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Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

机译:考虑用户信任倾向的移动人群感知系统中的数据可信度评估

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摘要

Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users’ trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang’s method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.
机译:移动人群感应是一种强大的范例,它利用智能手机的先进感应功能和普遍性来收集和分析固定传感器网络无法实现的规模的数据。移动式人群感知系统融合了人们,并依靠他们的参与和意愿来提供最新和准确的信息,这意味着此类系统容易产生恶意和错误的数据。因此,信任和声誉是确保移动人群感知系统可持续性所需要解决的关键因素。这项工作的目的是定义一个概念性信任框架,该框架考虑人类参与移动人群感应系统,并考虑到用户除了贡献其智能设备生成的原始感应数据外,还贡献自己的意见和其他主观数据。我们提出了一种新的方法来评估用户提供的数据的可信赖性,该方法还考虑了提供数据的主观性。该方法基于对用户信任态度的比较,并应用了非参数统计方法。我们已经通过广泛的模拟评估了该方法的性能,并将其与Huang提出的使用Gompertz函数对贡献进行评分的方法进行了比较。仿真结果表明,在不同的仿真设置下,我们的方法平均比Huang方法高28.6%,而没有数据可信度计算的方法平均高33.6%。

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