HGWO: A Novel Hybrid Optimization Algorithm for Energy-Aware Clustering and Routing in WSNs

Abstract Wireless Sensor Networks (WSNs) play an important role in real-time data acquisition in Internet of Things (IoT) applications such as smart cities and healthcare. On the other hand, energy constraints in battery-powered sensor nodes greatly challenge the longevity and performance of the network. The current study presents a hybrid of Grey Wolf Optimization (GWO) and Harris Hawks Optimization (HHO) algorithm to enhance cluster head (CH) selection and multi-hop routing, enhancing energy efficiency and increasing the lifecycle of the network. Comparative analysis with HAS-PSO, FFCGWO, HABC-MBOA, and EAFFO-CS reveals that the proposed approach achieves up to 40% improvement in network lifetime, 35% higher throughput, and 30% greater residual energy over existing methods. The proposed method extends the operational lifetime of the first node and keeps the higher number of the alive nodes alive during the entire network life. Integration of adaptive data aggregation and an intelligent fitness function taking into account residual energy, node degree, distance, and CH history has very well reduced node depletion. Results indicate that the proposed scheme enhances network efficiency and prolongs the lifetime of sensor nodes while minimizing maintenance costs. These results underscore the importance of intelligent energy management for WSNs, paving the way for robust, sustainable, and high-performance IoT-the basis on WSN. It also indicates that the proposed method increases network lifetime and energy efficiency, which is a good alternative for IoT-based WSN deployments having limited resources. By extending the operational lifetime of sensor nodes, use cases centred on energy management lower maintenance costs and improve data gathering dependability.