Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system

• Published in: Scientific reports Vol. 14; no. 1; pp. 27931 - 19
• Main Authors: Bao, Xuecai, Jiang, Yanlong, Han, Longzhe, Xu, Xiaohua, Zhu, Hongbo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/705/117; 639/705/258; Algorithms; Collaborative beamforming; Energy; Energy utilization; Humanities and Social Sciences; multidisciplinary; Node selection; Nodes; Optimization; Science; Science (multidisciplinary); Sensors; Sidelobe control; Solar energy; Wind power; Wireless sensor network; Wireless sensor network; Sidelobe control; Collaborative beamforming; Node selection
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-78671-1

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by 10.5 % and 11.2 % , respectively. The performance of the average number of active sensor nodes decreased by 13.2 % and 16.4 % compared to existing algorithms, while the average coverage redundancy decreased by 23.2 % and 21.6 % relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.