Intelligent Content Precaching Scheme for Platoon-Based Edge Vehicular Networks

被引:15
|
作者
Wu, Yu [1 ]
Fang, Xuming [1 ]
Luo, Chunbo [2 ]
Min, Geyong [2 ]
机构
[1] Southwest Jiaotong Univ, Key Lab Informat Coding & Transmiss, Chengdu 611756, Peoples R China
[2] Univ Exeter, Coll Engn Math & Phys Sci, Dept Comp Sci, Exeter EX4 4QF, Devon, England
来源
IEEE INTERNET OF THINGS JOURNAL | 2022年 / 9卷 / 20期
关键词
Reliability; Wireless communication; Quality of service; Network slicing; Vehicle dynamics; Optimization; Wireless sensor networks; Content precaching; deep reinforcement learning (DRL); mobile-edge caching; network slicing; platoon-based vehicular networks; CONTENT DISSEMINATION; JOINT OPTIMIZATION; CONNECTED VEHICLES; REINFORCEMENT; INTERNET; COMMUNICATION; COMPUTATION; RESOURCES; MANAGEMENT; PLACEMENT;
D O I
10.1109/JIOT.2022.3178099
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To provide various onboard entertainment services, the ever-increased Internet contents to be exchanged among remote data centers, roadside units (RSUs), and vehicles demand reliable and fast content dissemination in the vehicular networks. Edge precaching technology is expected to provide flexible and low-latency content dissemination by allowing edge nodes (i.e., RSUs and vehicles) to precache contents. However, the content dissemination process of edge precaching still suffers from high mobility and highly dynamic topology of vehicular networks. The recently proposed platoon-based vehicular network has potentials to mitigate the mobility challenges, but need to deal with multihop wireless content dissemination's latency and reliability issues. Additionally, the network resources are limited in edge nodes, whereas various onboard Internet services with different Quality-of-Service (QoS) requirements share the same resource pool by the same network resource scheduling policy, thereby decaying the network performance. Based on the above observations, to cope with the challenging content precaching problem under diverse QoS requirements in a platoon-based edge vehicular network, we first abstract two isolated virtual content service slices with different QoS requirements based on network slicing technology to provide on-demand customized services. Then, we propose an intelligent deep reinforcement learning (DRL)-based content precaching scheme, which optimally matches the available communication resources and limited caching capacities in the edge vehicular network. The scheme jointly considers the impacts of content precaching policy and multihop wireless transmission on the content precaching performance. Simulation results show that our proposed DRL-based content precaching scheme achieves a competitive performance of reliability and latency comparing with other state-of-the-art algorithms.
引用
收藏
页码:20503 / 20518
页数:16
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