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An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm

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Abstract

Nowadays, cloud and fog computing have been leveraged to enhance Internet of Things (IoT) performance. The outstanding potential of cloud platforms accelerates the processing and storage of aggregated big data from IoT equipment. Emerging fog-based schemes can improve service quality to IoT applications and mitigate excessive delays and security challenges. Also, since energy consumption can directly cause CO2 emissions from fog and cloud nodes, an efficient task scheduling method reduces energy consumption. In this regard, the growing need for an efficient task scheduling mechanism considering the optimal management of IoT resources is increasingly felt. IoT's task scheduling based on fog-cloud computing plays a crucial role in responding to users' requests. Optimal task scheduling can improve system performance. Therefore, this study uses an IoT task request scheduling method on resources by the Multi-Objective Moth-Flame Optimization (MOMFO) algorithm. It enhances the quality of IoT services based on fog-cloud computing to reduce task requests' completion and system throughput times and energy consumption. If energy consumption is diminished, the percentage of CO2 emissions is also reduced. Then, the proposed scheduling method to solve the task scheduling problem is evaluated using the datasets. A comparison between the proposed scheme and Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Salp Swarm Algorithms (SSA), Harris Hawks Optimizer (HHO), and Artificial Bee Colony (ABC) is performed to assess the performance. According to experiments, the proposed solution has reduced the completion time of IoT tasks and throughput time, thus cutting down the delay due to the processing of tasks, energy consumption, and CO2 emissions and increasing the system's performance rate.

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran

    Taybeh Salehnia & Saeid Raziani

  2. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Ali Seyfollahi & Ali Ghaffari

  3. Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

    Azad Noori

  4. Department of Computer Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Türkiye

    Ali Ghaffari

  5. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Anas Ratib Alsoud & Laith Abualigah

  6. Computer Science Department, Faculty for Information Technology, Al Al-Bayt University, Prince Hussein Bin Abdullah, Mafraq, 25113, Jordan

    Laith Abualigah

  7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  8. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  9. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  10. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  11. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

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  1. Taybeh Salehnia
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Salehnia, T., Seyfollahi, A., Raziani, S. et al. An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm. Multimed Tools Appl 83, 34351–34372 (2024). https://doi.org/10.1007/s11042-023-16971-w

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