Distributed Deep Learning Based on Edge Computing Over Internet of Vehicles: Overview, Applications, and Challenges

被引：0

作者：

Lin, Zhuangxing ^{[1
]}

Cui, Haixia ^{[1
]}

Liu, Yong ^{[1
]}

机构：

[1] South China Normal Univ, Sch Elect & Informat Engn, Foshan 528225, Peoples R China

来源：

IEEE ACCESS | 2024年 / 12卷

基金：

中国国家自然科学基金;

关键词：

Sensors; Deep learning; Roads; Computational modeling; Cloud computing; Training; Servers; Multi-access edge computing; Internet of Vehicles; mobile edge computing; the Internet of Vehicles; VEHICULAR NETWORKS; COMMUNICATION; ARCHITECTURE;

D O I：

10.1109/ACCESS.2024.3454790

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, the internet of vehicles (IoVs), mobile edge computing (MEC), and deep learning have attracted many research attentions in the applications of autonomous driving. MEC can help to reduce the network load and transmission delay by offloading the computing tasks to the powerful edge servers while deep learning can effectively improve the accuracy of obstacle detection to enhance the stability and safety of automatic driving. In this article, we first present a comprehensive overview of distributed deep learning based on edge computing over IoVs. Then, the related key techniques, including the distributed characteristics of IoVs, mobile edge collaborative computing architecture for high quality of service (QoS) requirements in terms of vehicle transmission delay and energy consumption, distributed deep learning, and its applications for vehicular networking, are discussed. Finally, the article identifies several important open challenges, opportunities, and potential research directions to provide a reference for readers in this field.

引用

页码：133734 / 133747

页数：14

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