{"ID":2843754,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06735","arxiv_id":"2511.06735","title":"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 \u003c 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.","short_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 a...","url_abs":"https://arxiv.org/abs/2511.06735","url_pdf":"https://arxiv.org/pdf/2511.06735v1","authors":"[\"Raya Majid Alsharfa\",\"Mahmood Mohassel Feghhi\",\"Majid Hameed Majeed\"]","published":"2025-11-10T05:58:38Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"eess.SP\",\"math.OC\"]","methods":"[]","has_code":false}