Context

The development of solutions to improve battery life in Android smartphones and the energy efficiency of apps running on them is hindered by diversity. There are more than 24k Android smartphone models in the world. Moreover, there are multiple active operating system versions, and a myriad application usage profiles.

Objective

In such a high-diversity scenario, profiling for energy has only limited applicability. One would need to obtain information about energy use in real usage scenarios to make informed, effective decisions about energy optimization. The goal of our work is to understand how Android usage, apps, operating systems, hardware, and user habits influence battery lifespan.

Method

We leverage crowdsourcing to collect information about energy in real-world usage scenarios. This data is collected by a mobile app, which we developed and made available to the public through Google Play store, and periodically uploaded to a centralized server and made publicly available to researchers, app developers, and smartphone manufacturers through multiple channels (SQL, REST API, zipped CSV/Parquet dump).

Results

This paper presents the results of a wide analysis of the tendency several smart-phone characteristics have on the battery charge/discharge rate, such as the different models, brands, networks, settings, applications, and even countries. Our analysis was performed over the crowdsourced data, and we have presented findings such as which applications tend to be around when battery consumption is the highest, do users from different countries have the same battery usage, and even showcase methods to help developers find and improve energy inefficient processes. The dataset we considered is sizable; it comprises 23+ million (anonymous) data samples stemming from a large number of installations of the mobile app. Moreover, it includes 700+ million data points pertaining to processes running on these devices. In addition, the dataset is diverse. It covers 1.6k+ device brands, 11.8k+ smartphone models, and more than 50 Android versions. We have been using this dataset to perform multiple analyses. For example, we studied what are the most common apps running on these smartphones and related the presence of those apps in memory with the battery discharge rate of these devices. We have also used this dataset in teaching, having had students practicing data analysis and machine learning techniques for relating energy consumption/charging rates with many other hardware and software qualities, attributes and user behaviors.

Conclusions

The dataset we considered can support studies with a wide range of research goals, be those energy efficiency or not. It opens the opportunity to inform and reshape user habits, and even influence the development of both hardware (manufacturers) and software (developers) for mobile devices. Our analysis also shows results which go outside of the common perception of what impacts battery consumption in real-world usage, while exposing new varied, complex, and promising research avenues.

中文翻译：

---

GreenHub：大规模协作数据集，用于分析Android设备的电池消耗

语境

多样性阻碍了改善Android智能手机电池寿命的解决方案的开发以及在其上运行的应用程序的能源效率。世界上有超过2万4千个Android智能手机型号。此外，有多个活动的操作系统版本，以及无数的应用程序使用情况配置文件。

客观的

在这种高多样性的情况下，能源配置文件的适用性有限。人们将需要获取有关实际使用场景中能源使用的信息，以便做出有关能源优化的明智，有效的决策。我们的工作目标是了解Android使用情况，应用程序，操作系统，硬件和用户习惯如何影响电池寿命。

方法

我们利用众包来收集有关实际使用情况下的能源信息。这些数据由移动应用收集，我们开发并通过Google Play商店将其提供给公众，然后定期上传到中央服务器，并通过多种渠道（SQL，REST）向研究人员，应用开发人员和智能手机制造商公开提供API，压缩CSV / Parquet dump）。

结果

本文介绍了对多种智能手机特性对电池充/放电率趋势的广泛分析结果，例如不同型号，品牌，网络，设置，应用，甚至国家/地区。我们的分析是基于众包数据进行的，我们提出了一些发现，例如电池消耗最高时倾向于哪些应用程序，不同国家的用户使用相同的电池使用率，甚至展示了帮助开发人员寻找和改进的方法能源效率低下的流程。我们认为的数据集相当大; 它包含来自移动应用程序大量安装的23+百万（匿名）数据样本。此外，它包括与这些设备上运行的进程有关的700+百万个数据点。此外，数据集是多种多样的。它涵盖了1.6k +个设备品牌，11.8k +智能手机型号以及50多个Android版本。我们一直在使用此数据集执行多次分析。例如，我们研究了在这些智能手机上运行的最常见的应用程序是什么，并将这些应用程序在内存中的存在与这些设备的电池放电率相关联。我们还在教学中使用了该数据集，让学生练习数据分析和机器学习技术，以将能耗/充电率与许多其他硬件和软件质量，属性和用户行为相关联。

结论

我们认为的数据集可以支持具有广泛研究目标的研究，无论是否达到能源效率。它为通知和重塑用户习惯，甚至影响移动设备的硬件（制造商）和软件（开发人员）的开发提供了机会。我们的分析还显示了结果，这些结果超出了在实际使用中对电池消耗产生影响的普遍认识，同时揭示了新的变化，复杂和有希望的研究途径。