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“Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources

In this article, we propose a dataset about the energy consumption of mobile devices that was collected as part of a “Tour de France” with an electrical wheelchair. Part of these data was used to propose a mathematical model based on an experimental methodology of the energy consumed in mobile devic...

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Detalles Bibliográficos
Autores principales: Ibrahim, Hamzaoui Khalil, Soufiane, Dahmani, Pierre, Boulet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548955/
https://www.ncbi.nlm.nih.gov/pubmed/33072823
http://dx.doi.org/10.1016/j.dib.2020.106334
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author Ibrahim, Hamzaoui Khalil
Soufiane, Dahmani
Pierre, Boulet
author_facet Ibrahim, Hamzaoui Khalil
Soufiane, Dahmani
Pierre, Boulet
author_sort Ibrahim, Hamzaoui Khalil
collection PubMed
description In this article, we propose a dataset about the energy consumption of mobile devices that was collected as part of a “Tour de France” with an electrical wheelchair. Part of these data was used to propose a mathematical model based on an experimental methodology of the energy consumed in mobile devices. Based on these precise measurements in a real environment, we have elaborated predictive models of energy consumption. The objective of this paper is to make accessible the data related to these publications to other researchers in several fields of science (computer science, telecommunications, meteorological science, artificial intelligence, statistics...). To our knowledge, this is the first publication of a dataset recording real world energy consumption data in mobile devices.
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spelling pubmed-75489552020-10-16 “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources Ibrahim, Hamzaoui Khalil Soufiane, Dahmani Pierre, Boulet Data Brief Data Article In this article, we propose a dataset about the energy consumption of mobile devices that was collected as part of a “Tour de France” with an electrical wheelchair. Part of these data was used to propose a mathematical model based on an experimental methodology of the energy consumed in mobile devices. Based on these precise measurements in a real environment, we have elaborated predictive models of energy consumption. The objective of this paper is to make accessible the data related to these publications to other researchers in several fields of science (computer science, telecommunications, meteorological science, artificial intelligence, statistics...). To our knowledge, this is the first publication of a dataset recording real world energy consumption data in mobile devices. Elsevier 2020-09-28 /pmc/articles/PMC7548955/ /pubmed/33072823 http://dx.doi.org/10.1016/j.dib.2020.106334 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Ibrahim, Hamzaoui Khalil
Soufiane, Dahmani
Pierre, Boulet
“Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title_full “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title_fullStr “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title_full_unstemmed “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title_short “Tour de France” data for the improvement of energy consumption in devices powered by limited energy sources
title_sort “tour de france” data for the improvement of energy consumption in devices powered by limited energy sources
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548955/
https://www.ncbi.nlm.nih.gov/pubmed/33072823
http://dx.doi.org/10.1016/j.dib.2020.106334
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