Abstract

Wireless Sensor Networks (WSNs) are mainly used for continuous monitoring and reliable data transmission are essential. However, the limited battery capacity of sensor nodes poses significant challenges to long-term network operation. Clustering is an effective strategy to reduce communication overhead, but selecting an optimal Cluster Head (CH) remains a complex task due to varying node energy, distance, and network conditions. This study proposes a hybrid Machine Learning–Firefly Optimization–based Cluster Head selection (ML–FOA–CH) approach that combines predictive fitness evaluation with metaheuristic optimization. Machine learning models assess node suitability using key features, while FOA refines the search by maximizing brightness values. Experimental results show that ML–FOA–CH significantly improves CH selection accuracy, prolongs network lifetime, and delays the first node death compared to LEGN, TEGN, and traditional FOA-based methods. The proposed model demonstrates superior adaptability and energy efficiency, making it a promising solution for intelligent and sustainable WSN operations.

Authors

D. Angeline Ranjithamani, R.S. Rajesh
Manonmaniam Sundaranar University, India

Keywords

Cluster Head Selection, Energy Efficiency, Firefly Optimization Algorithm, Machine Learning, Residual Energy, Wireless Sensor Network