%0 Journal Article
%A CHEN Ning
%A QIU Tie
%A WAN Zhiguo
%A XU Tianyi
%A LIU Likun
%T User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing
%D 2022
%R 10.13190/j.jbupt.2021-217
%J Journal of Beijing University of Posts and Telecommunications
%P 70-76
%V 45
%N 4
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-217