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A New Task Scheduling Approach for Energy Conservation in Internet of Things

Author

Listed:
  • Man-Wen Tian

    (National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China)

  • Shu-Rong Yan

    (National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China)

  • Wei Guo

    (School of Credit Management, Guangdong University of Finance, Guangzhou 510521, China)

  • Ardashir Mohammadzadeh

    (Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Ebrahim Ghaderpour

    (Department of Earth Sciences and CERI Research Centre, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185 Rome, Italy
    Earth and Space Inc., Calgary, AB T3A 5B1, Canada)

Abstract

Internet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is found to achieve a high data dissemination rate (8.16%), less energy utilization (10.65%), and reduced latency (11.44%) at different time slots.

Suggested Citation

  • Man-Wen Tian & Shu-Rong Yan & Wei Guo & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2023. "A New Task Scheduling Approach for Energy Conservation in Internet of Things," Energies, MDPI, vol. 16(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2394-:d:1085689
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    Citations

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    Cited by:

    1. Rafał Różycki & Grzegorz Waligóra, 2023. "Energy-Aware Evolutionary Algorithm for Scheduling Jobs of Charging Electric Vehicles in an Autonomous Charging Station," Energies, MDPI, vol. 16(18), pages 1-25, September.
    2. Ziyad Almudayni & Ben Soh & Alice Li, 2024. "IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks," Future Internet, MDPI, vol. 16(3), pages 1-13, March.

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