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Device Context Classification for Mobile Power Consumption Reduction
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DOI：10.1109/DSD.2016.102.png)
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Abstract
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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.
Keywords:
Power consumption
classification
Machine Learning
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