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

Thomas Huybrechts, Philippe Reiter, Siegfried Mercelis, Jeroen Famaey, Steven Latré and Peter Hellinckx
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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

Energies, 2021, vol. 14, issue 13, 1-26

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.

Keywords: batteryless devices; Worst-Case Energy Consumption; hybrid resource consumption analysis; machine learning; automated testbench; Internet-of-Things (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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