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| 基于细粒度数据的智能手机续航时间预测模型 |
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李豁然1,2, 刘譞哲1,2, 梅俏竹3, 梅宏1,2
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1.北京大学 信息科学技术学院 软件研究所, 北京 100871;2.高可信软件技术教育部重点实验室(北京大学), 北京 100871;3.School of Information, University of Michigan, Ann Arbor, Michigan 48109, USA
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| 摘要: |
| 如今，智能手机已成为人们日常生活中重要的组成部分.然而，在智能手机软硬件能力高速发展的同时，智能手机的电池能力却未能取得突破性的进展.这导致电池的续航能力经常会成为用户使用智能手机时的体验瓶颈.为了提高用户使用体验的优良感受，一种可行的方法是为用户提供电池续航时间预测.准确的电池续航时间预测能够帮助用户更加高效地规划其使用，从而能够改善其使用体验.由于缺乏高质量数据的支持，现有的电池续航时间预测方法通常比较简单，较难在真实场景下发挥实际用途.为了解决这一问题，基于一组细粒度大规模真实用户数据集，提出了一个智能手机电池续航时间预测模型.为了验证模型的效果，基于51名用户21个月内的细粒度使用数据进行了实验验证.结果显示：用户在发起查询时的使用行为、在当前会话内的使用行为以及其历史使用习惯上，均能够不同程度地帮助电池续航时间预测.总体来说，所提出的模型能够显著提升预测准确度. |
| 关键词: 移动计算 电池续航时间预测 机器学习 |
| DOI：10.13328/j.cnki.jos.006199 |
| 分类号:TP311 |
| 基金项目:国家杰出青年科学基金（61725201）；北京高校卓越青年科学家计划（BJJWZYJH01201910001004） |
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| Predicting Smartphone Battery Life by Fine-grained Usage Data |
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LI Huo-Ran1,2, LIU Xuan-Zhe1,2, MEI Qiao-Zhu3, MEI Hong1,2
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1.Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;2.Key Laboratory of High Confidence Software Technologies of Ministry of Education(Peking University), Beijing 100871, China;3.School of Information, University of Michigan, Ann Arbor, Michigan 48109, USA
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| Abstract: |
| Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of devices and apps. As a result, battery has always been a bottleneck of a user's daily experience of smartphones. An accurate estimation of the remaining battery life could tremendously help the user to schedule their activities and use their smartphones more efficiently. Existing studies on battery life prediction have been primitive due to the lack of real-world smartphone usage data at scale. This paper presents a novel method that uses the state-of-the-art machine learning models for battery life prediction, based on comprehensive and real-time usage traces collected from smartphones. The method is evaluated using a dataset collected from 51 users for 21 months, which covers comprehensive and fine-grained smartphone usage traces including system status, sensor indicators, system events, and app status. We find that the battery life of a smartphone can be accurately predicted based on how the user uses the device at the real-time, in the current session, and in history. As a conclusion, the proposed model could significantly raise the prediction accuracy. |
| Key words: mobile computing battery lifetime prediction machine learning |
