北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (4): 91-97.

• 论文 • 上一篇    下一篇

移动群智感知系统边云协同工人招募算法(CWSN 2021)

奚赫然,朱敬华,李金宝   

  1. 黑龙江大学
  • 收稿日期:2021-09-22 修回日期:2021-11-30 出版日期:2022-08-28 发布日期:2022-06-26
  • 通讯作者: 李金宝 E-mail:lijb6912@vip.sina.com
  • 基金资助:
    基于物联网的人类睡眠与健康数据获取及分析方法研究

Edge-Cloud Collaborative Worker Recruitment Algorithm in Mobile Crowd Sensing System

  • Received:2021-09-22 Revised:2021-11-30 Online:2022-08-28 Published:2022-06-26

摘要: 研究移动群智感知系统工人招募算法,针对云平台招募算法无法满足大规模网络实时任务的需求, 提出边云协同的三层工人招募算法ECRecruitment,旨在减少数据传输时延,降低智能设备能耗.云服务层负责任务接收,划分,发布和结果收集;边缘层负责获取工人实时信息,构建招募工人模型;感知层负责任务传播和数据采集. ECRecruitment算法考虑多重影响因素,如传感器类型,工人报价,最大分配任务数等.实验结果表明,ECRecruitmen算法既能满足成本和时间约束,又在空间覆盖率和运行时间方面获得较好性能.

关键词: 移动群智感知, 工人招募, 边云协同, 空间覆盖

Abstract: This paper studies the problem of worker recruitment algorithm for mobile crowd sensing system (MCS). Recruitment algorithms based on cloud platform cannot meet the needs of large scale network real-time tasks, a three-tier worker recruitment algorithm(ECRecruitment) based on edge cloud collaboration is proposed, which aims to reduce the data transmission delay and the energy consumption of intelligent devices. The cloud service layer is responsible for task reception, division, release and result collection; The edge layer is responsible for obtaining the real-time information of workers and constructing the recruitment model of workers; The perceptual layer is responsible for task propagation and data collection. ECRecruitment considers multiple influencing factors, such as sensor type, worker quotation, maximum number of assigned tasks, etc. The experimental results show that ECRecruitment can not only meet the cost and time constraints, but also achieve good performance in space coverage and operation time.

Key words: mobile crowd sensing, worker recruitment, edge-cloud collaboration, spatial coverage

中图分类号: