Clustering Based on Gray Wolf Optimization Algorithm for Internet of Things over Wireless Nodes

The Internet of Things (IoT) creates an environment where things are permitted to act, hear, listen, and talk. IoT devices encompass a wide range objects, from basic sensors intelligent devices, capable exchanging information with or without human intervention. However, the integration wireless nodes in systems brings about both advantages challenges. While connectivity enhances system functionality, it also introduces constraints on resources, including power consumption, memory, CPU processing capacity. Among these limitations, energy consumption emerges as critical challenge. To address challenges, metaheuristic algorithms have been widely employed optimize routing patterns networks. This paper proposes novel clustering strategy based Gray Wolf Optimization (GWO) algorithm. GWO-based approach aims achieve efficiency improve overall network performance. Experimental results demonstrate significant improvements key performance metrics. Specifically, proposed achieves up 14% reduction 34% decrease end-to-end delay, 10% increase packet delivery rate compared existing approaches. findings this research contribute advancement energy-efficient high-performance utilization GWO algorithm for network's ability conserve energy, reduce latency, data packets. These outcomes highlight effectiveness potential addressing resource limitations optimizing environments.