A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing
Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2043825 |
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| author | Zhaohua Zheng Zhaobin Qin Keqiu Li Tie Qiu |
| author_facet | Zhaohua Zheng Zhaobin Qin Keqiu Li Tie Qiu |
| author_sort | Zhaohua Zheng |
| collection | DOAJ |
| description | Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individuals need higher transportation costs to go to the task location alone, and the data quality perceived by individuals is difficult to guarantee. This paper proposes a team-based multitask data acquisition scheme under time constraints to address these challenges. The scheme optimised the number of participants, traffic cost, and data quality and designed four team-based multitask allocation algorithms under time constraints in the MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, and T-MostMITeam. The team size is associated with the number of participants required for the first task or the vehicle capacity to perform the task. We conducted extensive experiments based on a real large-scale dataset to evaluate the four algorithms' performances compared to two baseline algorithms (T-Random and T-most). The efficiency of the four algorithms has been significantly improved by team recruitment. The transportation cost can be multiplicatively reduced by carpooling. Data quality can be improved by at least 2% through reputation screening and team members' communication. |
| format | Article |
| id | doaj-art-c6fb3251a3ed4d75a534e1c4dda8fbc5 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-c6fb3251a3ed4d75a534e1c4dda8fbc52025-08-20T03:05:09ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411119114510.1080/09540091.2022.20438252043825A team-based multitask data acquisition scheme under time constraints in mobile crowd sensingZhaohua Zheng0Zhaobin Qin1Keqiu Li2Tie Qiu3Tianjin UniversityHainan UniversityTianjin UniversityTianjin UniversityMobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individuals need higher transportation costs to go to the task location alone, and the data quality perceived by individuals is difficult to guarantee. This paper proposes a team-based multitask data acquisition scheme under time constraints to address these challenges. The scheme optimised the number of participants, traffic cost, and data quality and designed four team-based multitask allocation algorithms under time constraints in the MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, and T-MostMITeam. The team size is associated with the number of participants required for the first task or the vehicle capacity to perform the task. We conducted extensive experiments based on a real large-scale dataset to evaluate the four algorithms' performances compared to two baseline algorithms (T-Random and T-most). The efficiency of the four algorithms has been significantly improved by team recruitment. The transportation cost can be multiplicatively reduced by carpooling. Data quality can be improved by at least 2% through reputation screening and team members' communication.http://dx.doi.org/10.1080/09540091.2022.2043825mobile crowd sensingmultitask allocationparticipant selectionteam formationtransportation costdata quality |
| spellingShingle | Zhaohua Zheng Zhaobin Qin Keqiu Li Tie Qiu A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing Connection Science mobile crowd sensing multitask allocation participant selection team formation transportation cost data quality |
| title | A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| title_full | A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| title_fullStr | A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| title_full_unstemmed | A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| title_short | A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| title_sort | team based multitask data acquisition scheme under time constraints in mobile crowd sensing |
| topic | mobile crowd sensing multitask allocation participant selection team formation transportation cost data quality |
| url | http://dx.doi.org/10.1080/09540091.2022.2043825 |
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