Energy-efficient cluster head selection in Internet of Things networks using an optimized evaporation rate water-cycle algorithm

Abstract This paper presents a new scheme for energy-efficient clustering in Internet of Things (IoT) networks by employing an optimized evolutionary rate water cycle algorithm (OERWCA), aiming to address crucial issues, such as energy conservation measured through average energy consumption per node, network longevity quantified by total operational rounds until node depletion, and throughput as an indicator of data transmission efficiency. In OERWCA, a local escaping operator (LEO) is introduced to avoid algorithm trapping in local optima by enhancing its exploration capability. Besides, advanced control-randomization operators balance exploration and exploitation dynamically for efficient search behavior in the solution space. The algorithm optimizes cluster head selection by minimizing energy consumption and redundant transmission. Simulations comparing OERWCA with previous optimization methods, including NCCLA, FHHO, and EACH-COA, demonstrate the superior performance of the proposed algorithm. Key metrics evaluated include network lifetime, throughput, average transmission delay, packet delivery ratio (PDR), and energy efficiency. OERWCA achieves significant improvements, including up to a 26% increase in network lifetime, a 32% boost in throughput, a 20% reduction in transmission delay, and a 27% enhancement in PDR compared to the best-performing benchmarks. These results highlight OERWCA’s effectiveness in optimizing critical performance parameters for IoT networks. The enhanced convergence properties of the proposed algorithm also address some common limitations found in existing methods. This work, therefore, provides a robust solution toward extending the operational lifetime of IoT networks, which is one of the fundamental steps forward in large-scale efficient resource management.