Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Power Efficiency Optimization and Enhanced Connectivity for Wireless Sensor Networks with the Fuzzy C-Means 3D Model

Version 1 : Received: 7 February 2024 / Approved: 7 February 2024 / Online: 8 February 2024 (15:49:44 CET)

How to cite: Le, D.P.C.; Wang, D.; Tu, N.H. Power Efficiency Optimization and Enhanced Connectivity for Wireless Sensor Networks with the Fuzzy C-Means 3D Model. Preprints 2024, 2024020462. https://doi.org/10.20944/preprints202402.0462.v1 Le, D.P.C.; Wang, D.; Tu, N.H. Power Efficiency Optimization and Enhanced Connectivity for Wireless Sensor Networks with the Fuzzy C-Means 3D Model. Preprints 2024, 2024020462. https://doi.org/10.20944/preprints202402.0462.v1

Abstract

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

Keywords

Wireless sensor networks; FCM-3WUSN clustering algorithm; Routing algorithm; Power load balancing; Optimization learning.

Subject

Computer Science and Mathematics, Computer Networks and Communications

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