Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from...

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Main Authors: Hongjing Ji, Osama Alfarraj, Amr Tolba
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9047892/
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spelling doaj-39a792e61f4d4a8190e6c605870b78b62021-03-30T01:29:23ZengIEEEIEEE Access2169-35362020-01-018610206103410.1109/ACCESS.2020.29836099047892Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and ApplicationsHongjing Ji0https://orcid.org/0000-0003-4674-5780Osama Alfarraj1https://orcid.org/0000-0001-6111-8617Amr Tolba2https://orcid.org/0000-0003-3439-6413School of Software, Dalian University of Technology, Dalian, ChinaComputer Science Department, Community College, King Saud University, Riyadh, Saudi ArabiaComputer Science Department, Community College, King Saud University, Riyadh, Saudi ArabiaWith the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.https://ieeexplore.ieee.org/document/9047892/Internet of Vehicles (IoV)mobile edge computing (MEC)artificial intelligence (AI)
collection DOAJ
language English
format Article
sources DOAJ
author Hongjing Ji
Osama Alfarraj
Amr Tolba
spellingShingle Hongjing Ji
Osama Alfarraj
Amr Tolba
Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
IEEE Access
Internet of Vehicles (IoV)
mobile edge computing (MEC)
artificial intelligence (AI)
author_facet Hongjing Ji
Osama Alfarraj
Amr Tolba
author_sort Hongjing Ji
title Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
title_short Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
title_full Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
title_fullStr Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
title_full_unstemmed Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
title_sort artificial intelligence-empowered edge of vehicles: architecture, enabling technologies, and applications
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.
topic Internet of Vehicles (IoV)
mobile edge computing (MEC)
artificial intelligence (AI)
url https://ieeexplore.ieee.org/document/9047892/
work_keys_str_mv AT hongjingji artificialintelligenceempowerededgeofvehiclesarchitectureenablingtechnologiesandapplications
AT osamaalfarraj artificialintelligenceempowerededgeofvehiclesarchitectureenablingtechnologiesandapplications
AT amrtolba artificialintelligenceempowerededgeofvehiclesarchitectureenablingtechnologiesandapplications
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