Mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning in internet of vehicles

Mobile edge computing has been deeply integrated with internet of vehicles (IoV) due to its efficient computing capabilities close to devices. However, the inefficiency of storage and computing capabilities for vehicle terminals is in conflict with the diversification of network application services, which poses a huge challenge to the high-performance computing in IoV. In response to the high-performance computing requirements, a mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning is proposed in order to solve low terminal storage capacity and diversified network service problems. Firstly, taking the energy consumption and transmission bandwidth of vehicle terminals as constraints, this paper establishes a task offloading and resource allocation model based on mathematical model using in-vehicle communication network. Besides, the model takes the maximum task processing rate as the objective function. Secondly, the AHP-DQN framework is used to solve the model, and the optimization variables are allocated according to the real-time state of the network to ensure the better performance of the task allocation algorithm in the multi-user scenario of IoV. Finally, simulation experiments show that the proposed algorithm can effectively realize the effective distribution of computing tasks in IoV.

Automated summary

A mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning is proposed to address the challenges of low terminal storage capacity and diversified network services in IoV. By establishing a mathematical model and using the AHP-DQN framework for optimization, the effective distribution of computing tasks in IoV is achieved, especially in multi-user scenarios.