群智感知中用户细粒度可靠性与真值预估模型

User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing

为了提高群智感知中感知数据的质量问题,提出了一种基于用户细粒度可靠性预估任务真值的方法,通过用户建模筛选出高质量数据。首先根据影响用户执行任务的瞬时因素评估用户的实时可靠性;其次,在刻画历史信誉度方面,引入信息熵衡量用户的信誉分布,包括用户总体信誉分布和在不同类别任务下的信誉分布;基于用户可靠性设计了有效的真值预估方法预估任务真值。实验结果表明,所提模型能够有效地评估用户在多类别任务下的可靠性,提高任务真值预估的准确率。

To improve perceived data quality in mobile crowdsensing, a method for estimating the truth value of crowdsensing tasks based on user fine-grained reliability is proposed, whichselect high-quality data through user modeling. First, the real-time reliability of users is evaluated according to the instantaneous factors that affect their task execution. Then, information entropy is introduced to measure the user’s reputation distribution, including the user’s overall reputation distribution and the reputation distribution under different tasks. Next, based on user reliability, an effective truth estimation method is designed to predict task truth. Experimental results show that the proposed model can effectively evaluate the reliability of users in multi-type tasks and improve the accuracy of task truth estimation.