Multi-armed bandit based device scheduling for crowdsensing in power grids
With the increase of devices in power grids, a critical challenge emerges on how to collect information from massive devices, as well as how to manage these devices. Mobile crowdsensing is a large-scale sensing paradigm empowered by ubiquitous devices and can achieve more comprehensive observation o...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1141954/full |
| _version_ | 1828033384795865088 |
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| author | Jie Zhao Jie Zhao Yiyang Ni Yiyang Ni Huisheng Zhu Huisheng Zhu |
| author_facet | Jie Zhao Jie Zhao Yiyang Ni Yiyang Ni Huisheng Zhu Huisheng Zhu |
| author_sort | Jie Zhao |
| collection | DOAJ |
| description | With the increase of devices in power grids, a critical challenge emerges on how to collect information from massive devices, as well as how to manage these devices. Mobile crowdsensing is a large-scale sensing paradigm empowered by ubiquitous devices and can achieve more comprehensive observation of the area of interest. However, collecting sensing data from massive devices is not easy due to the scarcity of wireless channel resources and a large amount of sensing data, as well as the different capabilities among devices. To address these challenges, device scheduling is introduced which chooses a part of mobile devices in each time slot, to collect more valuable sensing data. However, the lack of prior knowledge makes the device scheduling task hard, especially when the number of devices is huge. Thus the device scheduling problem is reformulated as a multi-armed bandit (MAB) program, one should guarantee the participation fairness of sensing devices with different coverage regions. To deal with the multi-armed bandit program, a device scheduling algorithm is proposed on the basis of the upper confidence bound policy as well as virtual queue theory. Besides, we conduct the regret analysis and prove the performance regret of the proposed algorithm with a sub-linear growth under certain conditions. Finally, simulation results verify the effectiveness of our proposed algorithm, in terms of performance regret and convergence rate. |
| first_indexed | 2024-04-10T15:18:55Z |
| format | Article |
| id | doaj.art-fd1dabbaf5f54817a7806e317e71a5f3 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T15:18:55Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-fd1dabbaf5f54817a7806e317e71a5f32023-02-14T17:30:26ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-02-011110.3389/fenrg.2023.11419541141954Multi-armed bandit based device scheduling for crowdsensing in power gridsJie Zhao0Jie Zhao1Yiyang Ni2Yiyang Ni3Huisheng Zhu4Huisheng Zhu5College of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, ChinaJiangsu Province Engineering Research Center of Basic Education Big Data Application, Jiangsu Second Normal University, Nanjing, ChinaCollege of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, ChinaJiangsu Province Engineering Research Center of Basic Education Big Data Application, Jiangsu Second Normal University, Nanjing, ChinaCollege of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, ChinaJiangsu Province Engineering Research Center of Basic Education Big Data Application, Jiangsu Second Normal University, Nanjing, ChinaWith the increase of devices in power grids, a critical challenge emerges on how to collect information from massive devices, as well as how to manage these devices. Mobile crowdsensing is a large-scale sensing paradigm empowered by ubiquitous devices and can achieve more comprehensive observation of the area of interest. However, collecting sensing data from massive devices is not easy due to the scarcity of wireless channel resources and a large amount of sensing data, as well as the different capabilities among devices. To address these challenges, device scheduling is introduced which chooses a part of mobile devices in each time slot, to collect more valuable sensing data. However, the lack of prior knowledge makes the device scheduling task hard, especially when the number of devices is huge. Thus the device scheduling problem is reformulated as a multi-armed bandit (MAB) program, one should guarantee the participation fairness of sensing devices with different coverage regions. To deal with the multi-armed bandit program, a device scheduling algorithm is proposed on the basis of the upper confidence bound policy as well as virtual queue theory. Besides, we conduct the regret analysis and prove the performance regret of the proposed algorithm with a sub-linear growth under certain conditions. Finally, simulation results verify the effectiveness of our proposed algorithm, in terms of performance regret and convergence rate.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1141954/fullcrowdsensingdevice schedulingmulti-armed bandit (MAB)edge intelligencepower grid |
| spellingShingle | Jie Zhao Jie Zhao Yiyang Ni Yiyang Ni Huisheng Zhu Huisheng Zhu Multi-armed bandit based device scheduling for crowdsensing in power grids Frontiers in Energy Research crowdsensing device scheduling multi-armed bandit (MAB) edge intelligence power grid |
| title | Multi-armed bandit based device scheduling for crowdsensing in power grids |
| title_full | Multi-armed bandit based device scheduling for crowdsensing in power grids |
| title_fullStr | Multi-armed bandit based device scheduling for crowdsensing in power grids |
| title_full_unstemmed | Multi-armed bandit based device scheduling for crowdsensing in power grids |
| title_short | Multi-armed bandit based device scheduling for crowdsensing in power grids |
| title_sort | multi armed bandit based device scheduling for crowdsensing in power grids |
| topic | crowdsensing device scheduling multi-armed bandit (MAB) edge intelligence power grid |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1141954/full |
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