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

## TL;DR

This paper reviews task offloading in vehicular fog networks, highlighting mobility as both a challenge and an opportunity, and explores machine learning and coded computing to optimize delay in dynamic vehicular environments.

## Contribution

It introduces a comprehensive review of VeFN task offloading, emphasizing mobility's dual role and proposing adaptive learning and coding techniques for delay optimization.

## Key findings

- Mobility can be exploited to improve delay performance.
- Machine learning enables adaptive offloading strategies.
- Coded computing enhances reliability and efficiency.

## Abstract

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.