Cooperative Vehicular Content Distribution in Edge Computing Assisted 5G-VANET  

作　　者：Guiyang Luo Quan Yuan Haibo Zhou Nan Cheng Zhihan Liu Fangchun Yang Xuemin (Sherman) Shen 

机构地区：[1]State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications [2]School of Electronic Science and Engineering, Nanjing University [3]Department of Electrical and Computer Engineering, University of Waterloo

出　　处：《China Communications》2018年第7期1-17,共17页中国通信（英文版）

基　　金：the support of National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant No.2016ZX03001025003;the Natural Science Foundation of Beijing under Grant No.4181002;the Natural Science Foundation of China under Grant No.91638204;BUPT Excellent Ph.D. Students Foundation under Grant No.CX2018210;Natural Sciences and Engineering Research Council (NSERC),Canada

摘　　要：By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.By leveraging the 5G enabled vehicular ad hoc network（5G-VANET）, it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.

关 键 词：content distribution 5G-VANET edge computing graph theory 

分 类 号：TN929.5[电子电信—通信与信息系统] U463.67[电子电信—信息与通信工程] U495[机械工程—车辆工程]