A comparative study on energy efficient clustering based on metaheuristic algorithms for WSN

One of the important technologies in wireless sensor networks (WSN) is an efficient and dependable routing system. In WSN, energy is the key resource for extending network lifespan. Nowadays, WSN is utilized for a variety of applications, and there is always an issue with energy consumption. As a result, to find out the best energy efficiency model is the primary focus of this research in order to extend the life of the network. Different energy-efficient clustering (EEC) methods based on artificial bee colony optimization (ABC) are applied for the WSN in this comparative research to increase the network's energy efficiency. To increase energy efficiency during the communication process, the enhanced memetic artificial bee colony (EMABC), global artificial bee colony algorithm based on the cross over and tabu search (CGTABC), Energy-efficient clustering using artificial bee colony (EC-ABC), memetic artificial bee colony algorithm (MeABC), and randomized memetic artificial bee colony algorithm (RMABC) are implemented. The EEC method involves forming the appropriate quantity of clusters and selecting cluster heads in a dynamic manner. Furthermore, relay nodes are chosen with energy efficiency objectives, and they are computed afterwards. The implemented methods perform EEC. In terms of assessment and validation, the implemented models are compared. As a consequence, the proposed EMABC model performed well in terms of energy efficiency, with an efficiency of 82.44%, an end-to-end delay of 99.68ms, a packet drop rate of 152, a throughput of 680.28Kbps, packet delivery ratio of 98.05%, and network lifetime of 91%.