Predicting and diagnosing self-intermittent faults in a dynamic distributed attack on wireless sensor network

Bhabani Sankar Gouda, Parimal Kumar Giri, Sudhakar Das, Trilochan Panigrahi and Bijay Kumar Paikaray

International Journal of Business Continuity and Risk Management, 2024, vol. 14, issue 2, 182-208

Abstract: In the distributed sensor network, it is challenging to secure communication while simultaneously being aware of the intermittent failure situation of a sensor node during the connection. The existing methods rely on KNN with statistical methods and iterative to identify error-free communication for the random behaviour of the sensor node. This research developed a KNN-based method for predicting whether a transmission would be faulted or fault-free and the statistics of sensor received data over a specific time interval, time period, and amount of time measures and compares the distance statistics of the sensor node at a predetermined, specific tolerance level. Moreover, in the simulation study, the entire network is based on the sending and receiving data status in a distributed WSN for real-time measurement with 100% data accuracy, a lower FPR, and a 0% FAR. All the experimental results found the statistical distance from a problematic cluster node exceeds 30%.

Keywords: distributed sensor network; fault diagnosis; statistical method; intermittent fault; KNN; wireless sensor networks; WSN; fuzzy set. (search for similar items in EconPapers)
Date: 2024
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