Asynchronous DRL-Based Multi-Hop Task Offloading in RSU-Assisted IoV Networks

Abstract

With the rapid advancement of intelligent transportation systems, Road Side Units (RSUs) in the Internet of Vehicles (IoV) play a crucial role in sensing environments information for vehicles to make driving decisions and transportation management. By offloading computation tasks from RSUs to other nodes such as cloud servers, other RSUs, and vehicles, the network computation resources can be fully utilized, however, at the expense of increasing task delay. In multi-hop task offloading, tasks can be forwarded to vehicles outside the coverage area of the source RSU where tasks are generated. However, due to the movement of vehicles, stable transmission among nodes is not guaranteed, posing a challenge in determining the next node for task forwarding. In this paper, we ensure the connectivity among nodes by establishing a mobility model and design a mechanism for selecting forwarding vehicles. The goal is to find those vehicles that can communicate stably with the source RSU and determine the optimal communication path. We formulate task offloading as a 0-1 mathematical model with the objective of minimizing the task delay. Subsequently, we propose a solution based on asynchronous deep reinforcement learning A3C. Through extensive simulations, we validate the effectiveness of our approach.