Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability

Autores
Longo, Mathias; Hirsch Jofré, Matías Eberardo; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days,without being plugged to the electricity grid. Nonetheless,misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device's computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner's activity and relevant device state variables. Themodel includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a relatedwork. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.
Fil: Longo, Mathias. University of Southern California; Estados Unidos
Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
BATTERY PREDICTION
DEW COMPUTING
FEATURE SELECTION
MACHINE LEARNING
MOBILE CLOUD COMPUTING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/121003
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availabilityLongo, MathiasHirsch Jofré, Matías EberardoMateos Diaz, Cristian MaximilianoZunino Suarez, Alejandro OctavioBATTERY PREDICTIONDEW COMPUTINGFEATURE SELECTIONMACHINE LEARNINGMOBILE CLOUD COMPUTINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days,without being plugged to the electricity grid. Nonetheless,misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device's computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner's activity and relevant device state variables. Themodel includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a relatedwork. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.Fil: Longo, Mathias. University of Southern California; Estados UnidosFil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaMDPI AG2019-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/121003Longo, Mathias; Hirsch Jofré, Matías Eberardo; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability; MDPI AG; Information; 10; 3; 2-2019; 1-172078-2489CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2078-2489/10/3/86info:eu-repo/semantics/altIdentifier/doi/10.3390/info10030086info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-26T09:07:52Zoai:ri.conicet.gov.ar:11336/121003instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-26 09:07:52.681CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
title Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
spellingShingle Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
Longo, Mathias
BATTERY PREDICTION
DEW COMPUTING
FEATURE SELECTION
MACHINE LEARNING
MOBILE CLOUD COMPUTING
title_short Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
title_full Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
title_fullStr Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
title_full_unstemmed Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
title_sort Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability
dc.creator.none.fl_str_mv Longo, Mathias
Hirsch Jofré, Matías Eberardo
Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author Longo, Mathias
author_facet Longo, Mathias
Hirsch Jofré, Matías Eberardo
Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author_role author
author2 Hirsch Jofré, Matías Eberardo
Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author2_role author
author
author
dc.subject.none.fl_str_mv BATTERY PREDICTION
DEW COMPUTING
FEATURE SELECTION
MACHINE LEARNING
MOBILE CLOUD COMPUTING
topic BATTERY PREDICTION
DEW COMPUTING
FEATURE SELECTION
MACHINE LEARNING
MOBILE CLOUD COMPUTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days,without being plugged to the electricity grid. Nonetheless,misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device's computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner's activity and relevant device state variables. Themodel includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a relatedwork. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.
Fil: Longo, Mathias. University of Southern California; Estados Unidos
Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days,without being plugged to the electricity grid. Nonetheless,misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device's computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner's activity and relevant device state variables. Themodel includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a relatedwork. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.
publishDate 2019
dc.date.none.fl_str_mv 2019-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/121003
Longo, Mathias; Hirsch Jofré, Matías Eberardo; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability; MDPI AG; Information; 10; 3; 2-2019; 1-17
2078-2489
CONICET Digital
CONICET
url http://hdl.handle.net/11336/121003
identifier_str_mv Longo, Mathias; Hirsch Jofré, Matías Eberardo; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability; MDPI AG; Information; 10; 3; 2-2019; 1-17
2078-2489
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2078-2489/10/3/86
info:eu-repo/semantics/altIdentifier/doi/10.3390/info10030086
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.011256

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