Task offloading decision making for IoV based on deep reinforcement learning

With the popularization and development of in-vehicle applications, the limitations of computing resources, storage resources, and energy on vehicles have become increasingly prominent. To meet the growing demand for compute-intensive applications, cloud-edge collaborative computing has emerged as a key scheme. However, existing challenges still urgently need to be addressed: current task offloading schemes under cloud-edge collaboration are generally limited to the assumption of full offloading, failing to address the demand for partial offloading in practical scenarios such as segmented data processing in autonomous driving and this makes it difficult to determine the optimal offloading rate. Furthermore, most schemes fail to establish a priority model based on the resource requirements of tasks, struggling to balance efficient offloading and rational resource allocation.To address these issues, this paper designs a communication model, an energy consumption model, a cost model, a priority model, and a task offloading model. It also proposes a task offloading decision scheme based on deep reinforcement learning algorithms, enabling the selection of optimal offloading strategies in dynamic environments. Experimental results demonstrate that in comparison with existing schemes reported in the literature, the proposed scheme achieves significantly optimized performance. After the algorithm converges, Compared with DQN-based scheme and DDPG-based scheme, IDDPG-based scheme has reduced latency by 59.46% and 67.39%, reduced energy consumption by 18.37% and 11.76% respectively.