Review on Energy-Efficient Model Hybrid Clustering Technique in WSNs - Matthew Iyobhebhe; Abdoulie Momodou . S Tekanyi; K. A Abubilal; Yau Isiaku; E. E Agbon; Elvis Obi; Abubakar Umar; Ajayi Ore-ofe; Benjamin Amough Kwembe; Botson Ishaya Chollom; Ridwam. O Eleshin; Fatima Ashafa; Paul Thomas Muge (Universitas Negeri Surabaya or The State University of Surabaya, 2026)
This review article explores advancements in energy-efficient hybrid clustering techniques for Wireless Sensor Networks (WSNs), highlighting their significance for optimizing energy consumption and extending network longevity. As WSNs become integral to various applications, efficient energy management is crucial for prolonging node lifespan and ensuring reliable data transmission. The purpose of this review is to analytically examine previous energy-efficient hybrid clustering techniques in WSNs, with a specific emphasis on their mathematical modeling, this serves as a unifying framework for quantitatively evaluating energy efficiency, networklifetime, and transmission performance to enhance network stability. We analyze existing models and compare their effectiveness in minimizing energy use while maximizing data delivery efficiency. Related literature was identified through a methodical search of scientific databases covering publications from 2022 through 2025. Keywords such as hybrid clustering, energy efficiency, wireless sensor network, and energy consumption models were used to ensure comprehensive coverage of the field. The analysis shows that most studies focus on protocols such as LEACH, DCO-EENSCGA, FIS, BWOA, and BeeCluster, as well as parameter metrics such as node density, dead nodes, network lifetime, and so on, while equation-based modeling is rarely used. We also discuss the challenges in implementing these techniques, including scalability and network dynamics. This review synthesizes current research to highlight emerging trends and future directions in energy-efficient clustering strategies, offering practical guidance for researchers and practitioners aiming to enhance the sustainability and performance of WSNs.