? 移动群智感知中任务与参与者匹配技术综述
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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 << Previous Articles | Next Articles >>
移动群智感知中任务与参与者匹配技术综述
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
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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摘要 移动群智感知是一种利用大量参与者及其移动设备感知物理世界多种信息的新型方式。根据这些感知信息,很多任务能够以高效的方式完成,例如环境监测、交通预测、室内定位等等。感知任务与参与者的匹配是移动群智感知中的重要问题,决定了移动群智感知任务的完成质量和效率。因此,近年来的研究工作提出了很多匹配策略。本文旨在对这一研究问题提供最新的研究综述。本文对任务与参与者匹配问题提出了一个研究框架,包括参与者模型、任务模型、以及方案设计。参与者模型包括三种参与者特征,分别是属性、需求和附加条件。任务模型根据应用背景和目标函数进行了区分。文中还讨论了相关文献中所提出的离线匹配方案和在线匹配方案。本文最后对这一领域的开放性问题进行了介绍,包括异构任务的匹配策略、上下文感知的匹配、在线匹配方案、利用历史数据完成新的任务等等。
关键词移动群智感知   任务分配   参与者选择   任务与参与者匹配     
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.
Keywordsmobile crowd sensing   participant selection   task allocation   task and participant matching     
Received 2017-08-24;
本文基金:

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.

通讯作者: 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.
引用本文:   
Yue-Yue Chen, Pin Lv, De-Ke Guo, Tong-Qing Zhou, Ming Xu.移动群智感知中任务与参与者匹配技术综述[J]  Journal of Computer Science and Technology , 2018,V33(4): 768-791
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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