Deep Reinforcement Learning-Guided Task Reverse Offloading in Vehicular Edge Computing

Abstract

The rapid development of Vehicular Edge Computing (VEC) provides great support for Collaborative Vehicle Infrastructure System (CVIS) and promotes the safety of autonomous driving. In CVIS, crowd-sensing data will be uploaded to the VEC server to fuse the data and generate tasks. However, when there are too many vehicles, it brings huge challenges for VEC to make proper decisions according to the information from vehicles and roadside infrastructure. In this paper, a reverse offloading framework is constructed, which comprehensively considers the relationship balance between task completion delay and the energy consumption of User Vehicle (UV). Furthermore, in order to minimize the overall system consumption, we establish an adaptive optimal reverse offloading strategy based on Deep Q-Network (DQN). Simulation results demonstrate that the proposed algorithm can effectively reduce the energy consumption and task delay, when compared with the full local and fixed offloading schemes.