Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing

作     者：Li, Hanshang Li, Ting Wang, Weichao Wang, Yu 

作者机构：Univ North Carolina Charlotte Coll Comp Informat Charlotte NC 28223 USA 

出 版 物：《IEEE TRANSACTIONS ON MOBILE COMPUTING》 (IEEE移动计算汇刊)

年 卷 期：2019年第18卷第12期

页      面：2842-2855页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：U.S. National Science Foundation [CNS-1319915, CNS-1343355] National Natural Science Foundation of China (NSFC) [61428203, 61572347] U.S. Department of Transportation Center for Advanced Multimodal Mobility Solutions and Education [69A3351747133] 

主　　题：Sensors Task analysis Smart phones Heuristic algorithms Memory Data models Approximation algorithms Participant selection greedy algorithm caching mobile crowd sensing 

摘      要：With the rapid increasing of smart phones and the advances of embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for large-scale sensing applications. One of the key challenges of large-scale mobile crowd sensing is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve a certain level of coverage while satisfying some constraints. This becomes more complex when the sensing tasks are dynamic (coming in real time) and heterogeneous (with different temporal and spacial coverage requirements). In this paper, we consider such a dynamic participant selection problem with heterogeneous sensing tasks which aims to minimize the sensing cost while maintaining certain level of probabilistic coverage. Both offline and online algorithms are proposed to solve the challenging problem. Extensive simulations over a real-life mobile dataset confirm the efficiency of the proposed algorithms.