Spatio-temporal data analysis and accessibility method for IoV in an urban scene

被引：0
|
作者：
Cheng J. [1 ]
Yuan G. [1 ]
Cui J. [2 ]
Zhou A. [3 ]
Lyu B. [4 ]
Li G. [5 ]
机构：
[1] Ministry of Education Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai
[2] School of Computer Science and Technology, Anhui University, Hefei
[3] School of Mechanical Engineering, Tongji University, Shanghai
[4] Tianhua College, Shanghai Normal University, Shanghai
[5] College of Electronic and Information Engineering, Tongji University, Shanghai
来源：
Zhou, Aiguo (zhouaiguo@tongji.edu.cn) | 1600年 / Editorial Board of Journal on Communications卷 / 42期
基金：
中国国家自然科学基金;
关键词：
Accessibility; Internet of vehicles; Spatio-temporal data analysis; Urban scene;
D O I：
10.11959/j.issn.1000-436x.2021110
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：52 / 61
页数：9
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