移动可中断性推理中能量交易的准确性

Aleksandar Cuculoski, V. Pejović
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引用次数: 0

摘要

来自移动设备的不合时宜的干扰可能会对我们的工作表现、压力和幸福感产生重大影响,在关键情况下,比如开车时,甚至会造成致命的后果。推断移动用户的可中断性的最先进方法是利用我们设备上可用的一系列传感器。然而,这些传感器的能量消耗与保护设备最宝贵的资源——电池电量的需求相冲突。在这项工作中,我们重新审视了基于传感器的可中断性推理方法,并检查了传感器的能量使用与其对可中断性建模的贡献之间的权衡。我们的发现,基于对14个用户进行的为期两周的实地研究表明,打开额外的传感器确实可以提高可中断性推断,但代价是增加能源消耗。然后,我们提出了一个可中断性管理系统,该系统使用分类器置信度作为旋钮,允许沿着权衡前沿进行细粒度调整,从而实现特定于用户和应用程序的能量优化可中断性管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trading energy for accuracy in mobile interruptiblity inference
Untimely interruptions from our mobile devices may have a significant impact on our work performance, stress and well-being, and in critical situations, such as when driving, can even have fatal consequences. State of the art approaches to inferring interruptiblity of mobile users harness an array of sensors available on our devices. Yet, the energy consumption of these sensors clashes with the need to preserve the most precious of the device's resources - its battery charge. In this work we revisit the sensor-based approach to interruptiblity inference and examine the trade-off between a sensor's energy use and its contribution to interruptiblity modelling. Our findings, based on a two week long field study with 14 users demonstrate that turning on additional sensors indeed improves interruptiblity inference, but at a cost of increased energy consumption. We then propose an interruptiblity management systems that uses the classifier confidence as a knob allowing fine-grain tuning along the trade-off front, thus enabling user- and application- specific energy-optimal interruptiblity management.
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