Enhancing Task Offloading in IoV With a Two-Stage Algorithm Under Information Asymmetry

In the Internet of Vehicles (IoV), task offloading is crucial for enhancing computational efficiency and managing resource constraints. However, the presence of information asymmetry and the inherent selfish behavior of vehicles pose significant challenges to task offloading in IoV. Thus, to improve the reliability and efficiency of offloading in scenarios with information asymmetry, an innovative two-stage offloading strategy that employs reinforcement learning to optimize task allocation and scheduling is introduced in this paper. To be specific, the first stage in the proposed offloading strategy involves an initial task allocation strategy based on task attributes, the state of the intermediate buffer queue, and the maximum task reception capacity of Roadside Units (RSUs). The second stage explores partial offloading strategies, investigating the impact of intermediate incentives on offloading decisions. By focusing on maximizing social utility, the destinations, offloading rates, and advantageous portions of intermediate incentives for partial offloading tasks are determined in this paper as well. Moreover, the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms are used to solve the optimization problem efficiently. Finally, simulation results demonstrate that the proposed innovative strategy significantly outperforms traditional approaches in terms of task completion reliability and efficiency, providing a robust solution for IoV scenarios with information asymmetry.