Exploring AI and Metaheuristic Optimization in Clustering-Based Localization for WSNs: A Comprehensive Review

Mohammed D. Almustapha, Ramat V. Usman, Ezekiel E. Agbon, Habeeb Bello

Abstract


Wireless Sensor Networks (WSNs) have become increasingly important in various applications, such as, Internet of Things (IoT), healthcare, target tracking, smart cities, underwater exploration, ecosystem monitoring, and military systems require reliable and accurate localization techniques to enable effective monitoring and data collection, with localization being a critical aspect particularly in large scale deployment.  Localization techniques, including range-based methods like Time of Arrival (TOA), Received Signal Strength (RSS), and range-free methods like centroid-based and hop-count-based approaches, aim to accurately determine the targets position. Clustering can improve localization accuracy and energy efficiency. This review explores the integration of Artificial Intelligence (AI) and metaheuristic optimization techniques in clustering-based localization for WSNs. We analyze how AI-driven approaches and metaheuristic algorithms can enhance localization performance, addressing challenges like multipath fading, noise, non-line-of-sight (NLOS) conditions, interference and node mobility. The review identifies opportunities and future research directions, focusing on improving accuracy, reducing energy consumption, and increasing scalability. This study provides insights for researchers and developers working on WSN-based applications, highlighting the potential of AI and metaheuristic optimization in addressing localization challenges.


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