群智感知中移动用户招募的防贪婪激励机制研究
作者:
作者单位:

湖南工商大学

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目),湖南省研究生科研创新项目,湖南省自然科学基金面上项目,湖南省社会科学基金重点项目,湖南省社会科学成果 评审委员会课题重点项目


Research on Anti-Greedy Incentive Mechanism for Mobile User Recruitment in Group Intelligence
Author:
Affiliation:

Hunan University Of Technology and Business

Fund Project:

the National Natural Science Foundation of China,Postgraduate Scientific Research Innovation Project of Hunan Province,Natural Science Foundation of Hunan Province

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    摘要:

    随着群智感知的普及,以合理的成本招募最佳感知信息提供者的问题也变得更加重要,但移动用户追求高回报的贪婪特性会使得招募成本偏高.为此,提出了一种针对团体的群智感知招募的激励机制, 首先通过移动用户的属性和任务详细信息,来迭代所有可能团体;然后,评估生成的随机初始团体,在其中删除违反任务约束的团体,并计算其余团体的QoI(the Quality of Information,QoI)比率,团体将经过轮盘赌程序从当前团体中选择候选人进行进化程序,选定的团体经过交叉,在团体之间随机交换成员;最后进行突变,该过程随机替换团体的成员,从解决方案集中选择具有最佳QoI比率的团体.解决了移动用户对数据进行过高定价以提高利润的倾向.提出的激励机制包括选择和支付机制,避免了移动用户选择过程中的贪婪特性.通过使其与现有的团队招募框架方法的对比,以及实验数据集与原始模型进行的比较,证明了该激励机制的有效性.

    Abstract:

    With the popularity of Crowd Sensing, the problem of recruiting the best perceptual information providers at a reasonable cost has become more important, but the greedy nature of mobile users pursuing high returns will make recruitment costs high. For this reason, a method is proposed. The incentive mechanism for group intelligence-aware recruitment is to iterate all possible groups through the attributes and task details of the mobile user; then, evaluate the generated random initial group, delete the group that violates the task constraint, and calculate the remaining groups The QoI (the Quality of Information, QoI) ratio, the group will go through the roulette process to select candidates from the current group for the evolution process, the selected group will be crossed, and members will be randomly exchanged between the groups; finally, the mutation The process randomly replaces the members of the group and selects the group with the best QoI ratio from the solution set. It solves the tendency of mobile users to overprice data to increase profits. The proposed incentive mechanism includes selection and payment mechanisms, which avoids mobile users The greedy nature of the selection process. By comparing it with the existing team recruitment framework methods, and comparing the experimental data set with the original model, the effectiveness of the incentive mechanism is proved.

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历史
  • 收稿日期:2020-06-12
  • 最后修改日期:2021-10-09
  • 录用日期:2020-08-28
  • 在线发布日期: 2020-10-02
  • 出版日期: