An Edge Caching Scheme to Distribute Content in Vehicular Networks

被引:145
|
作者
Su, Zhou [1 ]
Hui, Yilong [1 ]
Xu, Qichao [1 ]
Yang, Tingting [2 ]
Liu, Jianyi [3 ]
Jia, Yunjian [4 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
[2] Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China
[4] Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
Vehicular content networks; dynamic content delivery; edge caching; CONTENT DISSEMINATION; POPULAR CONTENT; ALLOCATION; DELIVERY; ACCESS;
D O I
10.1109/TVT.2018.2824345
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Vehicular content networks (VCNs), which distribute medium-volume contents to vehicles in a fully distributed manner, represent the key enabling technology of vehicular infotainment applications. In VCNs, the road-side units (RSUs) cache replicas of contents on the edge of networks to facilitate the timely content delivery to driving-through vehicles when requested. However, due to the limited storage at RSUs and soaring content size for distribution, RSUs can only selectively cache content replicas. The edge caching scheme in RSUs, therefore, becomes a fundamental issue in VCNs. This paper addresses the issue by developing an edge caching scheme in RSUs. Specifically, we first analyze the features of vehicular content requests based on the content access pattern, vehicle's velocity, and road traffic density. A model is then proposed to determine whether and where to obtain the replica of content when the moving vehicle requests it. After this, a cross-entropy-based dynamic content caching scheme is proposed accordingly to cache the contents at the edge of VCNs based on the requests of vehicles and the cooperation among RSUs. Finally, the performance of the proposed scheme is evaluated by extensive simulation experiments.
引用
收藏
页码:5346 / 5356
页数:11
相关论文
共 50 条
  • [31] Meta-reinforcement learning for edge caching in vehicular networks
    Sakr H.
    Elsabrouty M.
    Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (04) : 4607 - 4619
  • [32] Computation offloading and content caching and delivery in Vehicular Edge Network: A survey
    Dziyauddin, Rudzidatul Akmam
    Niyato, Dusit
    Nguyen Cong Luong
    Atan, Ahmad Ariff Aizuddin Mohd
    Izhar, Mohd Azri Mohd
    Azmi, Marwan Hadri
    Daud, Salwani Mohd
    COMPUTER NETWORKS, 2021, 197
  • [33] Matching-based Content Caching in Heterogeneous Vehicular Networks
    Wu, Huaqing
    Xu, Wenchao
    Chen, Jianyin
    Wang, Li
    Shen, Xuemin
    2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2018,
  • [34] Incentive Mechanism with the Caching Strategy for Content Sharing in Vehicular Networks
    Han, Xiaojing
    Li, Xi
    Luo, Changqing
    Ji, Hong
    Zhang, Heli
    2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [35] Optimal Caching of Encoded Data for Content Distribution in Vehicular Networks
    Idir, Lilia
    Paris, Stefano
    Nait-Abdesselam, Farid
    2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION WORKSHOP (ICCW), 2015, : 2483 - 2488
  • [36] An Online Machine Learning-based Content Caching Scheme in Mobile Edge Computing Networks
    Zhao, Qi
    Li, Yi
    Liu, Hang
    DeCortec, Nicholas
    Tucker, Frank
    Chen, Genshe
    SENSORS AND SYSTEMS FOR SPACE APPLICATIONS XV, 2022, 12121
  • [37] Mobility-Aware Edge Caching for Minimizing Latency in Vehicular Networks
    AlNagar, Yousef
    Gohary, Ramy H.
    Hosny, Sameh
    El-Sherif, Amr A.
    IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY, 2022, 3 : 68 - 84
  • [38] A Cooperative Caching Algorithm for Cluster-Based Vehicular Content Networks with Vehicular Caches
    Fang, Sangsha
    Fan, Pingzhi
    2017 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2017,
  • [39] Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks
    Choi, Yoonjeong
    Lim, Yujin
    SENSORS, 2023, 23 (03)
  • [40] Proactive Edge Caching in Vehicular Networks: An Online Bandit Learning Approach
    Wang, Qiao
    Grace, David
    IEEE ACCESS, 2022, 10 : 131246 - 131263