Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing  

作　　者：Yuanni Liu Xi Liu Xin Li Mingxin Li Yi Li 

机构地区：[1]School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China [2]School of Cyber Security and Information Law,Chongqing University of Posts and Telecommunications,Chongqing 400065,China [3]State Key Laboratory of Networking and Switching,Beijing [4]Research Institute,China Unicom,Beijing 100048,China

出　　处：《China Communications》2023年第8期163-176,共14页中国通信（英文版）

基　　金：supported by the Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2021-1-18);the General Program of Natural Science Foundation of Chongqing(cstc2020jcyj-msxmX1021);the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202000602);Chongqing graduate research and innovation project(CYS22478).

摘　　要：Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

关 键 词：data average entropy human mobility prediction markov chain mobile crowd sensing 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]