偏好感知的边云协同群智感知参与者选择策略

doi:10.19665/j.issn1001-2400.2022.01.014

西安电子科技大学学报 ›› 2022, Vol. 49 ›› Issue (1): 142-151.doi: 10.19665/j.issn1001-2400.2022.01.014

偏好感知的边云协同群智感知参与者选择策略

王汝言^1,^2,³(),刘佳^1,^2,³(),何鹏^1,^2,³(),崔亚平^1,^2,³()

1.重庆邮电大学通信与信息工程学院,重庆 400065
2.重庆高校市级光通信与网络重点实验室,重庆 400065
3.泛在感知与互联重庆市重点实验室,重庆 400065

收稿日期:2020-11-28 出版日期:2022-02-20 发布日期:2022-04-27
通讯作者: 何鹏
作者简介:王汝言(1969—),男,教授,博士,E-mail: wangry@cqupt.edu.cn;|刘佳(1996—),男,重庆邮电大学硕士研究生,E-mail: 1747543987@qq.com;|崔亚平(1986—),男,讲师,博士,E-mail: cuiyp@cqupt.edu.cn
基金资助:
国家自然科学基金(61901070);国家自然科学基金(61871062);国家自然科学基金(61771082);国家自然科学基金(61801065);重庆市教委科学技术研究项目(KJQN201900611);重庆市教委科学技术研究项目(KJQN202000603);重庆市高校创新研究群体(CXQT20017);重庆市自然科学基金重点项目(cstc2020jcyj-zdxmX0024)

Preference aware participant selection strategy for edge-cloud collaborative crowdsensing

WANG Ruyan^1,^2,³(),LIU Jia^1,^2,³(),HE Peng^1,^2,³(),CUI Yaping^1,^2,³()

1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
2. Chongqing Key Laboratory of Optical Communication and Networks,Chongqing 400065,China
3. Chongqing Key Laboratory of Ubiquitous Sensing and Networking,Chongqing 400065,China

Received:2020-11-28 Online:2022-02-20 Published:2022-04-27
Contact: Peng HE

摘要/Abstract

摘要：

群智感知依靠大量用户的移动性和智能设备的传感能力完成数据的收集,已经成为一种有效的感知数据收集方式。现有集中式群智感知网络中,云平台负责任务分发和数据收集等全部过程,难以有效处理大量实时数据,感知成本高;不同参与者对待任务的兴趣不同,忽略偏好因素会导致所选参与者完成任务的效率较低,参与者满意度较差。针对上述群智感知网络中存在的问题,提出一种边云协同架构下偏好感知的参与者选择策略。参与者选择过程由云平台和边缘节点协作执行。云平台基于任务的不同位置向边缘节点分发任务,并且从边缘节点收集数据。边缘节点负责参与者选择过程,通过评估时间匹配度、距离匹配度、任务类型和奖励,量化用户对任务的偏好;通过评估用户的声誉和感知成本,量化任务对用户的偏好。基于双方偏好和稳定匹配理论,将参与者选择问题建模为用户与任务间的多对一稳定匹配问题,并且求解稳定匹配使参与者偏好最大化。结果表明,该策略所选参与者的满意度较高,平台收集的数据质量较好。

关键词: 群智感知, 边云协同, 参与者选择, 数据质量, 用户偏好

Abstract:

Crowdsensing relies on the mobility of a large number of users and the sensing ability of intelligent devices to complete data collection,which has become an effective way of sensing data collection.In the existing crowdsensing network,the cloud platform is responsible for the whole process of task distribution and data collection,so it is difficult to effectively process a large amount of real-time data and the sensing cost is high.Different participants have different interests in tasks.Ignoring preference factors will lead to a low efficiency and poor satisfaction of selected participants.Aiming at the problems existing in the above crowdsensing network,a preference aware participant selection strategy under the edge-cloud collaborative architecture is proposed.The participant selection process is performed by the cloud platform and edge nodes in collaboration.The cloud platform distributes tasks to edge nodes based on different locations of tasks,and collects data from edge nodes.The edge node is responsible for the participant selection process,quantifying the user's preference for the task by evaluating the time matching degree,distance matching degree,task type and reward,and quantifying the user's preference for the task by evaluating the user's reputation and sensing cost.Based on the bilateral preference and stable matching theory,the participant selection problem is modeled as a many-to-one stable matching problem between users and tasks,with the stable matching solved to maximize the participant preference.The results show that the participants selected by this strategy have high satisfaction and that the data quality collected by the platform is good.

Key words: crowdsensing, edge cloud collaboration, participant selection, data quality, user preference

中图分类号:

TN929.5

王汝言,刘佳,何鹏,崔亚平. 偏好感知的边云协同群智感知参与者选择策略[J]. 西安电子科技大学学报, 2022, 49(1): 142-151.

WANG Ruyan,LIU Jia,HE Peng,CUI Yaping. Preference aware participant selection strategy for edge-cloud collaborative crowdsensing[J]. Journal of Xidian University, 2022, 49(1): 142-151.

图/表 7

图1

表1

图2

图3

图4

图5

图6

参考文献 23

[1]	GANTI R K, YE F, LEI H. Mobile Crowdsensing:Current State and Future Challenges[J]. IEEE Communications Magazine, 2011, 49(11):32-39.
[2]	CAPPONI A, FIANDRINO C, KANTARCI B, et al. A Survey on Mobile Crowdsensing Systems:Challenges,Solutions,and Opportunities[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3):2419-2465.
[3]	WANG J, WANG Y, ZHANG D, et al. Energy Saving Techniques in Mobile Crowd Sensing:Current State and Future Opportunities[J]. IEEE Communications Magazine, 2018, 56(5):164-169.
[4]	ZHOU Y, LIU L, WANG L, et al. Service Aware 6G:An Intelligent and Open Network Based on Convergence of Communication,Computing and Caching[J]. Digital Communications and Networks, 2020, 6(3):253-260. doi: 10.1016/j.dcan.2020.05.003
[5]	ZHOU Y, TIAN L, LIU L, et al. Fog Computing Enabled Future Mobile Communication Networks:A Convergence of Communication and Computing[J]. IEEE Communications Magazine, 2019, 57(5):20-27.
[6]	ZHOU Z, LIAO H, GU B, et al. Robust Mobile Crowd Sensing:When Deep Learning Meets Edge Computing[J]. IEEE Network, 2018, 32(4):54-60.
[7]	MARJANOVIĆ M, ANTONIĆ A, ŽARKO I P. Edge Computing Architecture for Mobile Crowdsensing[J]. IEEE Access, 2018, 6:10662-10674. doi: 10.1109/ACCESS.2018.2799707
[8]	ZHOU P, CHEN W, JI S, et al. Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing[J]. IEEE Internet of Things Journal, 2019, 6(5):7773-7787. doi: 10.1109/JIOT.2019.2903515
[9]	WU D, LIU J, YANG Z. Bilateral Satisfaction Aware Participant Selection with MEC for Mobile Crowd Sensing[J]. IEEE Access, 2020, 8:48110-48122. doi: 10.1109/ACCESS.2020.2978774
[10]	WU D, LI H, WANG R. User Characteristic Aware Participant Selection for Mobile Crowdsensing[J]. Sensors, 2018, 18(11):3959. doi: 10.3390/s18113959
[11]	WANG J, WANG L, WANG Y, et al. Task Allocation in Mobile Crowd Sensing:State-of-the-Art and Future Opportunities[J]. IEEE Internet of Things Journal, 2018, 5(5):3747-3757. doi: 10.1109/JIOT.2018.2864341
[12]	WANG X, WU W, QI D. Mobility-Aware Participant Recruitment for Vehicle-Based Mobile Crowdsensing[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5):4415-4426. doi: 10.1109/TVT.2017.2787750
[13]	WANG E, YANG Y, WU J, et al. An Efficient Prediction-Based User Recruitment for Mobile Crowdsensing[J]. IEEE Transactions on Mobile Computing, 2018, 17(1):16-28. doi: 10.1109/TMC.2017.2702613
[14]	XIONG J, CHEN X, YANG Q, et al. A Task-Oriented User Selection Incentive Mechanism in Edge-Aided Mobile Crowdsensing[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(4):2347-2360. doi: 10.1109/TNSE.2019.2940958
[15]	WU Y, LI F, MA L, et al. A Context-Aware Multiarmed Bandit Incentive Mechanism for Mobile Crowd Sensing Systems[J]. IEEE Internet of Things Journal, 2019, 6(5):7648-7658. doi: 10.1109/JIOT.2019.2903197
[16]	WANG J, WANG F, WANG Y, et al. Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors[J]. IEEE Transactions on Mobile Computing, 2019, 18(9):1979-1991. doi: 10.1109/TMC.2018.2869387
[17]	GUO B, LIU Y, WU W, et al. Activecrowd:A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems[J]. IEEE Transactions on Human-Machine Systems, 2016, 47(3):392-403. doi: 10.1109/THMS.2016.2599489
[18]	PENG D, WU F, CHEN G.. Data Quality Guided Incentive Mechanism Design for Crowdsensing[J]. IEEE Transactions on Mobile Computing, 2017, 17(2):307-319. doi: 10.1109/TMC.2017.2714668
[19]	LI Z, LIU H, WANG R. Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing[J]. Sensors, 2019, 19(21):4666. doi: 10.3390/s19214666
[20]	LIU L, ZHOU Y, YUAN J, et al. Economically Optimal MS Association for Multimedia Content Delivery in Cache-Enabled Heterogeneous Cloud Radio Access Networks[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(7):1584-1593. doi: 10.1109/JSAC.2019.2916280
[21]	GALE D, SHAPLEY L S. College Admissions and The Stability of Marriage[J]. The American Mathematical Monthly, 1962, 69(1):9-15. doi: 10.1080/00029890.1962.11989827
[22]	YANG J, FU L, YANG B, et al. Participant Service Quality Aware Data Collecting Mechanism with High Coverage for Mobile Crowdsensing[J]. IEEE Access, 2020, 8:10628-10639. doi: 10.1109/ACCESS.2020.2965734
[23]	LIU Y, XU X, PAN J, et al. A Truthful Auction Mechanism for Mobile Crowd Sensing with Budget Constraint[J]. IEEE Access, 2019, 7:43933-43947. doi: 10.1109/ACCESS.2019.2902882

参数名称	参数值	参数名称	参数值
感知区域大小/m²	1 000	用户感知成本	1~3
整个感知周期/min	30	用户历史感知任务次数	5
用户数量	10~300	感知数据质量	0~1
任务数量	10~50	感知任务类型数	3
时间匹配度	0~1	用户感知时间/min	5~15
距离匹配度	0~1	单个任务预算	5~15
任务需求用户数量	1~5

偏好感知的边云协同群智感知参与者选择策略

Preference aware participant selection strategy for edge-cloud collaborative crowdsensing

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 23

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	田霖,苏智杰,冯婉媚,陈真,唐杰,周恩丞. 面向多无人机携能网络的轨迹与资源规划算法[J]. 西安电子科技大学学报, 2021, 48(6): 115-122.
[2]	王汝言,吴和,崔亚平,吴大鹏,张鸿. 超密集网络中多基站博弈边缘卸载策略[J]. 西安电子科技大学学报, 2021, 48(4): 1-10.
[3]	宋传旺,刘栋,李恩玉,赵瑞收,郝思媛,申天琪. NOMA系统的被动中继选择策略[J]. 西安电子科技大学学报, 2021, 48(3): 56-62.
[4]	汤小波,李里,郝莉,范平志. 随机接入中基于一次碰撞序列的接入策略设计[J]. 西安电子科技大学学报, 2020, 47(6): 13-20.
[5]	王家恒,乐煜炜,张博文,郭瑞伟,高征,王子悦,凌昕彤. 区块链无线接入网:面向未来移动通信的新架构[J]. 西安电子科技大学学报, 2020, 47(5): 3-10.
[6]	王忍,王翊,胡艳军,蒋芳,许耀华. 超密集异构网络中过载MEC服务器的协作卸载[J]. 西安电子科技大学学报, 2020, 47(2): 126-134.
[7]	罗轶,施荣华,董健,王雨婷. κ-μ衰落下能量采集认知中继网络性能分析[J]. 西安电子科技大学学报, 2020, 47(1): 52-59.
[8]	刘灿,李勇朝,张锐,李涛. 三维大规模多进多出系统中导频污染抑制方案[J]. 西安电子科技大学学报, 2019, 46(4): 9-15.
[9]	朱翠涛,吴蓓. 一种低复杂度SCMA多用户检测算法[J]. 西安电子科技大学学报, 2019, 46(4): 80-86.
[10]	简鑫,王芳,宋健,方伟,蒋欣,曾孝平,谭晓衡. 多信道S-ALOHA功率攀升机制的性能分析[J]. 西安电子科技大学学报, 2019, 46(3): 59-64.
[11]	郑创明,刘龙伟,张海林,李勇朝. 超密集网络中宏微协作的干扰最小化资源分配[J]. 西安电子科技大学学报, 2019, 46(2): 6-11.
[12]	唐杰,敖少鹏,袁文俊,林沛斐,汪洋. 直流接地极极址刀闸控制系统[J]. 西安电子科技大学学报, 2019, 46(2): 78-82.
[13]	李中捷,陈燚雷,曹世明,朱翠涛. 一种蜂窝移动用户小区切换的宏分集方案[J]. 西安电子科技大学学报, 2019, 46(2): 114-118.
[14]	杨广宇, 仇洪冰. 保密MIMO无线携能系统的鲁棒能效优化[J]. 西安电子科技大学学报, 2018, 45(6): 31-37.
[15]	岳婧;白宝明. 无线传感器网络码分复用方案性能分析[J]. 西安电子科技大学学报, 2016, 43(3): 6-12.