Dynamic IoT Resource Allocation Using Graph Reinforcement Learning with Hypergraph Convolutions

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.