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

Deok-Won Yun1, Won-Cheol Lee2

  • 1Department of Electronic Engineering, Soongsil University, Seoul 06978, Korea.

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Summary

Intelligent dynamic spectrum resource management using data mining and case-based reasoning improves efficiency. This approach reduces complexity for faster, reliable real-time spectrum allocation in industrial IoT environments.

Keywords:
artificial neural networkcase-based reasoningcognitive radiodata miningdata preprocessinginterference analysisspectrum management

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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.