DRL-based Carbon Emission Optimization Method for the Vehicular Reverse Offloading System

The rapid development of Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) has revolutionized transportation networks by enabling real-time communication between vehicles, road infrastructure, and cloud systems. One such advancement is the vehicular reverse offloading system, which allows Road Side Units (RSUs) to offload tasks to vehicles on the road. This paradigm makes full use of the dynamic computational resources available on vehicles and helps reduce the overall carbon emissions of IoV systems. In this paper, we establish a multi-hop reverse offloading vehicular edge computing model, enabling RSUs to utilize dynamic computational resources beyond their communication range. Considering the potential variations in power supply sources for RSUs, we further construct a carbon intensity adaptive carbon emission optimization model for RSUs and optimize the system’s overall carbon emissions through deep reinforcement learning(DRL). Through extensive simulations, we demonstrate that our DRL-based approach significantly reduces carbon emissions compared to traditional task-offloading methods.