Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

Area of Science:

• Computer Science
• Electrical Engineering
• Telecommunications

Background:

• Intelligent dynamic spectrum resource management relies on vast sensing data for optimization.
• Existing methods struggle with rapid resource allocation and information optimization.
• Optimization algorithms aim to minimize interference, energy consumption, and manage spectrum handoffs.

Purpose of the Study:

• To implement intelligent dynamic real-time spectrum resource management.
• To reduce the complexity of current spectrum management techniques.
• To enable efficient and rapid real-time resource allocation.

Main Methods:

• Application of data mining and case-based reasoning.
• Analysis of incumbent user activity patterns using industrial IoT sensing data.
• Utilization of a classified case database for efficient resource allocation.

Main Results:

• Demonstrated effectiveness of the proposed intelligent dynamic real-time spectrum resource management.
• Confirmed optimization engine operations and spectrum management capabilities (handoff, latency, energy, link maintenance).
• Achieved minimized complexity and efficient real-time resource allocation.

Conclusions:

• The proposed method minimizes complexity in intelligent dynamic spectrum resource management.
• Efficient real-time resource allocation and reliable communication are maintained.
• The approach demonstrates superior performance compared to existing spectrum management techniques.