Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks

Abstract

Future vehicles will have rich computing resources to support autonomous driving and be connected by wireless technologies. Vehicular fog networks (VeFNs) have thus emerged to enable computing resource sharing via computation task offloading, providing a 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 VeFNs, and argue that mobility is not only an obstacle for timely computing in VeFNs, 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 VeFNs. Case studies are provided to illustrate how to adapt learning algorithms to suit the dynamic environment in VeFNs, and how to exploit the mobility with opportunistic computation offloading and task replication.