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

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
Main Authors: Thomas Huybrechts, Philippe Reiter, Siegfried Mercelis, Jeroen Famaey, Steven Latré, Peter Hellinckx
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Energies
Subjects:
batteryless devices
Worst-Case Energy Consumption
hybrid resource consumption analysis
machine learning
automated testbench
Internet-of-Things
Online Access:https://www.mdpi.com/1996-1073/14/13/3914

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https://www.mdpi.com/1996-1073/14/13/3914

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