? A Survey on Task and Participant Matching in Mobile Crowd Sensing
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :768-791    DOI: 10.1007/s11390-018-1855-y
Special Issue on Software Engineering for High-Confidence Systems Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
A Survey on Task and Participant Matching in Mobile Crowd Sensing
Yue-Yue Chen1, Pin Lv2,3,*, Member, CCF, ACM, IEEE De-Ke Guo4,5, Distinguished Member, CCF, Senior Member, IEEE, Member, ACM, Tong-Qing Zhou1, Ming Xu1, Member, CCF, ACM, IEEE
1 College of Computer, National University of Defense Technology, Changsha 410073, China;
2 School of Computer Electronics and Information, Guangxi University, Nanning 530004, China;
3 Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University, Nanning 530004, China;
4 College of System Engineering, National University of Defense Technology, Changsha 410073, China;
5 School of Computer Science and Technology, Tianjin University, Tianjin 300072, China

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Abstract Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.
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Keywordsmobile crowd sensing   participant selection   task allocation   task and participant matching     
Received 2017-08-24;
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This work was partially supported by the National Natural Science Foundation for Outstanding Excellent Young Scholars of China under Grant No. 61422214, the National Natural Science Foundation of China under Grant Nos. 61402513, 61379144, and 61772544, the National Basic Research 973 Program of China under Grant No. 2014CB347800, the Hunan Provincial Natural Science Fund for Distinguished Young Scholars of China under Grant No. 2016JJ1002, the Natural Science Foundation of Guangxi Zhuang Autonomous Region of China under Grant No. 2016GXNSFBA380182, the Guangxi Cooperative Innovation Center of Cloud Computing and Big Data under Grant Nos. YD16507 and YD17X11, and the Scientific Research Foundation of Guangxi University under Grant Nos. XGZ150322 and XGZ141182.

Corresponding Authors: Pin Lv,E-mail:lvpin@gxu.edu.cn     Email: lvpin@gxu.edu.cn
About author: Yue-Yue Chen received her B.S. and M.S. degrees in computer science and technology from National University of Defense Technology (NUDT), Changsha, in 2013 and 2015, respectively. She is currently a Ph.D. candidate in College of Computer, NUDT, Changsha. Her main research interests include mobile crowd sensing, task assignment, etc.
Cite this article:   
Yue-Yue Chen, Pin Lv, De-Ke Guo, Tong-Qing Zhou, Ming Xu.A Survey on Task and Participant Matching in Mobile Crowd Sensing[J]  Journal of Computer Science and Technology, 2018,V33(4): 768-791
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-018-1855-y
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