Please wait a minute...
浙江大学学报(工学版)  2018, Vol. 52 Issue (9): 1709-1716    DOI: 10.3785/j.issn.1008-973X.2018.09.011
计算机技术     
基于移动社交网络的群智感知社群化任务分发
王亮1,2, 於志文1, 郭斌1, 熊菲3
1. 西北工业大学 计算机学院, 陕西 西安 710072;
2. 西安科技大学 电气与控制工程学院, 陕西 西安 710054;
3. 北京交通大学 电子信息工程学院, 北京 100044
Crowd sensing socialization task allocation based on mobile social network
WANG Liang1,2, YU Zhi-wen1, GUO Bin1, XIONG Fei3
1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China;
3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
 全文: PDF(1572 KB)   HTML
摘要:

针对传统的“平台-用户”移动群智感知任务分发方式在任务执行鲁棒性以及数据回收方面存在的缺陷与不足,以群智感知参与用户的群体社会属性为基础,提出一种“平台-社群-用户”的社群化任务分发方法.基于参与用户时空移动特征分布的近似度计算对用户进行动态社群划分聚类,通过在社群中设置社群组织者与社群从属者角色,同时引入社交亲密度连接关系网络等模型,构建移动群智感知任务初次社群分发与二次用户选择的方法.基于WTD公开数据集上的实验结果表明,与传统的任务分发方式相比,所提方法可可有效提升任务完成率,增强执行过程的鲁棒性,同时可减少任务分发次数、降低通信负载.

Abstract:

A "platform-community-user" socialization task allocation approach was proposed by leveraging participant users' group social characteristics in view of the shortcomings of traditional "platform-user" mobile crowd sensing model. Firstly, a dynamic community division clustering was achieved based on the similarity of users' spatiotemporal mobility distribution. And then, via the roles of the community organizer and subordinate, a novel mobile crowd sensing task mechanism, i.e., primary community distribution and secondary user selection, was proposed by incorporating social intimacy connection network. The experimental results on public WTD data set show that, compared with the traditional task allocation method, the proposed method can effectively improve the task completion rate, enhance the robustness of the execution process, reduce the number of execution iteration and the communication overhead.

收稿日期: 2017-11-04 出版日期: 2018-09-20
CLC:  TP311  
基金资助:

国家“973”重点基础研究发展规划资助项目(2015CB352400);国家自然科学基金资助项目(61402360,61373119);陕西省教育厅科学研究计划项目(16JK1509);陕西省自然科学基础研究计划资助项目(2018JQ6034)

通讯作者: 於志文,男,教授,博导.orcid.org/0000-0002-5023-5508.     E-mail: 於志文,男,教授,博导.orcid.org/0000-0002-5023-5508.E-mail:zhiwenyu@nwpu.edu.cn
作者简介: 王亮(1984-),男,副教授,从事普适计算、群智感知研究.orcid.org/0000-0002-5897-4401.E-mail:liangwang0123@gmail.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

王亮, 於志文, 郭斌, 熊菲. 基于移动社交网络的群智感知社群化任务分发[J]. 浙江大学学报(工学版), 2018, 52(9): 1709-1716.

WANG Liang, YU Zhi-wen, GUO Bin, XIONG Fei. Crowd sensing socialization task allocation based on mobile social network. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(9): 1709-1716.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.09.011        http://www.zjujournals.com/eng/CN/Y2018/V52/I9/1709

[1] GANTI R, YE F, LEI H. Mobile crowdsensing:current state and future challenges[J]. IEEE Communications Magazine, 2011, 49(11):32-39.
[2] NI J, ZHANG A, LIN X. Security, privacy, and fairness in fog-based vehicular crowdsensing[J]. IEEE Communications Magazine, 2017, 55(6):146-152.
[3] GUO B, YU Z, ZHOU X, ZHANG D. From participatory sensing to Mobile Crowd Sensing[C]//2014 IEEE International Conference on Pervasive Computing and Communications Workshops. Budapest:IEEE, 2014:593-598.
[4] WAN J, LIU J, SHAO Z, et al. Mobile crowd sensing for traffic prediction in internet of vehicles[J]. Sensors, 2016, 16(1):88.
[5] LUDWING T, REUTER C, SIEBIGTEROTH T, et al. Crowdmonitor:mobile crowd sensing for assessing physical and digital activities of citizens during emergencies[C]//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. Seoul:ACM, 2015:4083-4092.
[6] GUO B, CHEN H, YU Z, et al. Fliermeet:cross-space public information reposting with mobile crowd sensing[J]. IEEE Transactions on Mobile Computing, 2015, 14(10):2020-2033.
[7] XIAO M, WU J, HUANG L, et al. Online task assignment for crowdsensing in predictable mobile social networks[J]. IEEE Transactions on Mobile Computing, 2017, 16(8):2306-2320.
[8] XIONG H, ZHANG D, CHEN G, et al. icrowd:near-optimal task allocation for piggyback crowdsensing[J]. IEEE Transactions on Mobile Computing, 2016, 15(8):2010-2022.
[9] ZHENG Z, WU F, GAO X, et al. A budget feasible incentive mechanism for weighted coverage maximization in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2017, 16(9):2392-2407.
[10] ZHENG L, CHEN L. Maximizing acceptance in rejection-aware spatial crowdsourcing[C]//2017 IEEE 33rd International Conference on Data Engineering (ICDE). San Diego:IEEE, 2017:71-78.
[11] PERSAUD A, OBRIEN S. Quality and acceptance of crowdsourced translation of web content[J]. International Journal of Technology and Human Interaction, 2017, 13(1):100-115.
[12] LIU Y, GUO B, WANG Y, et al. Taskme:multi-task allocation in mobile crowd sensing[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg:ACM, 2016:403-414.
[13] HE S, SHIN D, ZHANG J, et al. Near-optimal allocation algorithms for location-dependent tasks in crowdsensing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(4):3392-3405.
[14] 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.
[15] CHEUNG M, HOU F, HUANG J. Make a difference:diversity-driven social mobile crowdsensing[C]//2017 IEEE International Conference on Computer Communications. Atlanta:IEEE, 2017:1-9.
[16] LANE N, CHOU Y, ZHOU L, et al. Piggyback crowdSensing:energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities[C]//Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. Roma:ACM, 2013:1-14.

[1] 盛念祖, 李芳, 李晓风, 赵赫, 周桐. 基于区块链智能合约的物联网数据资产化方法[J]. 浙江大学学报(工学版), 2018, 52(11): 2150-2158.
[2] 杨小虎, 李珏峰. 多网络环境下基于爬山聚类算法的SOA性能优化[J]. J4, 2010, 44(4): 738-742.
[3] 谭志鹏, 谭善光. SILVER对象数据库对象持久化JAVA实现[J]. J4, 2009, 43(6): 1032-1036.
[4] 郭星明, 郭天晨, 张三元. 基于管理信息本体和需求功能构件的中间件平台[J]. J4, 2009, 43(5): 844-848.