AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Survey

A Survey on Task and Participant Matching in Mobile Crowd Sensing

College of Computer, National University of Defense Technology, Changsha 410073, China
School of Computer Electronics and Information, Guangxi University, Nanning 530004, China
Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University, Nanning 530004, China
College of System Engineering, National University of Defense Technology, Changsha 410073, China
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China
Show Author Information

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.

Keywords

mobile crowd sensingparticipant selectiontask allocationtask and participant matching

Electronic Supplementary Material

Download File(s)
jcst-33-4-768-Highlights.pdf (187.6 KB)

References

[1]

Guo B, Wang Z, Yu Z W, Wang Y, Yen N Y, Huang R H, Zhou X S. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys, 2015, 48(1): 1-31.
Crossref Google Scholar
[2]

Guo B, Chen C, Zhang D Q, Yu Z W, Chin A. Mobile crowd sensing and computing: When participatory sensing meets participatory social media. IEEE Communications Magazine, 2016, 54(2): 131-137.
Crossref Google Scholar
[3]
Cherian J, Luo J, Guo H L, Ho S S, Wisbrun R. Park-gauge: Gauging the occupancy of parking garages with crowdsensed parking characteristics. In Proc. the 17th IEEE Int. Conf. Mobile Data Management (MDM), July 2016, pp.92-101.
Crossref
[4]

Guo B, Chen H H, Yu Z W, Xie X, Huangfu S L, Zhang D Q. FlierMeet: A mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans. Mobile Computing, 2015, 14(10): 2020-2033.
Crossref Google Scholar
[5]
Koukoumidis E, Peh L S, Martonosi M R. SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In Proc. the 9th ACM Annu. Conf. Mobile Systems, Applications, and Services (MobiSys), June 2011, pp.127-140.
Crossref
[6]
Morishita S, Maenaka S, Nagata D, Tamai M, Yasumoto K, Fukukura T, Sato K. SakuraSensor: Quasi-realtime cherry-lined roads detection through participatory video sensing by cars. In Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2015, pp.695-705.
Crossref
[7]
Ludwig T, Reuter C, Pipek V. What you see is what I need: Mobile reporting practices in emergencies. In Proc. the 13th European Conf. Computer Supported Cooperative Work (ECSCW), September 2013, pp.181-206.
Crossref
[8]

Lane D D, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A T. A survey of mobile phone sensing. IEEE Communications Magazine, 2010, 48(9): 140-150.
Crossref Google Scholar
[9]

Gao H, Liu C H, Wang W D, Zhao J X, Song S, Su X, Crowcroft J, Leung K. A survey of incentive mechanisms for participatory sensing. IEEE Communications Surveys & Tutorials, 2015, 17(2): 918-943.
Crossref Google Scholar
[10]

Jaimes L G, Vergara-Laurens I J, Raij A. A survey of incentive techniques for mobile crowd sensing. IEEE Internet of Things Journal, 2015, 2(5): 370-380.
Crossref Google Scholar
[11]

Zhang X L, Yang Z, Sun W, Liu Y H, Tang S H, Xing K, Mao X F. Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials, 2016, 18(1): 54-67.
Crossref Google Scholar
[12]

Guo B, Chen H H, Yu Z W, Nan W Q, Xie X, Zhang D Q, Zhou X S. TaskMe: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. International Journal of Human Computer Studies, 2017, 102: 14-26.
Crossref Google Scholar
[13]

Rafael P T, César T H, Hiram G Z. Power management techniques in smartphone-based mobility sensing systems: A survey. Pervasive and Mobile Computing, 2016, 31: 1-21.
Crossref Google Scholar
[14]
Liaqat D, Jingoi S, Lara E D, Goel A, To W, Lee K, Garcia I D, Saldana M. Sidewinder: An energy efficient and developer friendly heterogeneous architecture for continuous mobile sensing. In Proc. the 21st Annu. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), April 2016, pp.205-215.
Crossref
[15]
Guo X N, Chan E, Liu C, Wu K S, Liu S Y, Ni L M. ShopProfiler: Profiling shops with crowdsourcing data. In Proc. the 33th IEEE Annu. Conf. Computer Communications (INFOCOM), April 2014, pp.1240-1248.
Crossref
[16]

Zhou P F, Zheng Y Q, Li M. How long to wait? Predicting bus arrival time with mobile phone based participatory sensing. IEEE Trans. Mobile Computing, 2014, 13(6): 1228-1241.
Crossref Google Scholar
[17]

Pournajaf L, Garcia-Ulloa D A, Xiong L, Sunderam V. Participant privacy in mobile crowd sensing task management: A survey of methods and challenges. ACM SIGMOD Record, 2016, 44(4): 23-34.
Crossref Google Scholar
[18]

Christin D. Privacy in mobile participatory sensing: Current trends and future challenges. Journal of Systems and Software, 2016, 116: 57-68.
Crossref Google Scholar
[19]

Yang K, Zhang K, Ren J, Shen X. Security and privacy in mobile crowdsourcing networks: Challenges and opportunities. IEEE Communications Magazine, 2015, 53(8): 75-81.
Crossref Google Scholar
[20]
Huang K L, Kanhere S S, Hu W. Are you contributing trustworthy data? The case for a reputation system in participatory sensing. In Proc. the 13th ACM Annu. Conf. Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM), October 2010, pp.14-22.
Crossref
[21]
Truskinger A, Yang H F, Wimmer J, Zhang J L, Williamson I, Roe P. Large scale participatory acoustic sensor data analysis: Tools and reputation models to enhance effectiveness. In Proc. the 7th IEEE Annu. Conf. eScience, December 2011, pp.150-157.
Crossref
[22]

Christin D, Roßkopf C, Hollick M, Martucci L A, Kanhere S S. IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications. Pervasive and Mobile Computing, 2013, 9(3): 353-371.
Crossref Google Scholar
[23]

Ren J, Zhang Y X, Zhang K, Shen X M. SACRM: Social aware crowdsourcing with reputation management in mobile sensing. Computer Communications, 2015, 65: 55-65.
Crossref Google Scholar
[24]

Mousa H, Mokhtar S B, Hasan O, Younes O, Hadhoud M, Brunie L. Trust management and reputation systems in mobile participatory sensing applications: A survey. Computer Networks, 2015, 90: 49-73.
Crossref Google Scholar
[25]

Zhao D, Ma H D, Tang S J, Li X Y. COUPON: A cooperative framework for building sensing maps in mobile opportunistic networks. IEEE Trans. Parallel and Distributed Systems (TPDS), 2015, 26(2): 392-402.
Crossref Google Scholar
[26]
He Z J, Cao J N, Liu X F. High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility. In Proc. the 34th IEEE Annu. Conf. Computer Communications (INFOCOM), April 2015, pp.2542-2550.
Crossref
[27]
Cho E, Myers S A, Leskovec J. Friendship and mobility: User movement in location-based social networks. In Proc. the 17th ACM Annu. Conf. Knowledge Discovery and Data Mining (SIGKDD), August 2011, pp.1082-1090.
Crossref
[28]
Gao Y, Dong W, Guo K, Liu X, Chen Y, Liu X J, Bu J J, Chen C. Mosaic: A low-cost mobile sensing system for urban air quality monitoring. In Proc. the 35th Annual IEEE Annu. Conf. Computer Communications (INFOCOM), April 2016.
Crossref
[29]

Chen Y Y, Lv P, Guo D K, Zhou T Q, Xu M. Trajectory segment selection with limited budget in mobile crowd sensing. Pervasive and Mobile Computing, 2017, 40(9): 123-138.
Crossref Google Scholar
[30]
Boutsis I, Kalogeraki V. Mobile stream sampling under time constraints. In Proc. the 14th IEEE Annu. Conf. Mobile Data Management (MDM), June 2013, pp.227-236.
Crossref
[31]
Hamid S A, Takahara G, Hassanein H S. On the recruitment of smart vehicles for urban sensing. In Proc. the 2013 IEEE Global Telecommunications Conference (GLOBECOM), December 2013, pp.36-41.
Crossref
[32]

Zheng Y. Trajectory data mining: An overview. ACM Trans. Intelligent Systems and Technology (TIST), 2015, 6(3): 1-41.
Crossref Google Scholar
[33]
Pournajaf L, Xiong L, Sunderam V, Goryczka S. Spatial task assignment for crowd sensing with cloaked locations. In Proc. the 15th IEEE Annu. Conf. Mobile Data Management (MDM), July 2014, pp.73-82.
Crossref
[34]
Wang L Y, Zhang D Q, Yang D Q, Lim B Y, Ma X J. Differential location privacy for sparse mobile crowdsensing. In Proc. the 16th IEEE Annu. Conf. Data Mining (ICDM), December 2016, pp.1257-1262.
Crossref
[35]

Wang L Y, Zhang D Q, Wang Y S, Chen C, Han X, M’Hamed A. Sparse mobile crowdsensing: Challenges and opportunities. IEEE Communications Magazine, 2016, 54(7): 161-167.
Crossref Google Scholar
[36]
Xiao M J, Wu J, Zhang S, Yu J P. Secret-sharing-based secure user recruitment protocol for mobile crowdsensing. In Proc. the 36th IEEE Annu. Conf. Computer Communications (INFOCOM), April 2017.
Crossref
[37]
Celis L E, Reddy S P, Singh I P, Vaya S. Assignment techniques for crowdsourcing sensitive tasks. In Proc. the 19th ACM Conf. Computer-Supported Cooperative Work and Social Computing (CSCW), February 2016, pp.836-847.
Crossref
[38]
Cheung M H, Southwell R, Hou F, Huang J W. Distributed time-sensitive task selection in mobile crowdsensing. In Proc. the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), June 2015, pp.157-166.
Crossref
[39]
Zhang L Y, Hu T, Min Y, Wu G B, Zhang J Y, Feng P C, Gong P H, Ye J P. A taxi order dispatch model based on combinatorial optimization. In Proc. the 23rd ACM Annu. Conf. Knowledge Discovery and Data Mining (SIGKDD), August 2017, pp.2151-2159.
Crossref
[40]

Cardone G, Foschini L, Bellavista P, Corradi A, Borcea C, Talasila M, Curtmola R. Fostering participaction in smart cities: A geo-social crowdsensing platform. IEEE Communications Magazine, 2013, 51(6): 112-119.
Crossref Google Scholar
[41]
Pu L J, Chen X, Xu J D, Fu X M. Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing. In Proc. the 35th Annual IEEE Annu. Conf. Computer Communications (INFOCOM), April 2016.
Crossref
[42]
Mavridis P, Gross-Amblard D, Miklós Z. Using hierarchical skills for optimized task assignment in knowledge-intensive crowdsourcing. In Proc. the 25th Annu. Conf. World Wide Web (WWW), April 2016, pp.843-853.
Crossref
[43]
Zhang X M, Wu Y B, Huang L F, Ji H, Cao G H. Expertiseaware truth analysis and task allocation in mobile crowdsourcing. In Proc. the 37th IEEE Annu. Conf. Distributed Computing Systems (ICDCS), June 2017, pp.922-932.
Crossref
[44]
Amintoosi H, Kanhere S S. A trust-based recruitment framework for multi-hop social participatory sensing. In Proc. the 2013 IEEE Annu. Conf. Distributed Computing in Sensor Systems (DCoSS), May 2013, pp.266-273.
Crossref
[45]
Chang W, Wu J. Reliability enhanced social crowdsourcing. In Proc. the 2015 IEEE Global Communications Conf. (GLOBECOM), December 2015.
Crossref
[46]

Nath S. ACE: Exploiting correlation for energy-efficient and continuous context sensing. IEEE Trans. Mobile Computing, 2013, 12 (8): 1472-1486.
Crossref Google Scholar
[47]
Reddy S, Shilton K, Burke J, Estrin D, Hansen M, Srivastava M. Using context annotated mobility profiles to recruit data collectors in participatory sensing. In Proc. Int. Sym. Location- and Context-Awareness (LoCA), May 2009, pp.52-69.
Crossref
[48]

Zhang H L, Xu Z K, Du X J, Zhou Z G, Shi J T. CAPR: Context-aware participant recruitment mechanism in mobile crowdsourcing. Wireless Communications and Mobile Computing, 2016, 16(15): 2179-2193.
Crossref Google Scholar
[49]
Liu S Z, Zheng Z Z, Wu F, Tang S J, Chen G H. Context-aware data quality estimation in mobile crowdsensing. In Proc. the 36th IEEE Annu. Conf. Computer Communications (INFOCOM), April 2017, pp.802-810.
Crossref
[50]

Xia S H, Gao L, Lai Y K, Yuan M Z, Chai J X. A survey on human performance capture and animation. Journal of Computer Science and Technology (JCST), 2017, 32(3): 536-554.
Crossref Google Scholar
[51]
Tamilin A, Carreras I, Ssebaggala E, Opira A, Conci N. Context-aware mobile crowdsourcing. In Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2012, pp.717-720.
Crossref
[52]
Ji S G, Zheng Y, Li T R. Urban sensing based on human mobility. In Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2016, pp.1040-1051.
Crossref
[53]
Jaimes L G, Vergara-Laurens I, Labrador M A. A location-based incentive mechanism for participatory sensing systems with budget constraints. In Proc. IEEE Annu. Conf. Pervasive Computing and Communications (PerCom), March 2012, pp.103-108.
Crossref
[54]
Mendez D, Labrador M A. Density maps: Determining where to sample in participatory sensing systems. In Proc. the 3rd FTRA Annu. Conf. Mobile, Ubiquitous, and Intelligent Computing (MUSIC), June 2012, pp.35-40.
Crossref
[55]

Song Z, Liu C H, Wu J, Ma J, Wang W D. QoI-aware multitask-oriented dynamic participant selection with budget constraints. IEEE Trans. Vehicular Technology, 2014, 63(9): 4618-4632.
Crossref Google Scholar
[56]

Liu L, Wei W Y, Zhao D, Ma H D. Urban resolution: New metric for measuring the quality of urban sensing. IEEE Trans. Mobile Computing, 2015, 14(12): 2560-2575.
Crossref Google Scholar
[57]

Kang X, Liu L, Ma H D. Enhance the quality of crowdsensing for fine-grained urban environment monitoring via data correlation. Sensors, 2017, 17(1): Article No. 88.
Crossref Google Scholar
[58]

Zhang Y, Roughan M, Willinger W, Qiu L L. Spatiotemporal compressive sensing and Internet traffic matrices. Network, 2009, 20(3): 267-278.
Crossref Google Scholar
[59]
Wu Y B, Wang Y, Cao G H. Photo crowdsourcing for area coverage in resource constrained environments. In Proc. the 36th IEEE Annu. Conf. Computer Communications (INFOCOM), April 2017.
Crossref
[60]
Wang L Y, Zhang D Q, Pathak A, Chen C, Xiong H Y, Yang D Q, Wang Y S. CCS-TA: Quality-guaranteed online task allocation in compressive crowdsensing. In Proc. ACM International Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2015, pp.683-694.
Crossref
[61]
Liu Y, Guo B, Wang Y, Wu W L, Yu Z W, Zhang D Q. TaskMe: Multi-task allocation in mobile crowd sensing. In Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2016, pp.403-414.
Crossref
[62]

Guo B, Liu Y, Wu W L, Yu Z W, Han Q. ActiveCrowd: A framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Trans. Human-Machine Systems, 2017, 47(3): 392-403.
Crossref Google Scholar
[63]
Kang Y R, Miao X, Liu K B, Chen L, Liu Y H. Quality-aware online task assignment in mobile crowdsourcing. In Proc. the 12th IEEE Annu. Conf. Mobile Ad Hoc and Sensor Systems (MASS), October 2015, pp.127-135.
[64]
Kazemi L, Shahabi C. GeoCrowd: Enabling query answering with spatial crowdsourcing. In Proc. the 20th Annu. Conf. Advances in Geographic Information Systems, November 2012, pp.189-198.
Crossref
[65]
Lane N D, Chon Y H, Zhou L, Zhang Y Z, Li F, Kim D W, Ding G Z, Zhao F, Cha H J. Piggyback crowdsensing (PCS): Energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities. In Proc. the 11th ACM Conf. Embedded Networked Sensor Systems (SenSys), November 2013, pp.7: 1-7: 14.
Crossref
[66]
Zhang D Q, Xiong H Y, Wang L Y, Chen G L. CrowdRecruiter: Selecting participants for piggyback crowdsensing under probabilistic coverage constraint. In Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing (UbiComp), September 2014, pp.703-714.
Crossref
[67]
Xiong H Y, Zhang D Q, Chen G L, Wang L Y, Gauthier V. CrowdTasker: Maximizing coverage quality in piggyback crowdsensing under budget constraint. In Proc. IEEE Annu. Conf. Pervasive Computing and Communications (PerCom), March 2015, pp.55-62.
Crossref
[68]

Xiong H Y, Zhang D Q, Chen G L, Wang L Y, Gauthier V, Barnes L E. ICrowd: Near-optimal task allocation for piggyback crowdsensing. IEEE Trans. Mobile Computing, 2016, 15(8): 2010-2022.
Crossref Google Scholar
[69]

Xiong H Y, Zhang D Q, Wang L Y, Chaouchi H. EMC3: Energy-efficient data transfer in mobile crowdsensing under full coverage constraint. IEEE Trans. Mobile Computing, 2015, 14(7): 1355-1368.
Crossref Google Scholar
[70]
Wang J T, Wang Y S, Zhang D Q, Wang F, He Y D, Ma L T. PSAllocator: Multi-task allocation for participatory sensing with sensing capability constraints. In Proc. ACM Conf. Computer Supported Cooperative Work and Social Computing (CSCW), February 2017, pp.1139-1151.
Crossref
[71]
Li H S, Li T, Wang Y. Dynamic participant recruitment of mobile crowd sensing for heterogeneous sensing tasks. In Proc. the 12th IEEE Annu. Conf. Mobile Ad Hoc and Sensor Systems (MASS), October 2015, pp.136-144.
Crossref
[72]
Li H S, Li T, Li F, Wang W C, Wang Y. Enhancing participant selection through caching in mobile crowd sensing. In Proc. the 24th IEEE/ACM International Symposium on Quality of Service (IWQoS), June 2016.
Crossref
[73]
Chen C, Cheng S F, Misra A, Lau H C. Multi-agent task assignment for mobile crowdsourcing under trajectory uncertainties. In Proc. Annu. Conf. Autonomous Agents and Multi-agent Systems, May 2015, pp.1715-1716.
[74]
Xiao M J, Wu J, Huang H, Huang L S, Hu C. Deadline-sensitive user recruitment for probabilistically collaborative mobile crowdsensing. In Proc. the Annu. Conf. Distributed Computing Systems (ICDCS), August 2016, pp.721-722.
Crossref
[75]
Tong Y X, She J Y, Ding B L, Wang L B, Chen L. Online mobile micro-task allocation in spatial crowdsourcing. In Proc. the 32nd IEEE Annu. Conf. Data Engineering (ICDE), May 2016, pp.49-60.
Crossref
[76]

Lee D H, Wang H, Cheu R, Teo S. A taxi dispatch system based on current demands and real-time traffic conditions. Journal of the Transportation Research Board, 2004, 1882(1): 193-200.
Crossref Google Scholar
[77]

Yu Z Y, Zhang D Q, Yu Z W, Yang D Q. Participant selection for offline event marketing leveraging location-based social networks. IEEE Trans. Systems, Man, and Cybernetics: Systems, 2015, 45(6): 853-864.
Crossref Google Scholar
[78]
Cheng P, Lian X, Chen L, Shahabi C. Prediction-based task assignment in spatial crowdsourcing. In Proc. the 33rd IEEE Annu. Conf. Data Engineering (ICDE), April 2017, pp.997-1008.
Crossref
[79]

Zhang X L, Yang Z, Gong Y J, Liu Y H, Tang S H. Spatial-Recruiter: Maximizing sensing coverage in selecting workers for spatial crowdsourcing. IEEE Trans. Vehicular Technology, 2017, 66(6): 5229-5240.
Crossref Google Scholar
[80]

Guo B, Han Q, Chen H H, Shangguan L F, Zhou Z M, Yu Z W. The emergence of visual crowdsensing: Challenges and opportunities. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2526-2543.
Crossref Google Scholar
[81]

Guo B, Chen H H, Han Q, Yu Z W, Zhang D Q, Wang Y. Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans. Mobile Computing, 2017, 16(8): 2379-2391.
Crossref Google Scholar
[82]

Chen H H, Guo B, Yu Z W, Chen L M, Ma X J. A generic framework for constraint-driven data selection in mobile crowd photographing. IEEE Internet of Things Journal, 2017, 4(1): 284-296.
Crossref Google Scholar
[83]

Zhang X L, Yang Z, Liu Y H, Tang S H. On reliable task assignment for spatial crowdsourcing. IEEE Trans. Emerging Topics in Computing (Early Access), 2017. DOI: https://doi.org/10.1109/TETC.2016.2614383.
Crossref Google Scholar
[84]
Hsieh H P, Lin S D, Zheng Y. Inferring air quality for station location recommendation based on urban big data. In Proc. the 21st ACM Annu. Conf. Knowledge Discovery and Data Mining (SIGKDD), August 2015, pp.437-446.
Crossref
[85]

Zhao D, Li X Y, Ma H D. Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully. IEEE/ACM Trans. Networking, 2016, 24(2): 647-661.
Crossref Google Scholar
[86]
Hu H Q, Zheng Y D, Bao Z F, Li G L, Feng J H, Cheng R. Crowdsourced POI labelling: Location-aware result inference and task assignment. In Proc. the 32nd IEEE Annu. Conf. Data Engineering (ICDE), May 2016, pp.61-72.
Crossref
[87]

Zhang X L, Yang Z, Zhou Z M, Cai H B, Chen L, Li X Y. Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE Trans. Parallel and Distributed Systems (TPDS), 2014, 25(12): 3190-3200.
Crossref Google Scholar
[88]

Zhang X L, Yang Z, Liu Y H, Li J Q, Ming Z. Toward efficient mechanisms for mobile crowdsensing. IEEE Trans. Vehicular Technology, 2017, 66(2): 1760-1771.
Crossref Google Scholar
[89]
Han K, Zhang C, Luo J. BLISS: Budget limited robust crowdsensing through online learning. In Proc. the 11th Annual IEEE Annu. Conf. Sensing, Communication, and Networking (SECON), June 2014, pp.555-563.
Crossref
[90]

Han K, Zhang C, Luo J, Hu M L, Veeravalli B. Truthful scheduling mechanisms for powering mobile crowdsensing. IEEE Trans. Computers, 2016, 65(1): 294-307.
Crossref Google Scholar
[91]
She J Y, Tong Y X, Chen L, Song T S. Feedback-aware social event-participant arrangement. In Proc. ACM Annu. Conf. Management of Data (SIGMOD), May 2017, pp.851-865.
Crossref
[92]
Song T S, Tong Y X, Wang L B, She J Y, Yao B, Chen L, Xu K. Trichromatic online matching in real-time spatial crowdsourcing. In Proc. the 33rd IEEE Annu. Conf. Data Engineering (ICDE), April 2017, pp.1009-1020.
Crossref
Journal of Computer Science and Technology
Volume 33 Issue 4,
July 2018
Pages 768-791
DOI: 10.1007/s11390-018-1855-y
Cite this article:
Chen Y-Y, Lv P, Guo D-K, et al. A Survey on Task and Participant Matching in Mobile Crowd Sensing. Journal of Computer Science and Technology, 2018, 33(4): 768-791. https://doi.org/10.1007/s11390-018-1855-y

439

Views

25

Crossref

N/A

Web of Science

30

Scopus

4

CSCD

Altmetrics
Received: 24 August 2017
Revised: 12 May 2018
Published: 13 July 2018
©2018 LLC & Science Press, China
Return