Machine learning-based prediction model for battery levels in IoT devices using meteorological variables
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Machine learning-based prediction model for battery levels in IoT devices using meteorological variablesData de publicació
2024-04-01Editor
ElsevierISSN
2542-6605Cita bibliogràfica
Juan Emilio Zurita Macias, Sergio Trilles, Machine learning-based prediction model for battery levels in IoT devices using meteorological variables, Internet of Things, Volume 25, 2024, 101109, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2024.101109.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/science/article/pii/S2542660524000519Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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 ... [+]
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. [-]
Publicat a
Internet of Things, Volume 25, 2024.Entitat finançadora
Ministerio de Ciencia e Innovación de España | Agencia Estatal de Investigación
Codi del projecte o subvenció
PID2022-141813OB-I00 | MCIN/AEI/10.13039/501100011033
Drets d'accés
© 2024 Published by Elsevier B.V.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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