Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system
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...
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| Vydané v: | Scientific reports Ročník 14; číslo 1; s. 27931 - 19 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Nature Publishing Group UK
14.11.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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
%
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. |
|---|---|
| AbstractList | 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
and
, respectively. The performance of the average number of active sensor nodes decreased by
and
compared to existing algorithms, while the average coverage redundancy decreased by
and
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. 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.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. 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. 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. 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. 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. |
| ArticleNumber | 27931 |
| Author | Zhu, Hongbo Bao, Xuecai Han, Longzhe Jiang, Yanlong Xu, Xiaohua |
| Author_xml | – sequence: 1 givenname: Xuecai surname: Bao fullname: Bao, Xuecai email: lx97821@nit.edu.cn organization: Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang Institute of Technology, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications – sequence: 2 givenname: Yanlong surname: Jiang fullname: Jiang, Yanlong organization: Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang Institute of Technology, School of Information Engineering, Nanchang Institute of Technology – sequence: 3 givenname: Longzhe surname: Han fullname: Han, Longzhe organization: Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang Institute of Technology, School of Information Engineering, Nanchang Institute of Technology – sequence: 4 givenname: Xiaohua surname: Xu fullname: Xu, Xiaohua organization: Smart Water Conservancy Research Institute, Jiangxi Academy of Water Science and Engineering – sequence: 5 givenname: Hongbo surname: Zhu fullname: Zhu, Hongbo organization: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39537671$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/JSEN.2021.3076203 10.1007/s11276-022-03224-1 10.1109/TAC.2008.2010885 10.1007/s11277-021-09381-4 10.1109/TNET.2019.2926403 10.1109/JSEN.2022.3227601 10.1016/j.solener.2005.03.013 10.1109/JSEN.2019.2948620 10.1109/LCOMM.2021.3110771 10.1109/JIOT.2019.2930073 10.1109/JSEN.2014.2310180 10.1109/LCOMM.2019.2904578 10.1109/JIOT.2018.2817590 10.1109/TII.2016.2603845 10.1109/ACCESS.2018.2833632 10.1016/j.renene.2010.12.001 10.1109/ACCESS.2024.3365511 10.1109/TMC.2018.2872576 10.1109/TVT.2019.2912188 10.1109/TVT.2019.2908584 10.1109/TVT.2014.2322356 10.1109/MCOM.2015.7321985 10.1109/JSEN.2024.3378998 10.1109/GLOCOM.2014.7036824 10.1109/ICC.2014.6883345 10.1109/TNSE.2019.2952369 10.1109/TETC.2014.2371543 10.1109/JSEN.2013.2286332 10.1109/TMC.2018.2813376 10.1109/INFOCOM.2018.8485808 10.1109/JIOT.2021.3109148 10.1109/JSYST.2018.2820085 10.1016/j.adhoc.2016.10.010 10.1109/INFCOM.2005.1498475 10.1155/2019/6312589 |
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| Keywords | Wireless sensor network Sidelobe control Collaborative beamforming Node selection |
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| SubjectTerms | 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 |
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| Title | Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system |
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