AHSWN Home · Issue Contents · Forthcoming Papers

Metaheuristic Optimization Based Energy Aware Clustering Scheme for Wireless Sensor Networks
G. Pushpa and S. Kannan

Wireless sensor networks (WSNs) usually contain several energy-constrained sensor nodes (SNs) to are required to work together for transmitting and collecting data. Clustering methods support organizing these nodes into clusters, with all the clusters having a leader (or cluster head (CH)) responsible for data aggregation and communication with the sink node or base station (BS). Clustering supports decreasing the energy counts utilized for data communication then only CHs transfer directly with BS, aggregating data from its cluster members (CMs). Energy-aware clustering systems are crucial for WSNs because it is a major impact on the network lifespan and performance. Clustering methods are a basic element of WSNs, assisting in optimizer energy consumption, increasing network scalability, and improving entire performances. The best clustering method and parameters rely on the certain requirements of the WSN application and the network features. With this motivation, this study presents a modified cheetah optimization algorithm-based energy-aware cluster approach (MCOA-EACA) for WSN. The purpose of the MCOA-EACA technique is to group the nodes in the WSN into clusters and elect a CH among them to accomplish network longevity. The MCOA-EACA scheme leverages the agility and efficiency of the MCOA, inspired by the hunting behaviours of cheetahs, to address the multi-layered challenges of WSNs. The MCOA-EACA technique also carefully designed an objective function using important parameters of energy, distance, and delay. The experimental values highlight the improved results of the MCOA-EACA technique compared to recent models. Furthermore, the MCOA-EACA technique demonstrates flexibility and efficiency over several aspects, thereby enhancing the network lifetime and boosting the overall performance.

Keywords: Wireless sensor networks, Network lifetime, Cheetah optimization algorithm, Energy efficiency, clustering

Full Text (IP)

DOI: 10.32908/ahswn.v58.10769