2024
Macias, Juan Emilio Zurita; Trilles-Oliver, Sergio
Machine learning-based prediction model for battery levels in IoT devices using meteorological variables Journal Article
In: Internet of Things, vol. 25, pp. 101109, 2024, ISSN: 2542-6605, (2024-03).
Abstract | Links | BibTeX | Tags: battery level prediction, Internet of things, machine learning
@article{Zurita2024a,
title = {Machine learning-based prediction model for battery levels in IoT devices using meteorological variables},
author = {Juan Emilio Zurita Macias and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1016/j.iot.2024.101109},
issn = {2542-6605},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Internet of Things},
volume = {25},
pages = {101109},
publisher = {Elsevier},
abstract = {Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.},
note = {2024-03},
keywords = {battery level prediction, Internet of things, machine learning},
pubstate = {published},
tppubtype = {article}
}
Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics.