Dynamic Multi-Task Allocation Method for Passenger Diffusion in Mobile Crowd Sensing

作     者：JIANG Weijin LYU Sijian JIANG Weijin;LYU Sijian

作者机构：College of Computer and Information Engineering Hunan University of Technology and Business 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2021年第30卷第5期

页      面：940-946页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China (No.61772196, No.61472136) the Hunan Provincial Focus Natural Science Fund (No.2020JJ4249) the Key Social Science Fund of Hunan Provincial (No.2016ZDB006) the Key Project of Hunan Provincial Social Science Achievement Review Committee(No.XSP19ZD1005) the Hunan Province Academic Degree and Graduate Education Reform Research Project (No.2020JGYB234) 

主　　题：Mobile radio systems Optimisation techniques Mobile, ubiquitous and pervasive computing Mobile crowd sensing Multitask allocation Subway passengers Participant selection Internet of things 

摘      要：This paper aims to solve the problem of low efficiency, high cost and instability in opportunistic network transmission in the process of mobile group intelligence perception task allocation. Two multi-task dynamic distribution methods based on Lowest cost Participant selection algorithm(LC-PSA) based on user incentive cost and Least number Participant selection algorithm(LN-PSA) based on number of users are proposed respectively. Through these two algorithms,the goal of minimizing the number of people and moving distance required for the task and reducing the system s incentive cost is achieved. Simulation experiments show that compared with similar algorithms, the number of participants in the task distribution scheme selected by the LN-PSA algorithm is reduced by 24.0%, and the system resource consumption is lower, which can provide stable services for the system when users are insufficient in emergencies. Compared with the traditional greedy heuristics algorithm, the LC-PSA algorithm reduces the total system cost by 37.74% and has better overall performance in the comparison experiment.