User selection based on user-union and relative entropy in mobile crowdsensing

作     者：Shao Zihao Qu Tianguang Wang Huiqiang Zou Yifan Lü Hongwu Shao Zihao;Qu Tianguang;Wang Huiqiang;Zou Yifan;Lü Hongwu

作者机构：College of Computer Science and TechnologyHarbin Engineering UniversityHarbin 150001China National Secrecy Science and Technology Evaluation CenterBeijing 100044China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2022年第29卷第3期

页      面：34-42页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61872104) Fundamental Research Fund for the Central Universities in China(3072020CF0603) 

主　　题：mobile crowdsensing(MCS) user selection interval-value user-union relative entropy 

摘      要：A critical issue in mobile crowdsensing(MCS) involves selecting appropriate users from a number of participants to guarantee the completion of a sensing task. Users may upload unnecessary data to the sensing platform, leading to redundancy and low user selection efficiency. Furthermore, using exact values to evaluate the quality of the user-union will further reduce selection accuracy when users form a union. This paper proposes a user selection method based on user-union and relative entropy in MCS. More specifically, a user-union matching scheme based on similarity calculation is constructed to achieve user-union and reduce data redundancy effectively. Then, considering the interval-valued influence, a user-union selection strategy with the lowest relative entropy is proposed. Extensive testing was conducted to investigate the impact of various parameters on user selection. The results obtained are encouraging and provide essential insights into the different aspects impacting the data redundancy and interval-valued estimation of MCS user selection.