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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Veröffentlicht in:	IEEE transactions on network science and engineering Jg. 12; H. 1; S. 332 - 343
Hauptverfasser:	Dai, Bin, Li, Hetao, Huang, Wenrui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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