Due to the rapid development of the Internet of Things, people's demand for spectrum resources is also rapidly increasing. However, existing spectrum resource sharing methods suffer from low sharing efficiency and difficulty dealing with complex environments. Therefore, this study proposes a spectrum resource sharing method for the Internet of Things based on a graph matching algorithm, which fully combines the advantages of bipartite graph matching, hypergraph matching, and auction theory, and can achieve reasonable allocation and sharing of spectrum resources. The experimental results show that the accuracy of this method can reach over 92.3%, the curve under the working area of the subjects can reach 0.931, and the Coefficient of Determination can reach 0.92, significantly higher than other models. And through verification in actual environments, the proposed algorithm can maintain high accuracy regardless of delay constraints, different sample sizes, or malicious environments. At the same time, its resource reuse rate remains above 58.2%, and when the sample size reaches 25, the required time is only 41.2 bps. Overall, the algorithm proposed by this study can face the challenges of complex environments, maintain high accuracy, and has significant advantages in practical spectrum resource sharing.
Jianxiao Wang (2026) studied this question.