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

Authors: Bhabani Sankar Gouda; Parimal Kumar Giri; Sudhakar Das; Trilochan Panigrahi; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, India; NIST Institute of Science and Technology (Autonomous), Brahmapur, Odisha, India ' Department of Computer Science and Information Technology (CSIT), GITA Autonomous College, Bhubaneswar, Odisha, India; Affiliated to BPUT, Rourkela, India ' Department of Electronic and Communication, National Institute of Science and Technology (NIST), Brahmapur, Odisha, India ' Department of Electronic and Communication, NIT Goa, Ponda, Goa, India ' Center for Data Science, SOA University, Odisha, India

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.

DOI: 10.1504/IJBCRM.2024.139044

International Journal of Business Continuity and Risk Management, 2024 Vol.14 No.2, pp.182 - 208

Received: 04 Aug 2023
Accepted: 07 Oct 2023
Published online: 10 Jun 2024 *

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