An optimal model for enhancing network lifetime and cluster head selection using hybrid snake whale optimization

Wireless Sensor Networks (WSNs) are utilized Several applications like industrial, transportation, buildings, etc. due to their flexible communication, reliable utilization, less cost, and high accessibility. However, due to many issues related to lifetime and energy consumption, the ability of WSNs to broadcast information collected through the network appears to be a sophisticated process. Several efforts have been made to enhance energy-aware networking operations through the clustering process. Existing works address the issue of optimizing energy efficiency and lifetime of the network through optimal cluster head selection (CHS), topology control, and scheduling for collision reduction. But, in the clustering procedure, the cluster head (CH) selection remains a complicated task while a proper selection of CH will enhance the network lifetime. Therefore, this paper proposes a novel ‘Hybrid Snake Whale Optimization (HSWO) Algorithm’ to select optimal CH from the cluster group that helps to manage the network in broadcasting information to the destination. Three main phases included in the proposed concept are the initialization phase, the route maintenance phase and the CHS phase. At the initialization phase, the network model, distance model, and energy model are formulated. Secondly, the HSWO algorithm is applied to select the most optimal CHs from the clusters by eliminating the worst ones with the consideration of constraints namely delay, energy, and distance. Finally, in the route maintenance phase, the efficient path is chosen to broadcast the sensed data to the destination without any link breakages. The effectiveness of the HSWO algorithm is validated using different performance measures and the results proved that the proposed HSWO algorithm yielded a superior network lifetime of 5600 rounds, and normalized network energy of 0.98 compared to other existing techniques.