Task Distribution Offloading Algorithm Based on DQN for Sustainable Vehicle Edge Network

The edge access component of the Internet of Vehicles has a high computational rate and energy consumption. This paper proposes a distribution offloading algorithm based on deep Q-learning network (DQN) to achieve the best latency and sustainable scheduling. Firstly, the computational tasks of various vehicles are prioritized using the analytic hierarchy process (AHP) to assign different weights to the task processing rate in order to establish a relationship model. Secondly, by introducing edge computing based on DQN, the task offloading model is established by using the weighted sum of task processing rate as the optimization goal, which realizes the long-term utility of offloading strategies. The performance evaluation results show that, when compared to the Q-learning algorithm, the proposed method can reduce the average task processing delay by 17%, effectively improving the sustainable task offload efficiency.