计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 275-282.doi: 10.11896/jsjkx.210700129

• 计算机网络 • 上一篇    下一篇


傅彦铭1, 朱杰夫1, 蒋侃2, 黄保华1, 孟庆文1, 周兴1   

  1. 1 广西大学计算机与电子信息学院 南宁 530004
    2 广西大学工商管理学院 南宁 530004
  • 收稿日期:2021-07-13 修回日期:2021-09-06 出版日期:2022-09-15 发布日期:2022-09-09
  • 通讯作者: 朱杰夫(zhujiefujeff@sina.com)
  • 作者简介:(fym2005@126.com)
  • 基金资助:

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).

摘要: 随着移动众包的快速发展,市面上的众包平台如雨后春笋般出现,它们发布任务并利用人群的力量来执行任务、收集数据。此时,移动众包中有效的激励机制变得十分重要。然而现有的激励机制只片面地考虑工人的信誉度、所在位置和执行时间等,这使得众包平台在有限的预算或其他约束的情况下选定优质工人并分配多个任务变得困难。针对以上问题,文中提出了一种基于多约束工人择优的激励机制(Multi-constrained Worker Selection Incentive Mechanism,MSIM),该模型依赖于两个相关算法:一是基于改进逆向拍卖的工人择优算法,该算法综合考虑工人信誉度、地理位置、任务完成度、结果质量等多个重要约束来选择最优的工人执行任务;二是评估和奖惩算法,该算法对任务执行结果和工人信誉度进行评估,从而制定对工人的奖励与惩罚规则。实验结果表明,MSIM可以选出优秀的工人,并提高任务执行结果的可信度和工人信誉度,是一种良好的激励机制。

关键词: 移动众包, 工人选择, 多约束, 结果评估, 激励机制

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


  • TP391
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