Mobile crowd sensing task optimal allocation： a mobility pattern matching perspective

作     者：Liang WANG Zhiwen YU Bin GUO Fei YI Fei XIONG 

作者机构：School of Computer Science Northwestern Polytechnical University Xi'an 710072 China School of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an 710054 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2018年第12卷第2期

页      面：231-244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)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：Acknowledgements This work was partially supported by the National Basic Research Program of China (2015CB352400)  the National Natural Science Foundation of China (Grant Nos. 61402360  61402369)  the Foundation of Shaanxi Educational Committee (16JK1509). The authors are grateful to the anonymous referees for their helpful comments and suggestions 

主　　题：mobile crowd sensing task allocation mobility regularity pattern matching 

摘      要：With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing （MCS） using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users＇ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy- based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on real- world open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.