Optimization‐Driven Localization in Wireless Sensor Networks: A Comprehensive Review of Single and Hybrid Metaheuristic Approaches

ABSTRACT

Wireless sensor networks (WSNs), consisting of numerous smart sensor nodes, are increasingly integrated into domains including battlefield surveillance, environmental monitoring, industrial automation, and smart cities. Precise sensor positioning ensures effective communication, optimizes energy consumption, and maintains dependable system performance. However, the dynamic nature of WSNs, characterized by random node deployment, mobility, energy constraints, and environmental unpredictability, poses significant localization challenges. Traditional techniques often fall short under these conditions. Although several studies have proposed optimization‐based approaches for localization, there is a lack of consolidated, up‐to‐date reviews that compare single and hybrid metaheuristic techniques across performance metrics. Additionally, past reviews often overlook recent trends, bibliometric insights, and evolving research challenges. This study provides an in‐depth survey of positioning methods employed in WSNs, focusing on single and hybrid metaheuristic methods. It introduces a year‐wise comparative framework that evaluates these techniques based on accuracy, energy consumption, localization error, computational complexity, scalability, and node distribution. A bibliometric analysis using the Web of Science database further explores research trends, identifies leading contributors, and tracks the evolution of keywords. Tables and figures summarize the advantages, drawbacks, and enhancements of each approach. The study highlights that hybrid optimization models consistently outperform single‐method techniques, achieving up to 25% lower localization error and improved energy efficiency. The findings also identify future directions in software‐defined networking and ML‐integrated hybrid models, providing a strong foundation for advancing localization in next‐generation WSNs.