AI-Empowered Content Caching in Vehicular Edge Computing: Opportunities and Challenges

Muhammad Awais Javed, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Vehicular networks are an indispensable component of future autonomous and intelligent transport systems. Today, many vehicular networking applications are emerging, and therefore, efficient data computation, storage, and retrieval solutions are needed. Vehicular edge computing (VEC) is a promising technique that uses roadside units to act as edge servers for caching and task offloading purposes. We present a task-based architecture of content caching in VEC, where three major tasks are identified, namely, content popularity prediction, content placement in the cache, and content retrieval from the cache. We present an overview of how artificial intelligence techniques such as regression and deep Q-learning can improve the efficiency of these tasks. We also highlight related future research opportunities in areas such as collaborative data sharing for improved caching, efficient sub-channel allocation for content retrieval in C-V2X, and secure caching.

Original languageEnglish
Article number9454594
Pages (from-to)109-115
Number of pages7
JournalIEEE Network
Volume35
Issue number3
DOIs
StatePublished - May 1 2021

Bibliographical note

Publisher Copyright:
© 1986-2012 IEEE.

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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