Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning

Compared with the traditional network tasks, the emerging Internet of Vehicles (IoV) technology has higher requirements for network bandwidth and delay. However, due to the limitation of computing resources and battery capacity of existing mobile devices, it is hard to meet the above requirements. How to complete task offloading and calculation with lower task delay and lower energy consumption is the most important issue. Aiming at the task offloading system of the IoV, this paper considers the situation of multiple MEC servers when modeling, and proposes a dynamic task offloading scheme based on deep reinforcement learning. It improves the traditional Q-Learning algorithm and combines deep learning with reinforcement learning to avoid dimensional disaster in the Q-Learning algorithm. Simulation results show that the proposed algorithm has better performance on delay, energy consumption, and total system overhead under the different number of tasks and wireless channel bandwidth.

Automated summary

The paper discusses the higher requirements for network bandwidth and delay in the emerging Internet of Vehicles (IoV) technology compared to traditional network tasks. It focuses on the important issue of completing task offloading and calculation with lower task delay and lower energy consumption. By considering multiple MEC servers and proposing a dynamic task offloading scheme based on deep reinforcement learning, the paper improves upon the traditional Q-Learning algorithm and shows better performance in delay, energy consumption, and total system overhead through simulation results.