Hybrid African vulture and Aquila optimizer based efficient clustering approach for enhancing network longevity in wireless sensor networks

Wireless Sensor Networks are essentially utilized as a perception layer for gathering and processing necessary data from intelligent networks that integrate Internet of Things and Industrial Internet of Things fields. However, minimizing energy essential for data communication and processing seems to be the main challenge in the implementation of the network. Energy-based clustering schemes and the dynamic construction of Cluster Heads (CHs) are determined to improve network lifetime to intended level. The hybrid swarm intelligent metaheuristic algorithm was found to provide better performance during the clustering process with respect to improved execution time, accuracy, and feature selection. In this paper, a Hybrid African Vulture and Aquila Optimization Algorithm-based clustering mechanism is propounded with the characteristic that prevents premature convergence, poor solution diversity and imbalance between exploration and exploitation to attain superior energy-efficient Cluster Heads. It uses factors of balancing, sink distance, Residual Energy (RE) and mean intra-cluster distance for evaluation of Fitness Function. The energy stability of implemented clustering protocol confirmed a better mean network stability of 23.86%, throughput of 26.79% and RE sustenance of 29.32% than baseline mechanisms taken for experimentation.