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

Author

Listed:
  • Bhabani Sankar Gouda
  • Parimal Kumar Giri
  • Sudhakar Das
  • Trilochan Panigrahi
  • Bijay Kumar Paikaray

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%.

Suggested Citation

  • Bhabani Sankar Gouda & Parimal Kumar Giri & Sudhakar Das & Trilochan Panigrahi & Bijay Kumar Paikaray, 2024. "Predicting and diagnosing self-intermittent faults in a dynamic distributed attack on wireless sensor network," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 14(2), pages 182-208.
    • Handle: RePEc:ids:ijbcrm:v:14:y:2024:i:2:p:182-208
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