In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.
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E. Jyothi Kiranmayi
E. Jyothi Kiranmayi
Department of Computer Science, SVD Government Degree College (W), Nidadavole, Andhra Pradesh, India.
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Cite this article
E. Jyothi Kiranmayi, N.V. Rao and K.S. Nayanathara, “Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks, Journal of Machine and Computing, vol.3, no.3, pp. 251-263, July 2023. doi: 10.53759/7669/jmc202303023.
