Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (3): 753-757.DOI: 10.11772/j.issn.1001-9081.2017082049

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Participant reputation evaluation scheme in crowd sensing

WANG Taochun1,2, LIU Tingting1, LIU Shen1, HE Guodong2   

  1. 1. School of Mathematics and Computer Science, Anhui Normal University, Wuhu Anhui 241002, China;
    2. Anhui Provincial Key Laboratory of Network and Information Security(Anhui Normal University), Wuhu Anhui 241002, China
  • Received:2017-08-29 Revised:2017-11-01 Online:2018-03-10 Published:2018-03-07
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61402014).

群智感知中的参与者信誉评估方案

王涛春1,2, 刘婷婷1, 刘申1, 何国栋2   

  1. 1. 安徽师范大学 数学计算机科学学院 安徽 芜湖 241002;
    2. 网络与信息安全安徽省重点实验室(安徽师范大学), 安徽 芜湖 241002
  • 通讯作者: 王涛春
  • 作者简介:王涛春(1979-),男,安徽无为人,副教授,博士,CCF会员,主要研究方向:隐私保护、无线传感器网络;刘婷婷(1996-),女,安徽宿州人,主要研究方向:群智感知;刘申(1996-),男,安徽六安人,主要研究方向:无线传感器网络;何国栋(1980-),男,安徽怀宁人,副教授,博士,主要研究方向:信号处理。
  • 基金资助:
    国家自然科学基金资助项目(61402014)。

Abstract: For a Mobile Crowd Sensing (MCS) network has a large group of participants, and the acquisition and submission of tasks are almost unrestricted, so that data redundancy is high and data quality cannot be guranteed. To solve the problem, a method called Participant Reputation Evaluation Scheme (PRES) was proposed to evaluate the data quality and the reputation of participants. A participant's reputation was evaluated from five aspects:response time, distance, historical reputation, data correlation and quality of submitted data. The five parameters were quantified, and a regression equation was established by using logistic regression model to get the participant reputation after submitting data. The reputation credibility of a participant was in the interval[0.0, 1.0], and concentrated in[0.0,0.2] and[0.8, 1.0], making it easier for the group of mental perception network to choose appropriate participants, and the accuracy of the evaluation results by the crowd sensing showed that PRES was more than 90%.

Key words: crowd sensing, reputation evaluation, logistic regression, participant selection, data quality

摘要: 针对群智感知网络中参与者群体大,且获取和提交任务几乎不受限制,使得群智感知网络存在数据冗余度高和数据质量不能得到保证的问题,提出了针对参与者提交数据质量和可信度的信誉评估方案——参与者信誉评估方案(PRES)。从参与者提交数据的响应时间、距离、历史信誉度、数据相关性和数据质量五个方面对参与者信誉进行评估,将这五个参数数值化,并利用逻辑回归模型建立回归方程,得出参与者本次提交数据后的信誉度。PRES得出的参与者信誉度在[0.0,1.0]范围内,且集中分布于[0.0,0.2]和[0.8,1.0]区间,使得群智感知网络容易选择合适的参与者,且评估结果表明PRES评估的准确率均在90%以上。

关键词: 群智感知, 信誉评估, 逻辑回归, 参与者选择, 数据质量

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