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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| Vydané v: | IEEE transactions on network science and engineering Ročník 12; číslo 1; s. 332 - 343 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
IEEE
01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| 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. |
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| 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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