蒋伟进^{1, 2},
吕斯健^1,,,
刘跃华¹,
陈君鹏¹,
张婉清²
1.湖南工商大学计算机与信息工程学院 长沙 410205
2.湖南工商大学大数据与互联网创新研究院 长沙 410205
基金项目:国家自然科学基金 (61772196, 61472136)，湖南省自然科学基金重点项目(2020JJ4249)，湖南省社会科学基金重点项目(2016ZDB006)，湖南省社会科学成果评审委员会课题重点项目(湘社评19ZD1005)，湖南省学位与研究生教育改革研究基金资助项目(2020JGYB234)，湖南省教育厅科学研究项目(20A131)

详细信息

作者简介:蒋伟进：男，1965年生，教授，研究方向为社会计算学、分布式计算
吕斯健：男，1996年生，硕士生，研究方向为移动群智感知、物联网
刘跃华：男，1965年生，教授，研究方向为智能化工程、大数据分发
陈君鹏：男，1997年生，硕士生，研究方向为群智感知、群体智能
张婉清：女，1997年生，硕士生，研究方向为移动群智感知、边缘计算、网络安全

通讯作者:吕斯健　lvsijian8@foxmail.com

中图分类号:TN919.2; TP273

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被引次数:0

出版历程

收稿日期:2020-06-23
修回日期:2021-05-28
网络出版日期:2021-07-13
刊出日期:2021-10-18

Task Distribution Method of Participatory Sensing Based on Urban Rail Transit

Weijin JIANG^{1, 2},
Sijian Lü^1,,,
Yuehua LIU¹,
Junpeng CHEN¹,
Wanqing ZHANG²
1. College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha 410205, China
2. Institute of Big Data and Internet Innovation, Hunan University of Technology and Business, Changsha 410205, China
Funds:The National Natural Science Foundation of China (61772196, 61472136), The Hunan Provincial Focus Natural Science Fund (2020JJ4249), The Hunan Provincial Focus Social Science Fund (2016ZDB006), The Key Project of Hunan Provincial Social Science Achievement Review Committee (XSP 19ZD1005), The Degree and Graduate Education Reform Research Project of Hunan Provincial (2020JGYB234), The Hunan Provincial Department of Education Science Research Fund (20A131)


摘要
摘要:随着当前移动终端设备的发展和5G技术的普及，移动群智感知的需求越来越大。但是目前感知任务的分发方法依然存在着传输效率低下、代价高且不稳定等问题，极大地限制了感知终端任务的完成。为此，该文利用城市轨道交通对于各大城区良好的覆盖性和轨道交通的可预测性，提出了面向激励成本的任务分发模型(ICTDM)和面向用户数量的任务分发模型(UNTDM)。通过轨道交通对聚集式人流的疏导性，实现感知任务在城市不同区域的选择性分发。并以任务所需人数和移动距离的最小化作为手段，完成降低系统总激励成本的目的。实验结果表明，该算法与同类算法相比，可以在完成相同任务集合的前提下，通过优化任务分发过程实现更少的任务参与者分发方案，以达到降低感知任务成本的目的。
关键词:数据众包/
移动物联网/
群智感知/
任务分发/
城市轨道交通
Abstract:With the current development of mobile terminal devices and the popularity of 5G technology, there is an increasing demand for mobile group intelligence awareness. However, the current distribution methods for sensing tasks still suffer from inefficient, costly and unstable transmission, which limits greatly the completion of sensing terminal tasks. For this reason, an Incentive Cost Task Distribution Model (ICTDM) and a User Number Task Distribution Model (UNTDM) based on the good coverage of urban rail transit and the predictability of urban rail transit are proposed. The selective distribution of sensory tasks in different areas of the city is achieved through the sparseness of rail traffic for aggregated pedestrian flows. And the minimization of the number of people required for the task and the distance moved is used as a means to accomplish the purpose of reducing the total incentive cost of the system. Experimental results show that this algorithm can achieve fewer task participant distribution schemes by optimizing the task distribution process to reduce the cost of perceived tasks compared with similar algorithms, while completing the same set of tasks.
Key words:Data crowdsourcing/
Mobile Internet of Things (IoT)/
Participatory sensing/
Task distribution/
Urban rail transit



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