Paper Info
Title
Mobile Edge Intelligence and Computing for the Internet of Vehicles
Abstract
The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified.
Year
DOI
Venue
2020
10.1109/JPROC.2019.2947490
arXiv: Networking and Internet Architecture
Keywords
Field
DocType
Cloud computing,Intelligent vehicles,Sensors,Task analysis,Artificial intelligence,Wireless communication
Open research,Information system,Edge computing,Wireless network,Use case,Data analysis,Computer science,Computer network,Cloud computing,Distributed computing,The Internet
Journal
Volume
Issue
ISSN
108
2
0018-9219
Citations 
PageRank 
References 
19
0.59
0
Authors
2
Name
Order
Citations
PageRank
Jun Zhang13772190.36
K. B. Letaief211078879.10