Spectrum resource sharing method for IoT based on graph matching algorithm

Area of Science:

• Wireless communication
• Network resource management
• Graph theory applications

Background:

• Increasing demand for spectrum resources driven by Internet of Things (IoT) expansion.
• Limitations of current spectrum sharing methods, including low efficiency and poor performance in complex scenarios.

Purpose of the Study:

• To propose an efficient and accurate spectrum resource sharing method for the Internet of Things.
• To address the challenges of low sharing efficiency and complex environments in existing methods.

Main Methods:

• Development of a novel spectrum resource sharing method for IoT.
• Integration of bipartite graph matching, hypergraph matching, and auction theory.
• Utilizing a graph matching algorithm for resource allocation and sharing.

Main Results:

• Achieved accuracy exceeding 92.3%, with a curve value of 0.931 and Coefficient of Determination of 0.92.
• Demonstrated high accuracy and robustness across various conditions, including delay constraints, sample sizes, and malicious environments.
• Reported a resource reuse rate above 58.2% and efficient processing time (41.2 bps for 25 samples).

Conclusions:

• The proposed graph matching algorithm offers significant advantages for practical spectrum resource sharing in IoT.
• The method effectively handles complex environments and maintains high accuracy and efficiency.
• The algorithm provides a robust solution for optimizing spectrum resource allocation and reuse.