensor network applications is how to accurately detect the fault status of a node when it is working in a harsh environment. The wrong detection of nodes status can cause a lot of the damage especially when it is used for critical applications. Using distributed self-fault diagnosis (DSFD) method, faults in wireless sensor networks (WSNs) can be easily detected. In this method, each sensor node collects its neighbourhood sensor node data and uses the statistical-based method for detecting its own fault status. In this paper, we discussed various statistical-based method such as standard deviation, interquartile range, median absolute deviation (MAD), Sn and Qn scale estimator for detection of the fault in WSNs. The result of the experiment shows that standard deviation and interquartile range fails to detect the fault, if multiple nodes are faulty, while MAD, Sn and Qn scale estimator detects the fault even 20-30% of the nodes are faulty.
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Deepak Yadav – @imdeepak_dky – dky.united@gmail.com
Distributed under the MIT license. See LICENSE for more information.
https://github.com/deepak7376/wsnFault/blob/master/LICENSE
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