Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system
Abstract 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 cove...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-78671-1 |
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| Summary: | Abstract 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 $$10.5\%$$ 10.5 % and $$11.2\%$$ 11.2 % , respectively. The performance of the average number of active sensor nodes decreased by $$13.2\%$$ 13.2 % and $$16.4\%$$ 16.4 % compared to existing algorithms, while the average coverage redundancy decreased by $$23.2\%$$ 23.2 % and $$21.6\%$$ 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. |
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| ISSN: | 2045-2322 |