User Incentive Mechanism Based on Spatial-Temporal Correlation for Crowd Sensing

doi:10.19678/j.issn.1000-3428.0057234

Abstract

Abstract: To realize effective user incentive in crowd sensing systems,this paper proposes two user incentive algorithms based on dominant and recessive spatial-temporal characteristics.The user incentive problem of dominant spatial-temporal correlation is transformed into a set coverage problem,which is solved by using the greedy algorithm.Then the dominant spatial-temporal correlation algorithm is combined with the Markov model to solve the user incentive problem of recessive spatial-temporal correlation.Experimental results on simulated data and real datasets show that compared with the traditional Minimum Cost(MC) algorithm,Maximum Overlap(MO) algorithm,and Minimum Cost Overlap(MCO) algorithm,the dominant and recessive spatial-temporal correlation algorithms can realize user incentive with the social benefits maximized and solve the problem of low completion rate and high cost of sensing tasks.

Key words: crowd sensing, user incentive mechanism, spatial-temporal correlation, Markov model, set coverage

摘要： 为在群智感知系统中实现有效的用户激励，提出基于显性与隐性时空关联的两种用户激励算法。将显性时空关联的用户激励问题转化为集合覆盖问题并利用贪心算法对其进行求解，同时结合显性时空关联算法和马尔科夫模型求解隐性时空关联的用户激励问题。在仿真数据和真实数据集上的实验结果表明，与传统最小化花费算法、最大化覆盖算法和最小化花费覆盖数比值算法相比，显性时空关联算法和隐性时空关联算法有效解决了感知任务完成率低且花费高的问题，能在实现用户激励的情况下最大化社会收益。

关键词: 群智感知, 用户激励机制, 时空关联性, 马尔科夫模型, 集合覆盖

CLC Number:

TP393

ZHOU Qiang, LI Peng, NIE Lei. User Incentive Mechanism Based on Spatial-Temporal Correlation for Crowd Sensing[J]. Computer Engineering, 2021, 47(3): 227-236.

周强, 李鹏, 聂雷. 群智感知中基于时空关联性的用户激励机制[J]. 计算机工程, 2021, 47(3): 227-236.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0057234

http://www.ecice06.com/EN/Y2021/V47/I3/227

Figures/Tables 11

References

[1] ZHAO Jian,ZHANG Xinti,LI Jiaming,et al.Research review of crowd intelligence 2.0[J].Computer Engineering,2019,45(12):1-7.(in Chinese)赵健,张鑫褆,李佳明,等.群体智能2.0研究综述[J].计算机工程,2019,45(12):1-7.
[2] WU Yao,ZENG Juru,PENG Hui,et al.Survey on incentive mechanisms for crowd sensing[J].Journal of Software,2016,27(8):2025-2047.(in Chinese)吴垚,曾菊儒,彭辉,等.群智感知激励机制研究综述[J].软件学报,2016,27(8):2025-2047.
[3] NAN Wenqian,GUO Bin,CHEN Huihui,et al.A cross-space,multi-interaction-based dynamic incentive mechanism for mobile crowd sensing[J].Chinese Journal of Computers,2015,38(12):2412-2425.(in Chinese)南文倩,郭斌,陈荟慧,等.基于跨空间多元交互的群智感知动态激励模型[J].计算机学报,2015,38(12):2412-2425.
[4] ZHAN Yufeng,XIA Yuanqing,ZHANG Jinhui,et al.Incentive mechanism design in mobile opportunistic data collection with time sensitivity[J].IEEE Internet of Things Journal,2018,5(1):246-256.
[5] FIANDRINO C,KANTARCI B,ANJOMSHOA F,et al.Sociability-driven user recruitment in mobile crowdsensing Internet of things platforms[C]//Proceedings of Global Communications Conference.Washington D.C.,USA:IEEE Press,2016:1-8.
[6] KARALIOPOULOS M,TELELIS O,KOUTSOPOULOS I.User recruitment for mobile crowdsensing over opportunistic networks[C]//Proceedings of 2015 IEEE Conference on Computer Communications.Washington D.C.,USA:IEEE Press,2015:2254-2259.
[7] YANG Shuo,WU Fan,TANG Shaojie,et al.On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing[J].IEEE Journal on Selected Areas in Communications,2017,34(4):832-847.
[8] HE Zongjian,CAO Jiannong,LIU Xuefeng.High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility[C]//Proceedings of 2015 IEEE Con-ference on Computer Communications.Washington D.C.,USA:IEEE Press,2015:2542-2550.
[9] HE S H,SHIN D H,ZHANG J S,et al.Near-optimal allocation algorithms for location-dependent tasks in crowdsensing[J].IEEE Transactions on Vehicular Technology,2017,66(4):3392-3405.
[10] XIAO Mingjun,WU Jie,HE Huang,et al.Deadline-sensitive user recruitment for mobile crowdsensing with probabilistic collaboration[C]//Proceedings of the 24th International Conference on Network Protocols.Washington D.C.,USA:IEEE Press,2016:1-10.
[11] LIU Yan,GUO Bin,WANG Yang,et al.TaskMe:multi-task allocation in mobile crowd sensing[C]//Proceedings of 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.New York,USA:ACM Press,2016:403-414.
[12] XU Jia,XIANG Jinxin,YANG Dejun.Incentive mechanisms for time window dependent tasks in mobile crowdsensing[J].IEEE Transactions on Wireless Communications,2015,14(11):6353-6364.
[13] WANG En,YANG Yongjian,WU Jie,et al.An efficient prediction-based user recruitment for mobile crowd-sensing[J].IEEE Transactions on Mobile Computing,2018,17(1):16-28.
[14] YUAN Q,CARDEI I,WU J.An efficient prediction-based routing in disruption-tolerant networks[J].IEEE Transactions on Parallel & Distributed Systems,2011,23(1):19-31.
[15] LIU Wenbin,YANG Yongjian,WANG En,et al.Prediction based user selection in time-sensitive mobile crowdsensing[C]//Proceedings of the 14th Annual IEEE International Conference on Sensing,Communication,and Networking.Washington D.C.,USA:IEEE Press,2017:157-163.
[16] WANG Xiumin,WU Weiwei,QI Deyu.Mobility-aware participant recruitment for vehicle-based mobile crowd-sensing[J].IEEE Transactions on Vehicular Technology,2018,67(5):4415-4426.
[17] YANG Yongjian,LIU Wenbin,WANG En,et al.A prediction-based user selection framework for heterogeneous mobile crowdsensing[J].IEEE Transactions on Mobile Computing,2018,18(11):2460-2473.
[18] CHEN Huihui,GUO Bin,YU Zhiwen,et al.CrowdTracking:real-time vehicle tracking through mobile crowdsensing[J].IEEE Internet of Things Journal,2019,6(5):7570-7583.
[19] YAN Yang,SUN Lijun,ZHU Lanting.Short-term traffic flow prediction method based on spatiotemporal relativity[J].Computer Engineering,2020,46(1):31-37.(in Chinese)闫杨,孙丽珺,朱兰婷.基于时空相关性的短时交通流量预测方法[J].计算机工程,2020,46(1):31-37.
[20] YU Jiapeng,XIAO Mingjun,GAO Guoju,et al.Minimum cost spatial-temporal task allocation in mobile crowdsensing[C]//Proceedings of Wireless Algorithms,Systems,and Applications.Washington D.C.,USA:IEEE Press,2016:262-271.
[21] BRACCIALE L,BONOLA M,LORETI P,et al.CRAWDAD[EB/OL].[2019-12-05].https://crawdad.org/roma/taxi/20140717.

Please choose a citation manager

Content to export