A Computing Offloading Resource Allocation Scheme Using Deep Reinforcement Learning in Mobile Edge Computing Systems

Aiming at the problems of increased latency and energy consumption and decreased service quality caused by current vehicle networks, this paper proposes a computing offloading resource allocation strategy based on deep reinforcement learning in Internet of Vehicles. Firstly, the system architecture for Internet of Vehicles is designed, calculation model and communication model of computing offloading strategy are constructed. Then, the resource allocation problem in offloading process is studied for real-time energy-aware offloading scheme in mobile edge computing. Besides, considering the battery capacity of vehicle users, the remaining energy rate is utilized to redefine weighting factors to sense energy consumption in real time. Finally, with the shortest delay and smallest computational cost as optimization goals, Q-learning is used to achieve the optimization of offloading strategy, that is, the optimal allocation of communication and computing resources, and the best system security. The simulation results show that the delay of the proposed algorithm is 0.442 s when the computational complexity is 9000 cycles/byte, and the performance of the delay is improved compared with the other three algorithms.

Automated summary

This paper proposes a computing offloading resource allocation strategy based on deep reinforcement learning in Internet of Vehicles, aiming to improve system delay performance and achieve optimal resource allocation and system security through redefining weighting factors and using Q-learning for optimization.