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

机译：具有用户信任性审视的移动众胶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：移动人群是一种强大的范式，可以利用先进的传感功能和智能手机的笨蛋，以便收集和分析固定传感器网络不可能的规模上的数据。移动人群系统融入了人们，依靠他们的参与和愿意贡献最新和准确的信息，这意味着这种系统易于恶意和错误的数据。因此，信任和声誉是需要解决的关键因素，以确保移动人群系统的可持续性。这项工作的目的是定义概念信任框架，考虑移动众一体系统的人类参与，并考虑到用户除了由其智能设备生成的原始感测数据之外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贡献的数据的可信度，这些数据也考虑了贡献数据中的主观性。该方法基于用户信任态度的比较，并应用非参数统计方法。我们已经评估了我们对广泛的模拟方法的性能，并将其与黄珀斯特兹函数提出的方法进行了比较，以便评定贡献。仿真结果表明，我们的方法平均优于黄色的方法28.6％，在不同仿真设置中平均地计算了33.6％的方法。

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