Skip to Main content Skip to Navigation
Conference papers

Device Context Classification for Mobile Power Consumption Reduction

Abstract : The diverse range of wireless interfaces, sensors, processing components added to the increasing popularity of power-hungry applications reduce the battery life of mobile devices. This paper proposes a tool for identifying the device context, understanding the user habits and preferences in order to adjust available resources and find trade-off between the power consumption and the user satisfaction. We use Machine Learning (ML) methods to identify and classify user/device contexts. On this basis, a software is developed to control at run-time system component activities. When applied only for the screen brightness level knob, the proposed solution can lower the power consumption by up to 20% vs. the out-of-the-box OS brightness manager with a negligible energy overhead.
Document type :
Conference papers
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03383100
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Monday, October 18, 2021 - 2:30:15 PM
Last modification on : Friday, November 5, 2021 - 3:30:14 PM

Identifiers

Collections

Citation

Ismat Chaib Draa, Maroua Nouiri, Smail Niar, Abdelghani Bekrar. Device Context Classification for Mobile Power Consumption Reduction. 2016 Euromicro Conference on Digital System Design (DSD), Aug 2016, Limassol, Cyprus. pp.682-685, ⟨10.1109/DSD.2016.102⟩. ⟨hal-03383100⟩

Share

Metrics

Record views

19