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Context-Aware Energy Enhancements for Smart Mobile Devices

机译:智能移动设备的上下文感知能源增强

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摘要

Within the past decade, mobile computing has morphed into a principal form of human communication, business, and social interaction. Unfortunately, the energy demands of newer ambient intelligence and collaborative technologies on mobile devices have greatly overwhelmed modern energy storage abilities. This paper proposes several novel techniques that exploit spatiotemporal and device context to predict device wireless data and location interface configurations that can optimize energy consumption in mobile devices. These techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression with neural networks, k-nearest neighbor, and support vector machines are explored and compared on synthetic and user traces from real-world usage studies. The experimental results show that up to 90% successful prediction is possible with neural networks and k-nearest neighbor algorithms, improving upon prediction strategies in prior work by approximately 50%. Further, an average improvement of 24% energy savings is achieved compared to state-of-the-art prior work on energy-efficient location-sensing.
机译:在过去的十年中,移动计算已发展成为人类交流,业务和社交互动的主要形式。不幸的是,移动设备上较新的环境智能和协作技术对能源的需求大大淹没了现代能源存储能力。本文提出了几种新颖的技术,它们可以利用时空和设备上下文来预测设备无线数据和位置接口配置,从而可以优化移动设备的能耗。这些技术包括线性判别分析,线性逻辑回归,具有神经网络的非线性逻辑回归,k最近邻和支持向量机的变体,并根据实际使用情况研究对合成和用​​户迹线进行了比较。实验结果表明,使用神经网络和k近邻算法,可以成功进行多达90%的成功预测,比以前的工作中的预测策略提高了约50%。此外,与最新的节能位置感测技术相比,平均节能量可提高24%。

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