@inproceedings{a5760b509aaa4c50b696c5f4cb796255,
title = "Artificial Intelligence based Edge Caching in Vehicular Mobile Networks: Architecture, Opportunities, and Research Issues",
abstract = "This paper investigates the potentials of utilizing artificial intelligence (AI) based edge caching in the next generation of vehicular mobile networks. In recent years, vehicle-to-everything (V2X) has been a research focus, which enables the exchange of information between the vehicles and the outside world. To integrate vehicular networks and cellular radio technology, cellular-V2X (C-V2X) was proposed in 3GPP release 14. Further, mobile edge caching is regarded as an effective technique to allow local data access, which can support the low latency requirement of the V2X use cases. With the advance of AI technologies such as deep learning, there has been increasing demand in inference and learning from big vehicular data. In this paper, we present the detailed architecture of AI-based edge caching in vehicular networks with misbehaving vehicle detection as an illustrative case. Performance results are provided to investigate the benefit of the proposed architecture. Finally, we highlight the potential research directions.",
author = "Liao, {Kai Min} and Chen, {Guan Yi} and Chen, {Yu Jia}",
note = "Publisher Copyright: {\textcopyright} 2019 IEICE.; 20th Asia-Pacific Network Operations and Management Symposium, APNOMS 2019 ; Conference date: 18-09-2019 Through 20-09-2019",
year = "2019",
month = sep,
doi = "10.23919/APNOMS.2019.8893099",
language = "???core.languages.en_GB???",
series = "2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 20th Asia-Pacific Network Operations and Management Symposium",
}