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。
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.
作者
程久军
原桂远
崔杰
周爱国
吕博
李光耀
CHENG Jiujun;YUAN Guiyuan;CUI Jie;ZHOU Aiguo;LYU Bo;LI Guangyao(Ministry of Education Key Laboratory of Embedded System and Service Computing,Tongji University,Shanghai 200092,China;School of Computer Science and Technology,Anhui University,Hefei 230601,China;School of Mechanical Engineering,Tongji University,Shanghai 200092,China;Tianhua College,Shanghai Normal University,Shanghai 201815,China;College of Electronic and Information Engineering,Tongji University,Shanghai 200092,China)
出处
《通信学报》
EI
CSCD
北大核心
2021年第6期52-61,共10页
Journal on Communications
基金
国家自然科学基金资助项目(No.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(No.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(No.SKLNST-2020-1-20)。
关键词
车联网
时空数据分析
通达性
城市场景
Internet of vehicles
spatio-temporal data analysis
accessibility
urban scene
