Energy‐Aware Multi‐Sink Deployment for Hotspot Mitigation in Heterogeneous Wireless Sensor Networks

ABSTRACT Recent advancements have established wireless sensor networks (WSNs) as a key technology for real‐time monitoring in hostile and inaccessible environments, such as dense forests, volcanic regions, and war zones. However, their performance is still bound by limited energy resources, and their distributed nature promotes load imbalance, leading to hotspots with consequent early network failure. The use of heterogeneous WSNs relaxes some of the above‐mentioned problems by deploying nodes with different energy capacities, and yet achieving energy‐aware routing and effective hotspot mitigation remains hard to achieve under dynamic network conditions. This paper proposes Harris Hawks and Grey Wolf Optimization, a hybrid metaheuristic framework that integrates K‐medoids clustering with Sooty Tern Optimization for optimal cluster head selection, Harris Hawks Optimization for adaptive mobile sink placement, and Grey Wolf Optimization for energy‐efficient routing path construction. The proposed approach dynamically balances residual energy and communication distance to effectively mitigate hotspots and ensure stable data forwarding. Comprehensive simulations conducted in NS‐2.35 show that HHGWO constantly outperforms existing protocols, including stable election protocol, distributed stable election protocol, energy‐aware stable routing algorithm, hybrid Harris Hawks and ant colony optimization, and smart hybrid optimization for cluster‐head selection, by achieving a 2.16% extension in network lifetime, a 19.51% reduction in dead nodes, and up to a 13.85% improvement in average residual energy while maintaining lower time complexity and stable convergence.