基于拍卖模型的移动群智感知网络激励机制

刘媛妮; 李垚焬; 李慧聪; 李万林; 张建辉; 赵国锋

doi:10.11959/j.issn.1000-436x.2019138

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基于拍卖模型的移动群智感知网络激励机制

学术通信 | 更新时间：2024-06-05

- 基于拍卖模型的移动群智感知网络激励机制
- Incentive mechanism based on auction model for mobile crowd sensing network
- 通信学报 2019年40卷第7期页码：208-222
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆 400065
  2. 国家数字交换系统工程技术研究中心，河南郑州 450002
  3. 重庆市高校光通信与网络重点实验室，重庆 400065
- 作者简介：
  
  [ "刘媛妮（1982- ），女，河南邓州人，博士，重庆邮电大学副教授、硕士生导师，主要研究方向为移动群智感知、物联网安全、IP路由技术和复杂网络。" ]
  [ "李垚焬（1995- ），男，重庆人，重庆邮电大学硕士生，主要研究方向为移动群智感知、物联网安全、IP路由技术和复杂网络。" ]
  [ "李慧聪（1993- ），女，湖北松滋人，重庆邮电大学硕士生，主要研究方向为移动群智感知、物联网安全、IP路由技术和复杂网络。" ]
  [ "李万林（1963- ），男，四川广安人，博士，重庆邮电大学教授、博士生导师，主要研究方向为移动大数据信息处理。" ]
  [ "张建辉（1977- ），男，河南平顶山人，博士，国家数字交换系统工程技术研究中心副教授，主要研究方向为路由和交换设计、路由协议、资源调度、网络安全和未来网络。" ]
  [ "赵国锋（1972- ），男，陕西泾阳人，博士，重庆邮电大学教授，主要研究方向为未来互联网、移动互联网、网络管理和网络安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501075;61701058);国家留学基金委项目(201707845004);“十三五”装备预研国防科技重点实验室基金资助项目(61422090301);重庆市教委科学技术研究基金资助项目(KJQN201800633);重庆市基础与前沿基金资助项目(2015jcyjBX0009)
- DOI：10.11959/j.issn.1000-436x.2019138
  中图分类号： TP393
- 网络出版日期：2019-07，
  
  纸质出版日期：2019-07-25
- 稿件说明：
移动端阅览
刘媛妮, 李垚焬, 李慧聪, 等. 基于拍卖模型的移动群智感知网络激励机制[J]. 通信学报, 2019,40(7):208-222.

Yuanni LIU, Yaoxi LI, Huicong LI, et al. Incentive mechanism based on auction model for mobile crowd sensing network[J]. Journal on communications, 2019, 40(7): 208-222.
刘媛妮, 李垚焬, 李慧聪, 等. 基于拍卖模型的移动群智感知网络激励机制[J]. 通信学报, 2019,40(7):208-222. DOI： 10.11959/j.issn.1000-436x.2019138.

Yuanni LIU, Yaoxi LI, Huicong LI, et al. Incentive mechanism based on auction model for mobile crowd sensing network[J]. Journal on communications, 2019, 40(7): 208-222. DOI： 10.11959/j.issn.1000-436x.2019138.

摘要

移动群智感知网络中用户的自私性和不确定性会造成用户参与感知活动的积极性不高及任务完成率较低等问题。针对此问题，提出了一种基于拍卖模型的激励机制。首先，以最大化用户效用为目标，在所提出的逆向拍卖机制（IMRA）中，以任务为中心进行赢标者选择且基于临界价格对赢标者进行报酬支付。然后，利用双向交互的激励机制（UBIM）使临时退出的用户可将未完成的任务转售给新用户，并提出基于二部图的用户匹配算法。实验结果表明，与TRAC、IMC-SS机制相比，所提的IMRA具有更高的用户平均效用和任务覆盖率，使用UBIM后也提高了任务完成率。

Abstract

The selfishness and uncertainty of user behaviors in the mobile crowd sensing network make them unwilling to participate in sensing activities

which may result to a lower sensing task completion rate.To deal with these problems

an incentive mechanism based on auction model was proposed.In order to maximize the utility of each user

the proposed incentive method based on reverse auction (IMRA) leveraged a task-centric method to choose winners

and payed them according to a critical-price strategy.Furthermore

the proposed user-bidirectional interaction incentive mechanism (UBIM) helped drop-out users (buyers) to transfer their unfinished tasks to new users.Simulation results show that

compared with TRAC and IMC-SS

IMRA can achieve a better performance in terms of average user utility and tasks coverage ratio

and the task completion ratio can also be improved by UBIM.

关键词

Keywords

references

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