Mobile Edge Intelligence and Computing for the Internet of Vehicles

TL;DR

This paper surveys the development of edge information systems (EIS) for the Internet of Vehicles, emphasizing how edge computing, caching, and AI enable low-latency, localized data processing for intelligent vehicular applications.

Contribution

It provides a comprehensive overview of EIS design issues, methodologies, hardware platforms, and use cases in the context of intelligent IoV, highlighting recent advancements and open challenges.

Findings

Edge computing enables low-latency data processing for IoV.

Edge AI supports perception, mapping, and localization in vehicles.

Open research problems include resource management and security.

Abstract

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. 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, which 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 upon 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. Such system 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.