FBLSC: Fuzzy BLS-Based Competitive Clustering Optimization Algorithm for Energy Efficient in Wireless Sensor Networks

Improving energy efficiency in wireless sensor networks (WSNs) is critical to ensuring effective transmission and prolonging network lifetime. However, most existing clustering methods are designed for relatively static scenarios and thus fail to cope with dynamic scenarios such as node mobility, topology variations, and uneven energy consumption, leading to poor adaptability, suboptimal real-time performance, and low energy efficiency, and significantly hinder the applicability in real-world WSNs. To address these challenges, we propose a structural lightweight fuzzy broad learning-based competitive clustering algorithm (FBLSC) for efficient cluster head (CH) selection and adaptive clustering in dynamic WSNs. Specifically, a fuzzy logic system (FLS) is employed to extract and evaluate node features under uncertainty, generating compete value (CV), which are then used to train a broad learning system (BLS) for fast and accurate CH prediction. Compared with traditional fuzzy systems and PSO-based models, the proposed hybrid structure improves adaptability, learning speed, and decision accuracy while maintaining low computational complexity. The experimental results show that, averaged over six representative network scenarios, FBLSC reduces average energy consumption by 61.8%, achieves +107.2% longer LND network lifetime, and improves throughput by 97.7% compared with the same baseline. Due to its ability to enhance energy efficiency and prolong network lifetime, FBLSC is well-suited for deployment in dynamic, energy-constrained application scenarios such as environmental monitoring, smart agriculture, emergency response systems, and others.