移动群智感知系统边云协同工人招募算法

doi:10.13190/j.jbupt.2021-205

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (4): 77-83.doi: 10.13190/j.jbupt.2021-205

移动群智感知系统边云协同工人招募算法

奚赫然^1,2, 朱敬华², 李金宝³

1. 黑龙江大学电子工程学院, 哈尔滨 150001;
2. 黑龙江大学计算机科学与技术学院, 哈尔滨 150001;
3. 齐鲁工业大学(山东省科学院) 山东省人工智能研究院, 济南 250353

收稿日期:2021-09-22 出版日期:2022-08-28 发布日期:2022-09-03
通讯作者: 李金宝(1969—),男,教授,博士生导师,邮箱:lijinb@sdas.org。 E-mail:lijinb@sdas.org
作者简介:奚赫然(1980—),男,博士生。

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

XI Heran^1,2, ZHU Jinghua², LI Jinbao³

1. School of Electronic Engineering, Heilongjiang University, Harbin 150001, China;
2. School of Computer Science and Technology, Heilongjiang University, Harbin 150001, China;
3. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

Received:2021-09-22 Online:2022-08-28 Published:2022-09-03

摘要/Abstract

摘要： 针对移动群智感知系统中基于云平台的工人招募算法无法满足大规模网络实时任务需求的问题，提出边云协同工人招募算法，旨在减少数据传输时延，降低智能设备的能耗。在云服务层完成任务的接收、划分、发布和结果收集；在边缘层获取工人的实时信息，构建招募工人模型；在感知层完成任务传播和数据采集。实验结果表明，考虑到传感器类型、工人报价、最大分配任务数等多种因素的影响，所提算法既满足了任务成本和时间的约束，又在空间覆盖率和运行时间方面获得了较好的性能。

关键词: 移动群智感知, 工人招募, 边云协同, 空间覆盖

Abstract: Since the recruitment algorithms based on cloud platform cannot meet the needs of large scale network real-time tasks, an edge-cloud collaboration recruitmentalgorithm is proposed whose aim is to reduce the data transmission delay and the energy consumption of intelligent devices. The cloud service layer performs task reception, division, release and result collection; the edge layer performs obtaining the real-time information of workers and constructing the recruitment model of workers,while the perceptual layer performs task propagation and data collection. The experimental results show that the proposed algorithm can not only meet the cost and time constraints, but also achieve good performance in space coverage and time by taking consideration of sensor type, worker quotation and the maximum number.

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

中图分类号:

TP393

奚赫然, 朱敬华, 李金宝. 移动群智感知系统边云协同工人招募算法[J]. 北京邮电大学学报, 2022, 45(4): 77-83.

XI Heran, ZHU Jinghua, LI Jinbao. Edge-Cloud Collaborative Worker Recruitment Algorithm in Mobile Crowd Sensing System[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(4): 77-83.

参考文献

[1] LI X, ZHANG X L. Multi-task allocation under time constraints in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(4):1494-1510.
[2] GUO W Z, ZHU W P, YU Z Y, et al. A survey of task allocation:contrastive perspectives from wireless sensor networks and mobile crowdsensing[J]. IEEE Access, 2019, 7:78406-78420.
[3] BEN MESSAOUD R, REJIBA Z, GHAMRI-DOUDANE Y. An energy-aware end-to-end crowdsensing platform:sensarena[C]//2016 13th IEEE Annual Consumer Communications & Networking Conference. Piscataway, NJ:IEEE Press, 2016:284-285.
[4] ANTONIĆ A, MARJANOVIĆ M, PRIPUŽIĆ K, et al. A mobile crowd sensing ecosystem enabled by CUPUS:cloud-based publish/subscribe middleware for the Internet of things[J]. Future Generation Computer Systems, 2016, 56:607-622.
[5] WU D P, YANG Z G, YANG B R, et al. From centra-lized management to edge collaboration:a privacy-preserving task assignment framework for mobile crowdsensing[J]. IEEE Internet of Things Journal, 2021, 8(6):4579-4589.
[6] BELLAVISTA P, BELLI D, CHESSA S, et al. A social-driven edge computing architecture for mobile crowd sen-sing management[J]. IEEE Communications Magazine, 2019, 57(4):68-73.
[7] ZHOU P, CHEN W B, JI S L, et al. Privacy-preserving online task allocation in edge-computing-enabled massive crowdsensing[J]. IEEE Internet of Things Journal, 2019, 6(5):7773-7787.
[8] WANG J T, WANG F, WANG Y S, et al. Social-network-assisted worker recruitment in mobile crowd sensing[J]. IEEE Transactions on Mobile Computing, 2019, 18(7):1661-1673.
[9] LU A Q, ZHU J H. Hybrid network assisted dynamic worker recruitment algorithm[C]//2019 IEEE International Conference on Smart Internet of Things (Smart-IoT). Piscataway, NJ:IEEE Press, 2019:254-261.
[10] LU A Q, ZHU J H. Worker recruitment with cost and time constraints in mobile crowd sensing[J]. Future Generation Computer Systems, 2020, 112:819-831.

移动群智感知系统边云协同工人招募算法

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

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