Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective
Mobile crowdsensing (MCS) is a promising sensing paradigm based on the mobile node which provides the solution with cost-effectiveness to perform urban data collection. To monitor the urban environment and facilitate the municipal administration, more and more applications adopt vehicles as particip...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8667822/ |
| _version_ | 1818924062519328768 |
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| author | Chaowei Wang Xiga Gaimu Chensheng Li He Zou Weidong Wang |
| author_facet | Chaowei Wang Xiga Gaimu Chensheng Li He Zou Weidong Wang |
| author_sort | Chaowei Wang |
| collection | DOAJ |
| description | Mobile crowdsensing (MCS) is a promising sensing paradigm based on the mobile node which provides the solution with cost-effectiveness to perform urban data collection. To monitor the urban environment and facilitate the municipal administration, more and more applications adopt vehicles as participants to carry out MCS tasks. The performance of the applications highly depends on the sensing data which is influenced by the recruiting strategy on vehicles. In this paper, we propose a novel vehicle selection algorithm to maximize the sensing range with limited cost while the vehicle selection problem was proved to be NP-complete. Specifically, we modeled the interaction between MCS server and candidate vehicles as a Markov decision process and formulated the maximum spatial temporal coverage (STC) optimization as a deep reinforcement learning process. The performance of our deep reinforcement learning-based vehicle selection (i.e., DRLVS) algorithm is evaluated with real trajectory dataset. The numerical result indicates that the proposed algorithm achieves an optimal solution and maximizes the STC. |
| first_indexed | 2024-12-20T02:19:21Z |
| format | Article |
| id | doaj.art-12b8ce75ebdf4497aa461be812eb367f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:19:21Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-12b8ce75ebdf4497aa461be812eb367f2022-12-21T19:56:51ZengIEEEIEEE Access2169-35362019-01-017373343734110.1109/ACCESS.2019.29052638667822Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning PerspectiveChaowei Wang0Xiga Gaimu1https://orcid.org/0000-0003-2660-0875Chensheng Li2He Zou3Weidong Wang4School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaMobile crowdsensing (MCS) is a promising sensing paradigm based on the mobile node which provides the solution with cost-effectiveness to perform urban data collection. To monitor the urban environment and facilitate the municipal administration, more and more applications adopt vehicles as participants to carry out MCS tasks. The performance of the applications highly depends on the sensing data which is influenced by the recruiting strategy on vehicles. In this paper, we propose a novel vehicle selection algorithm to maximize the sensing range with limited cost while the vehicle selection problem was proved to be NP-complete. Specifically, we modeled the interaction between MCS server and candidate vehicles as a Markov decision process and formulated the maximum spatial temporal coverage (STC) optimization as a deep reinforcement learning process. The performance of our deep reinforcement learning-based vehicle selection (i.e., DRLVS) algorithm is evaluated with real trajectory dataset. The numerical result indicates that the proposed algorithm achieves an optimal solution and maximizes the STC.https://ieeexplore.ieee.org/document/8667822/Mobile crowdsensingspatial-temporal coveragedeep reinforcement learning |
| spellingShingle | Chaowei Wang Xiga Gaimu Chensheng Li He Zou Weidong Wang Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective IEEE Access Mobile crowdsensing spatial-temporal coverage deep reinforcement learning |
| title | Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective |
| title_full | Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective |
| title_fullStr | Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective |
| title_full_unstemmed | Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective |
| title_short | Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective |
| title_sort | smart mobile crowdsensing with urban vehicles a deep reinforcement learning perspective |
| topic | Mobile crowdsensing spatial-temporal coverage deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/8667822/ |
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