Wireless Sensor Networks Nodes Clustering and Optimization Based on Fuzzy C-Means and Water Strider Algorithms

Abstract

Wireless sensor networks (WSNs) face critical challenges in energy management and network lifetime optimization due to limited battery resources and communication overhead. This study introduces a novel hybrid clustering protocol that integrates the Water Strider Algorithm (WSA) with Fuzzy C-Means (FCM) clustering to achieve superior energy efficiency and network longevity. The proposed WSA-FCM method employs WSA for global optimization of cluster-head positions and FCM for refined node membership assignment with fuzzy boundaries. Through extensive experimentation across networks of 200-800 nodes with 10 independent simulation runs, the method demonstrates significant improvements: First Node Death (FND) delayed by 16.1% ($678\pm12$ vs $584\pm18$ rounds), Last Node Death (LND) extended by 11.9% ($1,262\pm8$ vs $1,128\pm11$ rounds), and 37.4% higher residual energy retention ($5.47\pm0.09$ vs $3.98\pm0.11$ J) compared to state-of-the-art hybrid methods. Intra-cluster distances are reduced by 19.4% with statistical significance (p < 0.001). Theoretical analysis proves convergence guarantees and complexity bounds of $O(n\times c\times T)$, while empirical scalability analysis demonstrates near-linear scaling behaviour. The method outperforms recent hybrid approaches including MOALO-FCM, MSSO-MST, Fuzzy+HHO, and GWO-FCM across all performance metrics with rigorous statistical validation.