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Research Scholar, Department of Computer Science and Engineering, Annamalai University , Chidambaram, Tamil Nadu , India
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Associate Professor, Department of Computer Science and Engineering, Annamalai University , Chidambaram, Tamil Nadu , India
Professor, Department of Computer Science and Engineering, Narasimha Reddy Engineering college , Hyderabad, Telangana , India
WSNs are critical to the contemporary IoT and monitoring, yet the energy constraint, inefficient performance of cluster head (CH) selection, and fluctuating routing diminish the network lifetime and reliability. This paper will introduce a solution to these issues by proposing a Hybrid Whale -Grey Wolf Optimizer (HWGWO) to select the adaptive and energy-efficient CH. The goal is to improve the energy balance, delivery of data, and stability of the network by integrating the global exploration capacity of the Whale Optimization Algorithm (WOA) and local exploitation capacity of the Grey Wolf Optimizer (GWO). The approach is based on a two-stage optimization strategy and multi-objective fitness that takes into account residual energy, communication distance, and load balancing. The suggested model is simulated in MATLAB and NS-2, and 100 nodes are placed in a 200 m by 200 m network. The simulation findings prove that HWGWO is more efficient than current protocols like EPSO-CEO and LEACH. The suggested strategy has a high Packet Delivery Ratio (PDR) of up to 99.20, a low normalized routing overhead (NRO) of 0.98, and a delay of 0.012s. It also reduces the ratio of packet drop to 0.033% and increases the throughput to 120,000 bps. Moreover, HWGWO minimizes the total energy expenditure to 10.5 J (approximately 3 % decrease) and keeps the residual energy (208.5 J) higher, thus increasing the network lifetime. Generally, HWGWO offers an efficient solution to energy-efficient clustering and dependable communication within the WSNs.
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