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Fast participant recruitment algorithm for large-scale Vehicle-based Mobile Crowd Sensing

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成果类型:
期刊论文
作者:
Yi, Kefu*;Du, Ronghua;Liu, Li;Chen, Qingying;Gao, Kai
通讯作者:
Yi, Kefu
作者机构:
[Liu, Li; Du, Ronghua; Yi, Kefu; Gao, Kai] Changsha Univ Sci & Technol, Sch Automot & Mech Engn, Changsha 410114, Hunan, Peoples R China.
[Du, Ronghua] Key Lab Safety Design & Reliabil Technol Engn Veh, Changsha 410114, Hunan, Peoples R China.
[Liu, Li] Coll Hunan Prov, Key Lab Lightweight & Reliabil Technol Engn Vehic, Changsha 410114, Hunan, Peoples R China.
[Chen, Qingying] Univ Tulsa, Dept Phys & Engn Phys, Tulsa, OK 74104 USA.
通讯机构:
[Yi, Kefu] C
Changsha Univ Sci & Technol, Sch Automot & Mech Engn, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
Economic and social effects;Vehicles;Crowd sensing;Linear time complexity;Maximization problem;On-board resources;Participant recruitment;Participatory Sensing;Trace driven simulation;Vehicular sensing;Approximation algorithms
期刊:
Pervasive and Mobile Computing
ISSN:
1574-1192
年:
2017
卷:
38
页码:
188-199
基金类别:
This research was supported by the National Natural Science Foundation of China (11272067, 61403047, 61503048), the Scientific Research Fund of Hunan Provincial Education Department (16C0044).
机构署名:
本校为第一且通讯机构
院系归属:
汽车与机械工程学院
摘要:
Mobile crowd sensing has become an emerging computing and sensing paradigm that recruits ordinary participants to perform sensing tasks. With the highly dynamic mobility pattern and the abundance of on-board resources, vehicles have been increasingly recruited to participate large-scale crowd sensing applications such as urban sensing. However, existing participant recruitment algorithms take a long time in recruitment decision for large number of vehicular participants. In this paper, a fast algorithm for vehicle participant recruitment proble...

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