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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor

Ibrahim Mustapha1,2, Borhanuddin Mohd Ali3, Mohd Fadlee A Rasid4

  • 1Department of Computer and Communications Systems Engineering and Wireless and Photonics Research Centre, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang Selangor, Malaysia. mustib@unimaid.edu.ng.

Sensors (Basel, Switzerland)
|August 20, 2015
PubMed
Summary

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This study introduces a reinforcement learning algorithm for cognitive radio networks to optimize energy efficiency and cooperative sensing. The new method improves Primary User detection and achieves 9% energy savings.

Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • Clustering in wireless networks enhances energy efficiency and cooperative sensing.
  • Energy efficiency in cognitive radio networks requires further investigation.
  • Cooperative sensing is crucial for dynamic channel access.

Purpose of the Study:

  • To develop a reinforcement learning-based spectrum-aware clustering algorithm for cognitive radio networks.
  • To optimize energy consumption and cooperative sensing performance.
  • To address the challenge of energy efficiency in cognitive radio environments.

Main Methods:

  • A reinforcement learning algorithm is proposed for spectrum-aware clustering.
  • Member nodes learn energy and sensing costs for optimal cluster selection.
Keywords:
clusteringcognitive radiocooperative sensingenergy consumptionreinforcement learningwireless sensor network

Related Experiment Videos

Last Updated: Apr 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
  • The problem is formulated as a Markov Decision Process (MDP).

    • Main Results:

    • The algorithm demonstrates convergence, learning, and adaptability in dynamic environments.
    • Performance comparisons show improved Sum of Square Error (SSE) and reduced complexity compared to GWSA.
    • Achieved 9% energy savings and significant Primary User (PU) detection improvement.

    Conclusions:

    • The proposed reinforcement learning algorithm effectively optimizes energy efficiency and cooperative sensing in cognitive radio networks.
    • The algorithm's adaptability makes it suitable for dynamic network conditions.
    • Significant improvements in energy savings and PU detection validate the approach.