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The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.


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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey

Show Author's information Xiaolong XuHaoyuan LiWeijie XuZhongjian LiuLiang YaoFei Dai( )
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology and Engineering, Nanjing 210044, China
School of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650233, China
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Abstract

The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

Keywords:

edge service, internet of vehicles, artificial intelligence
Received: 28 June 2020 Revised: 14 July 2020 Accepted: 02 August 2020 Published: 29 September 2021 Issue date: April 2022
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Publication history

Received: 28 June 2020
Revised: 14 July 2020
Accepted: 02 August 2020
Published: 29 September 2021
Issue date: April 2022

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© The author(s) 2022

Acknowledgements

This research was supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps (No. 2020DB005), the National Key R&D Program of China (No. 2019YFE0190500), the National Natural Science Foundation of China (Nos. 61702442, 61862065, and 61702277), the Application Basic Research Project in Yunnan Province (No. 2018FB105), the Major Project of Science and Technology of Yunnan Province (Nos. 202002AD080002 and 2019ZE005), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.

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