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  1. Home
  3. Edge Caching In Fog-based Sensor Networks Through Deep Learning-associated Quantum Computing Framework.
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  3. Edge Caching In Fog-based Sensor Networks Through Deep Learning-associated Quantum Computing Framework.

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Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework.

Tayyabah Hasan1, Fahad Ahmad2, Muhammad Rizwan1

  • 1Department of Computer Sciences, Kinnaird College for Women, Lahore 54700, Punjab, Pakistan.

Computational Intelligence and Neuroscience
|January 17, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances fog computing (FC) by integrating deep learning (DL) with quantum computing (QC) to improve mobile edge computing (MEC) performance. The novel approach boosts cache hit ratios and accelerates content delivery in IoT networks.

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Area of Science:

  • Computer Science
  • Information Technology
  • Quantum Computing

Background:

  • Mobile edge computing (MEC) and fog computing (FC) offer edge services but face performance degradation and quality of service (QoS) issues in Internet of Things (IoT) scenarios.
  • Existing caching strategies struggle to optimize limited storage and accelerate content delivery, necessitating enhanced approaches.

Purpose of the Study:

  • To propose a novel framework merging deep learning (DL) with quantum computing (QC) to improve caching efficiency in fog computing (FC) based sensor networks.
  • To enhance the cache hit ratio and accelerate content delivery in mobile edge computing (MEC) environments.

Main Methods:

  • A deep learning (DL) agent utilizing Self-Organizing Maps (SOMs) algorithm prioritizes caching contents based on trending data.
  • Prioritized content is stored in a Quantum Memory Module (QMM) leveraging the Two-Level Spin Quantum Phenomenon (TLSQP).
  • Quantum parallelism (QP) principles of superposition and entanglement are utilized for efficient resource utilization and content storage.

    • Main Results:

    • The SOMs algorithm identified high and medium-priority content with a low topographic and quantization error (0.0000235) after 750,000 iterations.
    • The integration of DL and QC is shown to improve cache hit ratio by ranking and efficiently storing prioritized content.
    • Reduced resource utilization compared to conventional computing is achieved through quantum parallelism.

    Conclusions:

    • The proposed DL-MEC framework with QMM effectively addresses performance deterioration and QoS degradation in IoT environments.
    • This hybrid approach significantly enhances content delivery speed and optimizes storage management in edge computing.
    • The study demonstrates the potential of quantum computing (QC) to revolutionize caching strategies in next-generation wireless networks.