黄越1,
赵国生2,
赵中楠1
1.哈尔滨理工大学计算机科学与技术学院 ??哈尔滨 ??150080
2.哈尔滨师范大学计算机科学与信息工程学院 ??哈尔滨 ??150025
基金项目:国家自然科学基金(61403109, 61202458)，高等学校博士学科点专项科研基金(20112303120007)，黑龙江省自然科学基金(F2017021)，黑龙江省教育厅科研基金(12541169)，哈尔滨市科技创新人才研究专项资金(2016RAQXJ036)
详细信息
作者简介:王健：女，1979年生，博士，副教授，硕士生导师，研究方向为认知网络、可信计算
黄越：女，1994年生，硕士生，研究方向为移动群智感知关键技术
赵国生：男，1977年生，博士，教授，硕士生导师，研究方向为网络可生存性、服务计算
赵中楠：男，1978年生，博士，讲师，研究方向为无线传感器网络
通讯作者:王健 wangjianlydia@163.com
中图分类号:TP393计量
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被引次数:0
出版历程
收稿日期:2018-07-02
修回日期:2018-12-18
网络出版日期:2019-01-02
刊出日期:2019-06-01
The Incentive Model for Mobile Crowd Sensing Oriented to Differences in Mission Costs
Jian WANG1,,,Yue HUANG1,
Guosheng ZHAO2,
Zhongnan ZHAO1
1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
2. School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Funds:The National Natural Science Foundation of China (61403109, 61202458), The Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007), The Heilongjiang Province Natural Science Foundation (F2017021), The Scientific Research Fund of Heilongjiang Province Educational Committee (12541169), The Specialized Research Fund for Scientific and Technological Innovation Talents of Harbin (2016RAQXJ036)
摘要
摘要:针对感知任务参与者数量不足和提供数据质量不高的问题，该文提出一种面向任务代价差异的移动群智感知激励模型。首先，利用模糊推理方法分析数据量、环境条件及设备消耗对任务代价的影响，将感知任务按照代价差异划分为不同等级，同时为请求者制定预算并给予参与者合适的报酬。其次，通过信誉度评估和参与者优选将感知任务分配给更合适的参与者完成感知任务并上传感知数据。最后，对参与者上传感知数据评估，更新参与者信誉度，并根据参与者完成感知任务的代价等级支付相应报酬。基于真实数据集的仿真实验结果表明，该模型能够利用各个模块间的相互影响，有效招募更多的用户参与感知任务并促进参与者上传高质量的感知数据。
关键词:移动群智感知/
激励机理/
任务代价差异/
信誉度
Abstract:To solve the problem of insufficient number of participants and poor data quality in the sensing mission, a mobile crowd sensing incentive model for mission cost difference is proposed. First of all, the fuzzy reasoning method is used to analyze the impact of data quantity, environmental conditions and equipment consumption on mission cost, and the sensing mission is divided into different levels on the basis of cost difference. Meanwhile, the method is used to prepare a budget for the requester and give the participant an appropriate reward. Then, the sensing mission is assigned to more appropriate participants to complete the sensing mission and upload the sensing data through credibility assessment and participants’ preference. Finally, the sensing data uploaded by participants is evaluated, and the credibility of participants is updated. Besides, the participants are paid according to the cost level of perceived missions. The simulation experiments based on the real data set show that the model can recruit more users to participate in the sensing mission effectively and promote participants to upload high-quality sensing data by using the mutual influence between different modules.
Key words:Mobile crowd sensing/
Incentive scheme/
Differences in mission costs/
Reputation
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