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.

中文翻译：

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车载边缘计算中人工智能赋能的内容缓存：机遇与挑战


车载网络是未来自主和智能交通系统不可或缺的组成部分。如今，许多车载网络应用不断涌现，因此需要高效的数据计算、存储和检索解决方案。车辆边缘计算 (VEC) 是一种很有前途的技术，它使用路边单元充当边缘服务器，以实现缓存和任务卸载目的。我们提出了 VEC 中基于任务的内容缓存架构，其中确定了三个主要任务，即内容流行度预测、缓存中的内容放置以及从缓存中检索内容。我们概述了回归和深度 Q 学习等人工智能技术如何提高这些任务的效率。我们还强调了未来在以下领域的相关研究机会：用于改进缓存的协作数据共享、用于 C-V2X 中内容检索的高效子通道分配以及安全缓存。