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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.
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Submitted on : Monday, October 18, 2021 - 2:30:15 PM
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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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