感知质量优化的移动群智感知任务在线分发算法

doi:10.11959/j.issn.2096-3750.2020.00185

物联网学报 ›› 2020, Vol. 4 ›› Issue (4): 91-97.doi: 10.11959/j.issn.2096-3750.2020.00185

感知质量优化的移动群智感知任务在线分发算法

张伟^1,²,李卓^1,²(),陈昕²

¹ 网络文化与数字传播北京市重点实验室，北京 100101
² 北京信息科技大学计算机学院，北京 100101

修回日期:2020-02-18 出版日期:2020-12-30 发布日期:2020-12-14
作者简介:张伟（1996- ），女，北京人，北京信息科技大学硕士生，主要研究方向为移动群智感知|李卓（1983- ），男，河南南阳人，博士，北京信息科技大学副教授，主要研究方向为移动无线网络、分布式计算|陈昕（1965- ），男，江西南昌人，博士，北京信息科技大学教授，主要研究方向为网络性能评价、网络安全
基金资助:
国家自然科学基金资助项目(61872044);国家自然科学基金资助项目(61502040);北京市青年拔尖人才项目、北京市属高校高水平教师队伍建设支持计划青年拔尖人才培育计划资助项目(CIT＆TCD201804055);网络文化与数字传播北京市重点实验室开放课题资助项目(ICDDXN001);北京信息科技大学“勤信人才”培育计划资助项目

Data quality optimized online task allocation method for mobile crowdsensing

Wei ZHANG^1,²,Zhuo LI^1,²(),Xin CHEN²

¹ Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing 100101,China
² Beijing Information Science and Technology University,Beijing 100101,China

Revised:2020-02-18 Online:2020-12-30 Published:2020-12-14
Supported by:
The National Natural Science Foundation of China(61872044);The National Natural Science Foundation of China(61502040);The Beijing Municipal Program for Top Talent,Beijing Municipal Program for Top Talent Cultivation(CIT＆TCD201804055);The Open Program of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(ICDDXN001);The Qinxin Talent Program of Beijing Information Science and Technology University

摘要/Abstract

摘要：

感知质量优化和用户招募是移动群智感知的两个重要问题，随着数据量的大幅度增加，感知内容出现冗余，存在感知质量降低的风险。提出了一种感知质量优化的任务分发机制，在保证覆盖率的情况下，提高群体的感知质量。利用聚类算法评估任务真值，量化用户数据质量；基于汤普森抽样算法和贪婪算法设计并实现了一种用户招募策略，在保证任务空间覆盖率的基础上优化感知质量。针对TSUR（Thompson based user recruit）算法的性能进行仿真分析，并与已有的BBTA（bandit-based task assignment）算法和BUR（basic user recruitment）算法作比较。实验表明，在同一区域进行任务感知，与BBTA算法和BUR算法相比，累计感知质量分别提高了16%和20%，空间覆盖率分别提高了30%和22%。

关键词: 移动群智感知, 任务分发, 数据质量, 在线学习

Abstract:

Optimization of the perceived quality and the recruitment of user are two important issues of mobile crowdsensing.As the amount of data increases rapidly,perceived data becomes redundant,and perceived quality is at risk of decreasing.A mechanism of task assignment based on the perceptive quality optimization was proposed to improve the perceived quality under the condition of full coverage.The clustering algorithm was used to evaluate the truth value of the task and quantify the quality of the user data.Based on Thompson sampling algorithm and greedy algorithm,a user recruitment strategy was designed and implemented to optimize the perceived quality on the basis of ensuring the spatial coverage of the task.The performance of Thompson based user recruit (TSUR) algorithm was simulated and analyzed that compared with the existing algorithms of BBTA and basic user recruitment (BUR).Experiments show that in the same area,compared with bandit-based task assignment (BBTA) algorithm and BUR algorithm,the quality of the cumulative sensing data was improved by 16% and 20%,and the spatial coverage was improved by 30% and 22%.

Key words: mobile crowdsensing, task assignment, data quality, online learning

中图分类号:

TP393.01

张伟,李卓,陈昕. 感知质量优化的移动群智感知任务在线分发算法[J]. 物联网学报, 2020, 4(4): 91-97.

Wei ZHANG,Zhuo LI,Xin CHEN. Data quality optimized online task allocation method for mobile crowdsensing[J]. Chinese Journal on Internet of Things, 2020, 4(4): 91-97.

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参考文献 21

[1]	LEASE M , . On quality control and machine learning in crowdsourcing[C]// The 11th AAAI Workshop on Human Computation. AAAI Press, 2011: 97-102.
[2]	赵东, 马华东, 刘亮 . 移动群智感知质量度量与保障[J]. 中兴通讯技术, 2015,21(6): 2-5.
	ZHAO D , MA H D , LIU L . Quality measuring and assurance for mobile crowd sensing[J]. ZTE Technology Journal, 2015,21(6): 2-5.
[3]	ZHANG H , MA Y , SUGIYAMA M . Bandit-based task assignment for heterogeneous crowdsourcing[J]. Neural Computation, 2015,27(11): 2447-2475.
[4]	XIAO M J , WU J , ZHANG S ,et al. Secret-sharing-based secure user recruitment protocol for mobile crowdsensing[C]// IEEE INFOCOM 2017 IEEE Conference on Computer Communications. IEEE, 2017.
[5]	GUO B , YU Z W , ZHANG D Q ,et al. From participatory sensing to mobile crowd sensing[C]// 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS). IEEE, 2014: 593-598.
[6]	ZHAO Y R , ZHANG Y , YU T ,et al. CityDrive:a map-generating and speed-optimizing driving system[C]// IEEE INFOCOM 2014 IEEE Conference on Computer Communications. IEEE, 2014: 1986-1994.
[7]	KIM S , ROBSON C , ZIMMERMAN T ,et al. Creek watch:pairing usefulness and usability for successful citizen science[C]// SIGCHI Conference on Human Factors in Computing Systems. ACM, 2011: 2125-2134.
[8]	MATHUR S , JIN T , KASTURIRANGAN N ,et al. ParkNet:drive-by sensing of road-side parking statistics[C]// The 8th International Conference on Mobile Systems,Applications,and Services. ACM, 2010: 123-136.
[9]	SIMOENS P , XIAO Y , PILLAI P ,et al. Scalable crowd-sourcing of video from mobile devices[C]// The 11th Annual International Conference on Mobile Systems,Applications,and Services. ACM, 2013: 139-152.
[10]	WANG J T , WANG Y S , ZHANG D Q ,et al. Multi-task allocation in mobile crowd sensing with individual task quality assurance[J]. IEEE Transactions on Mobile Computing, 2018,17(9): 2101-2113.
[11]	KO H , PACK S , LEUNG V C M . Coverage-guaranteed and energy-efficient participant selection strategy in mobile crowdsensing[J]. IEEE Internet of Things Journal, 2018:1.
[12]	WANG E , YANG Y J , LOU K H ,et al. User selection utilizing data properties in mobile crowdsensing[J]. Information Sciences, 2019,490: 210-226.
[13]	MAARRY K E , BALKE W T , CHO H ,et al. Skill ontology-based model for quality assurance in crowdsourcing[C]// International Conference on Database Systems for Advanced Applications. Springer, 2014.
[14]	HU X , LI X , NGAI E C H ,et al. Multidimensional context-aware social network architecture for mobile crowdsensing[J]. IEEE Communications Magazine, 2014,52(6): 78-87.
[15]	ALLAHBAKHSH M , BENATALLAH B , IGNJATOVIC A ,et al. Quality control in crowdsourcing systems:issues and directions[J]. IEEE Internet Computing, 2013,17(2): 76-81.
[16]	LIU S Z , ZHENG Z Z , WU F ,et al. Context-aware data quality estimation in mobile crowdsensing[C]// IEEE INFOCOM 2017 IEEE Conference on Computer Communications. IEEE, 2017.
[17]	YANG S , WU F , TANG S J ,et al. On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing[J]. IEEE Journal on Selected Areas in Communications, 2017: 832-847.
[18]	GUO B , CHEN H H , QI H ,et al. Worker-contributed data utility measurement for visual crowdsensing systems[J]. IEEE Transactions on Mobile Computing, 2017,16(8): 2379-2391.
[19]	SALAS O F , ADZIC V , KALVA H . Subjective quality evaluations using crowdsourcing[C]// 2013 Picture Coding Symposium(PCS). IEEE, 2013.
[20]	RUSSO D , VAN R B , KAZEROUNI A ,et al. A tutorial on Thompson sampling[J]. Foundations and Trends in Machine Learning, 2017,11(1):112.
[21]	KARALIOPOULOS M , TELELIS O , KOUTSOPOULOS I . User recruitment for mobile crowdsensing over opportunistic networks[C]// Computer Communications. IEEE, 2015: 2254-2262.

感知质量优化的移动群智感知任务在线分发算法

Data quality optimized online task allocation method for mobile crowdsensing

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