摘要
The demand for the Internet of Everything has slowed down network routing efficiency.Tradi-tional routing policies rely on manual configuration,which has limitations and adversely affects network performance.In this paper,we propose an Inter-net of Things(IoT)Intelligent Edge Network Routing(ENIR)architecture.ENIR uses deep reinforcement learning(DRL)to simulate human learning of empir-ical knowledge and an intelligent routing closed-loop control mechanism for real-time interaction with the network environment.According to the network de-mand and environmental conditions,the method can dynamically adjust network resources and perform in-telligent routing optimization.It uses blockchain tech-nology to share network knowledge and global op-timization of network routing.The intelligent rout-ing method uses the deep deterministic policy gradient(DDPG)algorithm.Our simulation results show that ENIR provides significantly better link utilization and transmission delay performance than various routing methods(e.g.,open shortest path first,routing based on Q-learning and DRL-based control framework for traffic engineering).
基金
This work has been supported by the Leadingedge Technology Program of Jiangsu Natural Science Foundation(No.BK20202001).
