EER-CGHHOA: A Hybrid Genetic Algorithm Driven Dynamic Clustering for Energy Efficient Routing in Border Surveillance WSNs

Wireless sensor networks (WSN) are challenging yet playing a crucial role in data dissemination and reception because sensors featuring nodes are used in remote areas due to their usability in real-time scenarios. These sensor nodes operate with limited battery energy and memory which needs to be utilized efficiently to improve network performance in terms of routing, communication between nodes, network longevity, reducing overhead and latency, especially in border surveillance, disaster management, and more. Moreover, a common approach to tackle the random distribution of sensor nodes across a network in clustering, as this process is complex, but crucial for determining overall network performance. This research describes a unique approach for optimizing the border surveillance WSN that mixes a hybrid Energy Efficient Routing with Cluster based Genetic Harris Hawkeye Optimization Algorithm (EER-CGHHOA) that is suitable for highly distributed networks, and the main objective of this approach is to conserve node energy and to find optimized routes with fitness function calculation. Genetic based Harris Hawkeye optimization algorithm is applied to ensure optimal routing with reduced latency, prolonged network lifetime, and minimal energy consumption across the WSN using selection, crossover, and mutation operators when needed. The performance of EER-CGHHOA, and the results were compared with other benchmarking methods. From the results, it is observed that the proposed approach has consumed 11.36% less energy, has delivered 97.02% packets to base station and reduced the network delay by 6.25% than other approaches considered in this study. The results showed that EER-CGHHOA outperformed other approaches with reduced latency, minimized energy consumption, and higher packet delivery ratio.