Mobile Edge Intelligence and Computing for the Internet of Vehicles
By Jun Zhang, Khaled Ben Letaief. Proceedings of the IEEE, 2019
463 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/2a6459d12bc80b49bb1b802be38a29df0364b67a
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In Computer Networks and Communications
Citations in the corpus
- Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The data processing tasks for autonomous driving mainly include perception, high-definition (HD) mapping, as well as SLAM [117], [302].”
- Communication-Efficient Edge AI: Algorithms and Systems (2020, background): “…breakthroughs in a wide range of applications, including speech processing [1], image classi-fication [2] and reinforcement learning [3], etc. AI is expected to affect significant segments of many vertical industries and our daily life, such as intelligent vehicles [4] and tactile robots [5].”
- Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2023, background): “These are embedded with several sensing instruments (e.g., cameras, Light Detection and Ranging (LIDAR), and radar) and thus generate massive amounts of data [220].”
- Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, unknown): “Contributions AIGC Algorithms AIGC Applications Edge Intelligence 2019 [14] Introduce mobile edge intelligence, and discuss the infrastructure, implementation methodologies, and use cases 7 7 3”
- Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “Finally, resource sharing will also bring some data privacy and security issues [160].”