Wireless Sensor Networks (WSNs) have emerged as a vital enabler for diverse Internet of Things (IoT) applications, encompassing smart cities, healthcare monitoring, industrial automation, and environmental sensing. However, the inherent energy constraints of sensor nodes present a significant challenge, often resulting in limited network longevity, degraded performance, and inefficient data transmission. This paper introduces an advanced energy-efficient clustering protocol grounded in fuzzy logic, designed to address the limitations of conventional methods such as LEACH and HEED. Unlike traditional protocols that rely on probabilistic or static heuristics, the proposed framework employs a dynamic, multi-criteria fuzzy inference system to optimize the selection of Cluster Heads (CHs). Critical parameters including residual energy, node centrality, and proximity to the base station are evaluated to ensure robust CH selection, uniform energy dissipation, and enhanced scalability. Simulation results reveal marked improvements in network metrics—achieving a 40.8% reduction in overall energy consumption, a 51.2% increase in throughput, and a 55% enhancement in network lifespan compared to baseline methods. Additionally, the model significantly improves CH stability, minimizing control overhead and elevating network reliability. This study demonstrates the efficacy of integrating fuzzy logic into WSN clustering strategies, offering a highly adaptive, intelligent, and sustainable solution for next-generation IoT deployments.
Indhumathi et al. (2025) studied this question.