Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks. Issue 1 (12th December 2021) - A., Balamurugan; Janakiraman, Sengathir; M., Deva Priya
Abstract: Wireless sensor networks (WSNs) are capable of offering data dissemination among the nodes such that the exploration of a network's potential could be performed based on the frequency range. It is highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issues of network lifetime, coverage, scheduling and data aggregation. Prolonging network lifetime confirms energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are highly suitable for effectively utilizing the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. This problem of energy‐aware optimization included in the clustering process is an NP optimization problem. At this juncture, metaheuristic optimization algorithms are potential candidates for optimizing energy that attributes towards predominant sustenance in network lifetime. In this article, a modified African buffalo and group teaching optimization algorithm (MABGTOA) is proposed for achieving energy stability and maintaining network lifetime by efficient cluster head (CH) selection during the process of clustering. This MABGTOA scheme is developed for sustaining the tradeoff existing between the rate of exploitation and exploration that aids in efficient selection of CHs, thus maintaining lifetime and energy stability in the network. The simulation experiments of the proposed MABGTOA confirm its predominance by offering increased throughput by 18.21% and sustenance in residual energy by 15.48% when compared to the benchmarked schemes taken for comparative investigation. Abstract : Flowchart of the proposed MABGTOA scheme: (a) MABGTOA‐based cluster selection scheme is proposed for achieving energy stability and improved network lifetime by mutually integrating their local and global capabilities. (b) The parliamentary decision process of ABOA is globally improved through teacher allocation phase of GTOA and the ability‐grouping phase of GTOA is improved locally through the utilization of guided reinitialization (c) This mutual integration of ABOA and GTOA aids in better selection of CHs with the objective of sustaining network lifetime and energy stability.