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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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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