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 t...

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Published in:IEEE transactions on network science and engineering Vol. 12; no. 1; pp. 332 - 343
Main Authors: Dai, Bin, Li, Hetao, Huang, Wenrui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Deep learning
deep reinforcement learning
Delays
Graph representation learning
Graphical representations
Heuristic algorithms
Industrial applications
Industrial Internet of Things
Machine learning
Network topology
Quality of service
Quality of service architectures
Representation learning
Routing
routing optimization
Scalability
Topology
Vectors
ISSN:2327-4697, 2334-329X
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Abstract 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.
AbstractList 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.
Author Huang, Wenrui
Dai, Bin
Li, Hetao
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SubjectTerms Algorithms
Deep learning
deep reinforcement learning
Delays
Graph representation learning
Graphical representations
Heuristic algorithms
Industrial applications
Industrial Internet of Things
Machine learning
Network topology
Quality of service
Quality of service architectures
Representation learning
Routing
routing optimization
Scalability
Topology
Vectors
Title Distributed On-Demand Routing Algorithm With Graph Representation Learning for Industrial IoT
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Volume 12
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