Computer Science ›› 2022, Vol. 49 ›› Issue (9): 275-282.doi: 10.11896/jsjkx.210700129

• Computer Network • Previous Articles     Next Articles

Incentive Mechanism Based on Multi-constrained Worker Selection in Mobile Crowdsourcing

FU Yan-ming1, ZHU Jie-fu1, JIANG Kan2, HUANG Bao-hua1, MENG Qing-wen1, ZHOU Xing1   

  1. 1 School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    2 School of Business Administration,Guangxi University,Nanning 530004,China
  • Received:2021-07-13 Revised:2021-09-06 Online:2022-09-15 Published:2022-09-09
  • About author:FU Yan-ming,born in 1976,Ph.D,associate professor.His main research in-terests include data Mining,computation intelligence and network security,etc.
    ZHU Jie-fu,born in 1997,postgraduate,is a member of China Computer Federation.His main research interests include mobile crowdsourcing and incentive mechanism.
  • Supported by:
    National Natural Science Foundation of China(71962002) and “Guangxi Development Strategy Research Institute” of Guangxi Universities Key Research Base of Humanities and Social Sciences(2021GDSIYB14).

Abstract: With the rapid development of mobile crowdsourcing,crowdsourcing programs in the market have sprung up.They distribute tasks and use the power of the crowd to perform the tasks for collecting data and an effective incentive mechanism in mobile crowdsourcing becomes very important.However,the existing incentive mechanisms nowadays partially consider the reputation value,location and execution time of workers,which makes it difficult for crowdsourcing platform to select high-quality workers and assign multiple tasks on limited budgets or other constraints.To solve the above problems,this paper proposes an incentive mechanism on the basis of the multi-constrained worker selection (MSIM),which relies on two related algorithms.One is the algorithm of worker selection based on improved reverse auction model,which comprehensively considers many important limitations to select great workers to perform the tasks,such as worker reputation,geographical location,task completion degree and result quality.The other is the algorithm of reward and punishment by evaluation,which contains the evaluation of task-perceiving results and workers' reputation.The experimental results showed that not only can MSIM select excellent workers,but also it improved the credibility of the task results and the reputation of workers.It is proved within this paper that the MSIM is an effective incentive mechanism.

Key words: Mobile crowdsourcing, Worker selection, Multiple constraints, Result evaluation, Incentive mechanism

CLC Number: 

  • TP391
[1]WU Y,ZENG J R,PENG H,et al.Survey on incentive mechanisms for crowd sensing[J].Journal of Software,2016,27(8):2025-2047.
[2]HU Y,WANG Y J,TONG X R.Task Recommendation ModelBased on Crowd Worker's Movement Trajectory[J].Computer Science,2020,47(10):32-40.
[3]WANG Y,CAI Z,TONG X,et al.Truthful incentive mechanismwith location privacy-preserving for mobile crowdsourcing systems [J].Computer Networks,2018,135:32-43.
[4]LI Z,CHENG B,GAO X,et al.A unified task recommendation strategy for realistic mobile crowdsourcing system[J].Theoretical Computer Science,2021,857(D):43-58.
[5]JIANG N,XU D,ZHOU J,et al.Toward optimal participant decisions with voting-based incentive model for crowd sensing[J].Information Sciences,2020,512:1-17.
[6]JIA X,ZHENG Q R,LI J X,et al.Incentive Mechanism for Multiple Cooperative Tasks with Compatible Users in Mobile Crowd Sensing via Online Communities[J].IEEE Transactions on Mobile Computing,2020,19(7):1618-1633.
[7]JIANG L Y,FAN H,HU W,et al.Quality-Aware IncentiveMechanism for Mobile Crowd Sensing[J].Journal of Sensors,2017(3):1-14.
[8]CAI H,ZHU Y,FENG Z,et al.Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones[J].Computer Networks,2018,141:1-16.
[9]CHEN X,MIN L,ZHOU Y,et al.A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System[J].Sensors (Basel,Switzerland),2017,17(1):1-17.
[10]LI X H,ZHU Q.Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing[J].Sensors,2018,18(1):1-21.
[11]YANG G,HE S B,SHI Z G,et al.Promoting Cooperation by the Social Incentive Mechanism in Mobile Crowdsensing[J].IEEE Communications Magazine,2017,55(3):86-92.
[12]AMINTOOSI H,KANHERE S S,TORSHIZ M N.A socially-aware incentive scheme for social participatory sensing[C]//2015 IEEE 10th International Conference on Intelligent Sensors,Sensor Networks and Information Processing,ISSNIP 2015.Institute of Electrical and Electronics Engineers Inc.,2015:1-6.
[13]LUO T,KANHERE S S,TAN H P.SEWing a Simple Endorsement Web to Incentivize Trustworthy Participatory Sensing[C]//Eleventh IEEE International Conference on Sensing.IEEE,2014:636-644.
[14]ROUGHGARDEN T.Stackelberg scheduling strategies[J].Siam Journal on Computing,2001,33(2):332-350.
[15]MYERSON R B.Optimal auciton design[J].Mathematics ofOperations Research,1981,6(1):58-73.
[16]YANG D,XUE G,XI F,et al.Crowdsourcing to smartphones:Incentive mechanism design for mobile phone sensing[C]//Proceedings of the Annual International Conference on Mobile Computing and Networking.MOBICOM,2012:173-184.
[17]EMILIANI M L,STEC D J.Online reverse auction purchasing contracts[J].Supply Chain Management,2001,6(3):101-105.
[18]AHMED A,PATEL A,BROWN T,et al.Task assignment for a physical agent team via a dynamic forward/reverse auction mechanism[C]//International Conference on Integration of Knowledge Intensive Multi-Agent Systems,2005.2005:311-317.
[19]LEE J S,HOH B.Sell your experiences:a market mechanism based incentive for participatory sensing[C]//2010 IEEE International Conference on Pervasive Computing and Communications(PerCom 2010).IEEE,2010:60-68.
[20]ZHANG X,XUE G,YU R,et al.Robust Incentive Tree Design for Mobile Crowdsensing[C]//2017 IEEE 37th International Conference on Distributed Computing Systems(ICDCS).IEEE,2017:458-468.
[21]YANG T,LI Z Q,YANG L X.Reputation-Updating Online Incentive Mechanism for Mobile Crowd Sensing [J].Journal of Data Acquisition and Processing,2019,34(5):797-807.
[22]JIN H,SU L,CHEN D,et al.Quality of information aware incentive mechanisms for mobile crowd sensing systems[C]//Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing(MobiHoc).Association for Computing Machinery,2015:167-176.
[23]HE S B,SHIN D H,ZHANG J S,et al.Near-Optimal Allocation Algorithms for Location-Dependent Tasks in Crowdsensing[J].IEEE Transactions on Vehicular Technology,2017,66(4):3392-3405.
[24]XIAO M J,WU J,HUANG H,et al.Deadline-sensitive UserRecruitment for mobile crowdsensing with probabilistic collaboration[C]//IEEE International Conference on Network Protocols.IEEE Computer Society,2016.
[25]ZHAO D,LI X Y,MA H.How to crowdsource tasks truthfully without sacrificing utility:Online incentive mechanisms with budget constraint[C]//IEEE INFOCOM 2014-IEEE Confe-rence on Computer Communications.Toronto,Canada:IEEE,2014:1213-1221.
[26]ZHAO D,LI X Y,MA H.Budgetfeasible online incentivemecha-nisms for crowdsourcing tasks truthfully[J].IEEE/ACM Transactions on Networking,2016,24(2):647-661.
[27]ZHOU P,ZHENG Y,LI M,How Long to Wait?Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing[J].Mobile Computing,2014,13(6):1228-1241.
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