Dynamic IoT Resource Allocation Using Graph Reinforcement Learning with Hypergraph Convolutions
Zhang, Shilong Liu, Tong Chen, Jinhua Aboya Messou, Franck Junior Yu, Keping Guizani, Mohsen
Zhang, Shilong
Liu, Tong
Chen, Jinhua
Aboya Messou, Franck Junior
Yu, Keping
Guizani, Mohsen
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Dynamic resource allocation is crucial for sustaining optimal network performance in Internet of Things (IoT) environments. The frequent arrival and departure of devices result in dynamic topology changes, which pose significant challenges to effective resource allocation. To address these limitations, this study introduces a method that leverages hypergraph modeling to explicitly characterize multi-node resource collision relationships and proposes a graph reinforcement learning with hypergraph convolutions for dynamic resource allocation. Experimental evaluations indicate that the proposed method outperforms compared approaches in channel allocation efficiency and resource utilization.
Citation
S. Zhang, T. Liu, J. Chen, F. J. Aboya Messou, K. Yu and M. Guizani, "Dynamic IoT Resource Allocation Using Graph Reinforcement Learning with Hypergraph Convolutions," 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring), Oslo, Norway, 2025, pp. 1-5, doi: 10.1109/VTC2025-Spring65109.2025.11174513.
Source
Proceedings of the IEEE 101st Vehicular Technology Conference
Conference
2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)
Keywords
Graph Reinforcement Learning, Hypergraph Convolutional Network, Time-varying Topology, Internet of Things, Resource Allocation
Subjects
Source
2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)
Publisher
IEEE