Data Collaboration and Task Offloading Strategies for Edge-Vehicle Networks Based on DQN

With the development of intelligent transportation and Internet of Things technologies, the Edge-Vehicle Network plays a key role in enhancing vehicle computing capabilities and quality of service. This paper focuses on the edge-vehicle network, introduces Deep Q-Network (DQN) methods to achieve efficient data collaboration and task offloading strategies, and proposes the MOTO algorithm. First, this paper constructs a high-reliability model for data collaboration in edge-vehicle networks and derives the delay formula for tasks from mobile vehicle terminals to edge server nodes. Then, tasks are divided into fine-grained categories according to their types and structures. Aiming to minimize delay and under energy consumption constraints, DQN are used to optimize data collaboration and task offloading for vehicles. Finally, by designing the state space, action space, and reward function, the agent can learn optimal resource allocation strategies through continuous exploration and trial-and-error. Experimental results show that the MOTO algorithm has significant advantages over traditional algorithms in reducing task processing delay and improving resource utilization efficiency.