Distributed On-Demand Routing Algorithm with Graph Representation Learning for Industrial IoT

Emerging industrial Internet-of-Things (IoT) applications demand diverse and critical Quality of Service (QoS). Deep reinforcement learning (DRL)-based routing approaches offer promise but struggle with scalability and convergence, particularly when dealing with graph-based network information. To tackle the challenge, we propose a distributed routing model that leverages graph representation learning (GRL) to learn the optimal routing decision in a distributed manner. We further present on-demand routing algorithms composed of graph representation learning (GRL)-based feature engineering and DRL-based routing decision-making to meet differential QoS requirements. Experimental results demonstrate our approach outperforms state-of-the-art DRL-based routing algorithms in a distributed manner, particularly in large-scale and heavy-load networks.