Abstract
The demand for the Internet of Everything has slowed down network routing efficiency. Traditional routing policies rely on manual configuration, which has limitations and adversely affects network performance. In this paper, we propose an Internet of Things (IoT) Intelligent Edge Network Routing (ENIR) architecture. ENIR uses deep reinforcement learning (DRL) to simulate human learning of empirical knowledge and an intelligent routing closed-loop control mechanism for real-time interaction with the network environment. According to the network demand and environmental conditions, the method can dynamically adjust network resources and perform intelligent routing optimization. It uses blockchain technology to share network knowledge and global optimization of network routing. The intelligent routing 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).
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