UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning
Mobile CrowdSensing (MCS) is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles (UAVs) as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthq...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2023-02-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-2-57.pdf |
| _version_ | 1827965477454872576 |
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| author | Cui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG |
| author_facet | Cui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG |
| author_sort | Cui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG |
| collection | DOAJ |
| description | Mobile CrowdSensing (MCS) is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles (UAVs) as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes rescue.In this paper,we focus on scheduling UAVs to sense the task Point-of-Interests (PoIs) with different frequency coverage requirements.To accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach (G-MADDPG) to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm (DCA),then it regards each team as an agent.In this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy. |
| first_indexed | 2024-04-09T17:33:38Z |
| format | Article |
| id | doaj.art-6d22b765352b40c09a33677d4c43cd94 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:33:38Z |
| publishDate | 2023-02-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-6d22b765352b40c09a33677d4c43cd942023-04-18T02:33:17ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2023-02-01502576810.11896/jsjkx.221100114UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement LearningCui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG01 College of Computer Science and Technology,Jilin University, Changchun 130012,China ;2 College of Information Engineering,East China Jiaotong University,Nanchang 330013,ChinaMobile CrowdSensing (MCS) is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles (UAVs) as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes rescue.In this paper,we focus on scheduling UAVs to sense the task Point-of-Interests (PoIs) with different frequency coverage requirements.To accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach (G-MADDPG) to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm (DCA),then it regards each team as an agent.In this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-2-57.pdfuav crowdsensing|frequency coverage|grouping multi-agent deep reinforcement learning |
| spellingShingle | Cui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning Jisuanji kexue uav crowdsensing|frequency coverage|grouping multi-agent deep reinforcement learning |
| title | UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning |
| title_full | UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning |
| title_fullStr | UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning |
| title_full_unstemmed | UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning |
| title_short | UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning |
| title_sort | uav frequency based crowdsensing using grouping multi agentdeep reinforcement learning |
| topic | uav crowdsensing|frequency coverage|grouping multi-agent deep reinforcement learning |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-2-57.pdf |
| work_keys_str_mv | AT cuizhangenwangfuningyangyongjianyangnanjiang uavfrequencybasedcrowdsensingusinggroupingmultiagentdeepreinforcementlearning |