User Characteristic Aware Participant Selection for Mobile Crowdsensing
Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/11/3959 |
| _version_ | 1811184924054519808 |
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| author | Dapeng Wu Haopeng Li Ruyan Wang |
| author_facet | Dapeng Wu Haopeng Li Ruyan Wang |
| author_sort | Dapeng Wu |
| collection | DOAJ |
| description | Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner. |
| first_indexed | 2024-04-11T13:20:35Z |
| format | Article |
| id | doaj.art-ba4090ae992e41b09e140a1702eb6b49 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:20:35Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ba4090ae992e41b09e140a1702eb6b492022-12-22T04:22:12ZengMDPI AGSensors1424-82202018-11-011811395910.3390/s18113959s18113959User Characteristic Aware Participant Selection for Mobile CrowdsensingDapeng Wu0Haopeng Li1Ruyan Wang2School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaMobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner.https://www.mdpi.com/1424-8220/18/11/3959mobile crowdsensingparticipant selectionregional heatuser characteristic |
| spellingShingle | Dapeng Wu Haopeng Li Ruyan Wang User Characteristic Aware Participant Selection for Mobile Crowdsensing Sensors mobile crowdsensing participant selection regional heat user characteristic |
| title | User Characteristic Aware Participant Selection for Mobile Crowdsensing |
| title_full | User Characteristic Aware Participant Selection for Mobile Crowdsensing |
| title_fullStr | User Characteristic Aware Participant Selection for Mobile Crowdsensing |
| title_full_unstemmed | User Characteristic Aware Participant Selection for Mobile Crowdsensing |
| title_short | User Characteristic Aware Participant Selection for Mobile Crowdsensing |
| title_sort | user characteristic aware participant selection for mobile crowdsensing |
| topic | mobile crowdsensing participant selection regional heat user characteristic |
| url | https://www.mdpi.com/1424-8220/18/11/3959 |
| work_keys_str_mv | AT dapengwu usercharacteristicawareparticipantselectionformobilecrowdsensing AT haopengli usercharacteristicawareparticipantselectionformobilecrowdsensing AT ruyanwang usercharacteristicawareparticipantselectionformobilecrowdsensing |