Future vehicles will have rich computing resources to support autonomous
driving and be connected by wireless technologies. Vehicular fog networks
(VeFN) have thus emerged to enable computing resource sharing via computation
task offloading, providing wide range of fog applications. However, the high
mobility of vehicles makes it hard to guarantee the delay that accounts for
both communication and computation throughout the whole task offloading
procedure. In this article, we first review the state-of-the-art of task
offloading in VeFN, and argue that mobility is not only an obstacle for timely
computing in VeFN, but can also benefit the delay performance. We then identify
machine learning and coded computing as key enabling technologies to address
and exploit mobility in VeFN. Case studies are provided to illustrate how to
adapt learning algorithms to fit for the dynamic environment in VeFN, and how
to exploit the mobility with opportunistic computation offloading and task
replication.

本文回顾了 Vefn 中任务卸载的现有技术，并阐述了车辆的高移动性既是时限约束的障碍，也可助于延迟表现。文章进一步探讨了机器学习和编码计算作为关键的支持技术，并提供案例以演示如何在变化的 Vefn 环境中适应学习算法，并利用车辆的移动性来实现任务卸载和任务复制。