Distributed fuzzy clustering approach for balanced energy consumption in large-scale networks.
The rapid expansion of the Internet of Things (IoT) has heightened the demand for efficient data transmission within wireless sensor networks (WSNs). However, these networks continue to face major challenges related to energy efficiency, latency control, and battery lifespan, all of which critically influence their overall performance. A key issue is the uneven distribution of energy consumption among sensor nodes, often resulting in premature network breakdown and reduced longevity. To address this, we propose a novel fuzzy logic-based clustering scheme that optimizes cluster head (CH) selection through a multi-level fuzzy framework integrating three essential parameters: residual energy, distance to the base station, and applications requirements. In contrast to conventional methods, our approach combines unequal clustering with adaptive CH rotation to mitigate hotspot issues. Simulation experiments conducted in NS2 reveal that, compared to standard AODV and LEACH protocols, the proposed method increases network lifetime by about 45%, enhances energy efficiency by 30%, and improves data transmission reliability. The primary contribution of this work lies in the introduction of a multi-dimensional fuzzy evaluation mechanism that dynamically balances energy consumption across heterogeneous IoT scenarios while providing measurable benchmarks for clustering performance.