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Distributed On-Demand Routing Algorithm With Graph Representation Learning for Industrial IoT
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AI
DOI：10.1109/TNSE.2024.3496438.png)
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Abstract
En 中文
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.
Keywords:
Routing
Quality of service
Scalability
Industrial Internet of Things
Network topology
Delays
Vectors
Topology
Representation learning
Heuristic algorithms
Graph representation learning
quality of service
deep reinforcement learning
routing optimization
industrial Internet-of-Things
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K
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