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Quantum Inspired Task Optimization for IoT Edge Fog Computing Environment

Author

Listed:
  • Tariq Ahamed Ahanger

    (Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Fadl Dahan

    (Department of Management Information Systems, College of Business Administration—Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    Department of Computer Sciences, Faculty of Computing and Information Technology Alturbah, Taiz University, Taiz 9674, Yemen)

  • Usman Tariq

    (Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Imdad Ullah

    (College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

Abstract

IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computing nodes has been a major challenge. State-of-the-art task allocation techniques such as Min–Max, Minimum Completion time, and Round Robin perform task allocation, butv several limitations persist including large energy consumption, delay, and error rate. Henceforth, the current work provides a Quantum Computing-inspired optimization technique for efficient task allocation in an Edge Computing environment for real-time IoT applications. Furthermore, the QC-Neural Network Model is employed for predicting optimal computing nodes for delivering real-time services. To acquire the performance enhancement, simulations were performed by employing 6, 10, 14, and 20 Edge nodes at different times to schedule more than 600 heterogeneous tasks. Empirical results show that an average improvement of 5.02% was registered for prediction efficiency. Similarly, the error reduction of 2.03% was acquired in comparison to state-of-the-art techniques.

Suggested Citation

  • Tariq Ahamed Ahanger & Fadl Dahan & Usman Tariq & Imdad Ullah, 2022. "Quantum Inspired Task Optimization for IoT Edge Fog Computing Environment," Mathematics, MDPI, vol. 11(1), pages 1-28, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:156-:d:1017991
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    References listed on IDEAS

    as
    1. Yangyang Li & Zhenghan Chen & Yang Wang & Licheng Jiao & Yu Xue, 2017. "A Novel Distributed Quantum-Behaved Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2017, pages 1-9, May.
    2. Taher M. Ghazal & Mohammad Kamrul Hasan & Muhammad Turki Alshurideh & Haitham M. Alzoubi & Munir Ahmad & Syed Shehryar Akbar & Barween Al Kurdi & Iman A. Akour, 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review," Future Internet, MDPI, vol. 13(8), pages 1-19, August.
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