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Communication Dans Un Congrès Année : 2016

Device Context Classification for Mobile Power Consumption Reduction

Résumé

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.
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Dates et versions

hal-03383100 , version 1 (18-10-2021)

Identifiants

Citer

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⟩
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