Abstract

Abstract

Abstract

Abstract
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.

Abstract