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

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...

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Bibliographic Details
Published inEnergies (Basel) Vol. 14; no. 13; p. 3914
Main Authors Huybrechts, Thomas, Reiter, Philippe, Mercelis, Siegfried, Famaey, Jeroen, Latré, Steven, Hellinckx, Peter
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2021
Subjects
automated testbench
Automation
batteryless devices
Energy budget
Energy consumption
Energy harvesting
Energy management
Environmental impact
hybrid resource consumption analysis
Internet of Things
Learning algorithms
Machine learning
Pessimism
Regression analysis
Schedules
Sensors
Software
Upper bounds
Worst-Case Energy Consumption
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14133914