Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks: A Methodological Survey

Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides an in-depth review of the applications of GWO and its variants for clustering, multi-hop routing, and hybrid cluster-based routing in WSNs. We categorize and analyze the existing GWO-based approaches across these key network optimization tasks, discussing the different problem formulations, decision variables, objective functions, and performance metrics used. In doing so, we examine standard GWO, multi-objective GWO, and hybrid GWO models that incorporate other computational intelligence techniques. Each method is evaluated based on how effectively it addresses the core constraints of WSNs, including energy consumption, communication overhead, and network lifetime. Finally, this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing. Our goal is to provide researchers and practitioners with a clear, structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.