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Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

Author

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  • Thomas Huybrechts

    (IDLab, Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Philippe Reiter

    (IDLab, Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Siegfried Mercelis

    (IDLab, Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Jeroen Famaey

    (IDLab, Department of Computer Science, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Steven Latré

    (IDLab, Department of Computer Science, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Peter Hellinckx

    (IDLab, Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

Abstract

Batteryless Internet-of-Things (IoT) devices need to schedule tasks on very limited energy budgets from intermittent energy harvesting. Creating an energy-aware scheduler allows the device to schedule tasks in an efficient manner to avoid power loss during execution. To achieve this, we need insight in the Worst-Case Energy Consumption (WCEC) of each schedulable task on the device. Different methodologies exist to determine or approximate the energy consumption. However, these approaches are computationally expensive and infeasible to perform on all type of devices; or are not accurate enough to acquire safe upper bounds. We propose a hybrid methodology that combines machine learning-based prediction on small code sections, called hybrid blocks, with static analysis to combine the predictions to a final upper bound estimation for the WCEC. In this paper, we present our work on an automated testbench for the Code Behaviour Framework (COBRA) that measures and profiles the upper bound energy consumption on the target device. Next, we use the upper bound measurements of the testbench to train eight different regression models that need to predict these upper bounds. The results show promising estimates for three regression models that could potentially be used for the methodology with additional tuning and training.

Suggested Citation

  • Thomas Huybrechts & Philippe Reiter & Siegfried Mercelis & Jeroen Famaey & Steven Latré & Peter Hellinckx, 2021. "Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices," Energies, MDPI, vol. 14(13), pages 1-26, June.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3914-:d:585403
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    References listed on IDEAS

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    1. McManus, M.C., 2012. "Environmental consequences of the use of batteries in low carbon systems: The impact of battery production," Applied Energy, Elsevier, vol. 93(C), pages 288-295.
    2. Shaikh, Faisal Karim & Zeadally, Sherali, 2016. "Energy harvesting in wireless sensor networks: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1041-1054.
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    Cited by:

    1. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.

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