城市场景中车联网时空数据分析及其通达性方法

程久军; 原桂远; 崔杰; 周爱国; 吕博; 李光耀

doi:10.11959/j.issn.1000-436x.2021110

您当前的位置：

首页 >

文章列表页 >

城市场景中车联网时空数据分析及其通达性方法

学术论文 | 更新时间：2024-06-05

- 城市场景中车联网时空数据分析及其通达性方法
- Spatio-temporal data analysis and accessibility method for IoV in an urban scene
- 通信学报 2021年42卷第6期页码：52-61
- 作者机构：
  
  1. 同济大学嵌入式系统与服务计算教育部重点实验室，上海 200092
  2. 安徽大学计算机科学与技术学院，安徽合肥 230601
  3. 同济大学机械与能源工程学院，上海 200092
  4. 上海师范大学天华学院，上海 201815
  5. 同济大学电子与信息工程学院，上海 200092
- 作者简介：
  
  [ "程久军（1974− ），男，安徽怀宁人，博士，同济大学教授、博士生导师，主要研究方向为车联网、无人驾驶等" ]
  [ "原桂远（1993− ），男，山东烟台人，同济大学博士生，主要研究方向为车联网、无人驾驶等" ]
  [ "崔杰（1980− ），男，河南淮阳人，博士，安徽大学教授、博士生导师，主要研究方向为车联网安全、物联网安全等" ]
  [ "周爱国（1973− ），男，湖南株洲人，博士，同济大学副教授、博士生导师，主要研究方向为机器人控制、无人驾驶等" ]
  [ "吕博（1977− ），女，辽宁抚顺人，博士，上海师范大学副教授，主要研究方向为计算机视觉、机器学习等" ]
  [ "李光耀（1965− ），男，安徽安庆人，博士，同济大学教授、博士生导师，主要研究方向为图形图像、数据挖掘及人工智能等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61872271);中央高校基本科研业务费重点领域学科交叉重大基金资助项目(22120190208);网络与交换技术国家重点实验室（北京邮电大学）开放课题基金资助项目(SKLNST-2020-1-20)
- DOI：10.11959/j.issn.1000-436x.2021110
  中图分类号： TP391.9
- 网络出版日期：2021-06，
  
  纸质出版日期：2021-06-25
- 稿件说明：
移动端阅览
程久军, 原桂远, 崔杰, 等. 城市场景中车联网时空数据分析及其通达性方法[J]. 通信学报, 2021,42(6):52-61.

Jiujun CHENG, Guiyuan YUAN, Jie CUI, et al. Spatio-temporal data analysis and accessibility method for IoV in an urban scene[J]. Journal on communications, 2021, 42(6): 52-61.
程久军, 原桂远, 崔杰, 等. 城市场景中车联网时空数据分析及其通达性方法[J]. 通信学报, 2021,42(6):52-61. DOI： 10.11959/j.issn.1000-436x.2021110.

Jiujun CHENG, Guiyuan YUAN, Jie CUI, et al. Spatio-temporal data analysis and accessibility method for IoV in an urban scene[J]. Journal on communications, 2021, 42(6): 52-61. DOI： 10.11959/j.issn.1000-436x.2021110.

摘要

为了解决城市场景中车联网时空数据异构以及单个基础设施范围内存在连通效率低下的问题，提出一种车联网时空数据分析及其通达性方法。首先，给出基于噪声去除和数据填充的时空数据分析方法，构建基于张量因子聚合的神经网络预测车辆之间的连通强度；然后，基于车联网连通强度给出有基础设施车联网的通达性方法。仿真实验结果表明，基于张量因子聚合的神经网络可以有效预测车辆之间的连通强度，所提方法可以有效减少连通冗余和路边基础设施负载。

Abstract

In order to solve the problems of diversity spatio-temporal data and low connectivity efficiency in a single road side unit for Internet of vehicles (IoV) in an urban scene

a spatio-temporal data analysis and accessibility method was presented.First

a spatio-temporal data analysis method based on de-noising and data filling was introduced

and a tensor factor aggregation-based neural network was constructed to predict connectivity intensity among vehicles.Then

a connectivity intensity prediction-based accessibility method was proposed.The simulation results demonstrate that the proposed connectivity intensity prediction method can accurately predict connectivity intensity among vehicles

and the proposed accessibility method can effectively reduce connectivity redundancy and loads of road side units.

关键词

Keywords

references

焦政达 , 马建峰 , 孙聪 , 等 . 新的车辆远程诊断授权协议 [J ] . 通信学报 , 2014 , 35 ( 11 ): 146 - 153 .

JIAO Z D , MA J F , SUN C , et al . New remote authorization protocol for vehicle diagnosis [J ] . Journal on Communications , 2014 , 35 ( 11 ): 146 - 153 .

陈亮 , 王军 , 陈蓉 , 等 . 车载自组织网视频流媒体协助下载研究 [J ] . 通信学报 , 2019 , 40 ( 1 ): 51 - 63 .

CHEN L , WANG J , CHEN R , et al . Research on video streaming media cooperative downloading in vehicular ad hoc network [J ] . Journal on Communications , 2019 , 40 ( 1 ): 51 - 63 .

ZHANG J , CUI J , ZHONG H , et al . PA-CRT:Chinese remainder theorem based conditional privacy-preserving authentication scheme in vehicular ad-hoc networks [J ] . IEEE Transactions on Dependable and Secure Computing , 2021 , 18 ( 2 ): 722 - 735 .

CUI J , ZHANG X Y , ZHONG H , et al . Extensible conditional privacy protection authentication scheme for secure vehicular networks in a multi-cloud environment [J ] . IEEE Transactions on Information Forensics and Security , 2020 , 15 : 1654 - 1667 .

CHEN C , HU J N , QIU T , et al . CVCG:cooperative V2V-aided transmission scheme based on coalitional game for popular content distribution in vehicular ad-hoc networks [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 12 ): 2811 - 2828 .

CHEN C , WANG C , QIU T , et al . Caching in vehicular named data networking:architecture,schemes and future directions [J ] . IEEE Communications Surveys ＆ Tutorials , 2020 , 22 ( 4 ): 2378 - 2407 .

CONTRERAS-CASTILLO J , ZEADALLY S , GUERRERO IBAÑEZ J A . Solving vehicular ad hoc network challenges with Big Data solutions [J ] . IET Networks , 2016 , 5 ( 4 ): 81 - 84 .

LIU Y S , CHEN X , CHEN C L , et al . Traffic big data analysis supporting vehicular network access recommendation [C ] // 2016 IEEE International Conference on Communications . Piscataway:IEEE Press , 2016 : 1 - 6 .

DU N B , ZHAN J F , ZHAO M , et al . Spatio-temporal data index model of moving objects on fixed networks using HBase [C ] // 2015 IEEE International Conference on Computational Intelligence ＆Communication Technology . Piscataway:IEEE Press , 2015 : 247 - 251 .

CHENG J J , YUAN G Y , ZHOU M C , et al . A fluid mechanics-based data flow model to estimate VANET capacity [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 6 ): 2603 - 2614 .

ALTOMARE A , CESARIO E , COMITO C , et al . Trajectory pattern mining for urban computing in the cloud [J ] . IEEE Transactions on Parallel and Distributed Systems , 2017 , 28 ( 2 ): 586 - 599 .

KUMAR N , MISRA S , RODRIGUES J J P C , et al . Coalition games for spatio-temporal big data in Internet of vehicles environment:a comparative analysis [J ] . IEEE Internet of Things Journal , 2015 , 2 ( 4 ): 310 - 320 .

刘业 , 吴国新 . 基于 802.11p/WAVE 的车联网连通性模型及其应用研究 [J ] . 通信学报 , 2013 , 34 ( 6 ): 85 - 91 .

LIU Y , WU G X.Study on the connectivity model of VANET based on 802 . 11p/WAVE and its application [J ] . Journal on Communications , 2013 , 34 ( 6 ): 85 - 91 .

QURESHI K N , ABDULLAH A H , LLORET J , et al . Road-aware routing strategies for vehicular ad hoc networks:characteristics and comparisons [J ] . International Journal of Distributed Sensor Networks , 2016 , 12 ( 3 ): 1605734 .

ZHENG J , WANG Y . Connectivity analysis of vehicles moving on a highway with one entry and exit [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 5 ): 4476 - 4486 .

CHENG J J , YUAN G Y , ZHOU M C , et al . Accessibility analysis and modeling for IoV in an urban scene [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 4 ): 4246 - 4256 .

CHENG J J , YUAN G Y , ZHOU M C , et al . A connectivity-prediction-based dynamic clustering model for VANET in an urban scene [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 9 ): 8410 - 8418 .

GARDNER E S J . Exponential smoothing:the state of the art—part II [J ] . International Journal of Forecasting , 2006 , 22 ( 4 ): 637 - 666 .

BRAKATSOULAS S , PFOSER D , TRYFONA N . Practical data management techniques for vehicle tracking data [C ] // 21st International Conference on Data Engineering . Piscataway:IEEE Press , 2005 : 324 - 325 .

SARWAR B , KARYPIS G , KONSTAN J , et al . Item-based collaborative filtering recommendation algorithms [C ] // Proceedings of the 10th International Conference on World Wide Web . New York:ACM Press , 2001 : 285 - 295 .

SOCHER R , CHEN D , MANNING C D , et al . Reasoning with neural tensor networks for knowledge base completion [J ] . Advances in Neural Information Processing Systems , 2013 , 26 : 926 - 934 .

FIEDLER M . Algebraic connectivity of graphs [J ] . Czechoslovak Mathematical Journal , 1973 , 23 ( 2 ): 298 - 305 .

ALEXIADIS V , COLYAR J , HALKIAS J , et al . The next generation simulation program [J ] . ITE Journal , 2004 , 74 ( 8 ): 22 - 26 .

CHENG J J , CHENG J L , ZHOU M C , et al . Routing in Internet of vehicles:a review [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 5 ): 2339 - 2352 .

吴振华 , 胡鹏 . VANET中路由协议分析 [J ] . 通信学报 , 2015 , 36 ( S1 ): 75 - 84 .

WU Z H , HU P . Analysis on VANET routing protocols [J ] . Journal on Communications , 2015 , 36 ( S1 ): 75 - 84 .

KARP B , KUNG H T . GPSR:greedy perimeter stateless routing for wireless networks [C ] // Proceedings of the 6th Annual International Conference on Mobile Computing and Networking . New York:ACM Press , 2000 : 243 - 254 .

浏览量

457

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

内容新鲜度保障的车联网多智能体缓存分发策略

面向新能源汽车能源交易系统的PoRT共识机制

基于新型可净化多重签名的车联网高效假名证书分发方案

面向双层区块链的人车分离信任管理方案

车联网联邦学习的数据异质性问题及基于个性化的解决方法综述