Optimal hybrid energy-saving cluster head selection for wireless sensor networks: an empirical study

Wireless sensor networks (WSNs) are gaining attention in modern technology, and are valued for their affordability and versatility. However, energy efficiency is a still major challenge, especially since sensor nodes are often deployed in remote areas where replacing or recharging batteries is difficult. While clustering algorithms and cluster head (CH) selection methods have been recognized as key strategies for extending network lifetime, existing studies often lack a comprehensive evaluation of hybrid CH selection techniques. This paper addresses these gaps by presenting a detailed analysis of clustering algorithms aimed at optimizing energy consumption in WSNs and evaluating various hybrid CH selection procedures. A key contribution of this work is the development of a framework that compares these methods across multiple performance metrics, including Packet Delivery Ratio (PDR), energy consumption, delay, network lifetime, and routing efficiency, using robust simulation tools. The findings demonstrate that hybrid CH selection techniques can significantly enhance energy efficiency while maintaining or improving network performance. By providing a thorough quantitative analysis and actionable insights, this research advances the state of the art in WSN energy management and offers practical implications for designing more sustainable and efficient sensor networks. The proposed methodologies and findings can serve as a foundation for future studies and real-world deployments, addressing critical challenges in resource-constrained environments.