Deep Reinforcement Learning for Delay-Optimized Task Offloading in Vehicular Fog Computing

TL;DR

This paper develops a deep reinforcement learning approach for task offloading in vehicular fog computing, significantly reducing delay and congestion in autonomous vehicle networks through a realistic simulation environment.

Contribution

It introduces a novel DRL-based offloading algorithm tailored for VFC, validated in a realistic simulation with a new vehicular movement model, outperforming traditional methods.

Findings

DRL reduces task delay effectively.

DRL outperforms greedy and conventional methods.

Model demonstrates scalability and congestion reduction.

Abstract

The imminent rise of autonomous vehicles (AVs) is revolutionizing the future of transport. The Vehicular Fog Computing (VFC) paradigm has emerged to alleviate the load of compute-intensive and delay-sensitive AV programs via task offloading to nearby vehicles. Effective VFC requires an intelligent and dynamic offloading algorithm. As a result, this paper adapts Deep Reinforcement Learning (DRL) for VFC offloading. First, a simulation environment utilizing realistic hardware and task specifications, in addition to a novel vehicular movement model based on grid-planned cities, is created. Afterward, a DRL-based algorithm is trained and tested on the environment with the goal of minimizing global task delay. The DRL model displays impressive results, outperforming other greedy and conventional methods. The findings further demonstrate the effectiveness of the DRL model in minimizing queue congestion, especially when compared to traditional cloud computing methods that struggle to handle the demands of a large fleet of vehicles. This is corroborated by queuing theory, highlighting the self-scalability of the VFC-based DRL approach.