Data driven smartphone energy level prediction (original) (raw)

An empirical approach to smartphone energy level prediction

Proceedings of the 13th international conference on Ubiquitous computing, 2011

We conduct a large-scale user study to measure the energy consumption characteristics of 20,100 BlackBerry smartphone users. Our dataset is several orders of magnitude larger than any previous work. We use this dataset to build the Energy Emulation Toolkit (EET) that allows developers to evaluate the energy consumption requirements of their applications against real users' energy traces. The EET computes the successful execution rate of energy-intensive applications across all users, specific devices, and specific smartphone user types. We also consider active adaptation to energy constraints. By classifying smartphone users based on their charging characteristics we demonstrate that energy level can be predicted within 72% accuracy a full day in advance, and through an Energy Management Oracle energy intensive applications can adapt their execution to achieve a near optimal successful execution rate.

GreenHub Farmer: Real-World Data for Android Energy Mining

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), 2019

As mobile devices are supporting more and more of our daily activities, it is vital to widen their battery up-time as much as possible. In fact, according to the Wall Street Journal, 9/10 users suffer from low battery anxiety. The goal of our work is to understand how Android usage, apps, operating systems, hardware and user habits influence battery lifespan. Our strategy is to collect anonymous raw data from devices all over the world, through a mobile app, build and analyze a large-scale dataset containing real-world, day-today data, representative of user practices. So far, the dataset we collected includes 12 million+ (anonymous) data samples, across 900+ device brands and 5.000+ models. And, it keeps growing. The data we collect, which is publicly available and by different channels, is sufficiently heterogeneous for supporting studies with a wide range of focuses and research goals, thus opening the opportunity to inform and reshape user habits, and even influence the development of both hardware and software for mobile devices.

Model-based Energy Consumption Prediction for Mobile Applications

Investigating the energy consumption of mobile applications (apps) is becoming a growing software engineering challenge due to the limited battery lifetime of mobile devices. Energy consumption is defined as the power demand integrated over time. Profiling the power demand of an app is a time consuming activity and the results are only valid for the target hardware used during the measurements. The energy consumption is influenced by the resource demands of an app, the hardware on which the app is running, and its workload. This work adapts resource profiles for enterprise applications to predict the energy consumption of mobile apps without the need to own a physical device. Resource profiles are models that represent all aspects influencing the energy consumption of an app. They can be used to predict the energy consumption for different hardware devices and evaluate the overall efficiency of an app. Moreover, the workload can be changed so that the impact of different usage patterns can be investigated. These capabilities lay the foundation for a platform-independent way of quantifying the energy consumption of mobile apps.

Profiling power consumption on mobile devices

The proliferation of mobile devices, and the migration of the information access paradigm to mobile platforms, motivate studies of power consumption behaviors with the purpose of increasing the device battery life. The aim of this work is to profile the power consumption of a Samsung Galaxy I7500 and a Samsung Nexus S, in order to understand how such feature has evolved over the years. We performed two experiments: the first one measures consumption for a set of usage scenarios, which represent common daily user activities, while the second one analyzes a context-aware application with a known source code. The first experiment shows that the most recent device in terms of OS and hardware components shows significantly lower consumption than the least recent one. The second experiment shows that the impact of different configurations of the same application causes a different power consumption behavior on both smartphones. Our results show that hardware improvements and energyaware software applications greatly impact the energy efficiency of mobile devices.

TIDE: A User-centric Tool for Identifying Energy Hungry Applications on Smartphones

2015 IEEE 35th International Conference on Distributed Computing Systems, 2015

Today, many smartphone users are unaware of what applications (apps) they should stop using to prevent their battery from running out quickly. The problem is identifying such apps is hard due to the fact that there exist hundreds of thousands of apps and their impact on the battery is not well understood. We show via extensive measurement studies that the impact of an app on battery consumption depends on both environmental (wireless) factors and usage patterns. Based on this, we argue that there exists a critical need for a tool that allows a user to (a) identify apps that are energy hungry, and (b) understand why an app is consuming energy, on her phone. Towards addressing this need, we present TIDE, a tool to detect high energy apps on any particular smartphone. TIDE's key characteristic is that it accounts for usage-centric information while identifying energy hungry apps from among a multitude of apps that run simultaneously on a user's phone. Our evaluation of TIDE on a testbed of Android-based smartphones, using weeklong smartphone usage traces from 17 real users, shows that TIDE correctly identifies over 94% of energy-hungry apps and has a false positive rate of < 6%.

A Method for Characterizing Energy Consumption in Android Smartphones

Cellular phones and tablets are ubiquitous, with a market penetration that is counted in millions of active users and units sold. The increasing computing capabilities and strict autonomy requirements on mobile devices drive a particular concern on energy utilization and optimization of this kind of equipment. In this paper, we investigate an approach to relate the energy consumption of smartphones with the operational status of the device, surveying parameters exposed by the operating system using an Android application. Our goal is to explore the means to expand the information that may help to produce more reliable measurements that can be used in further research for designing energy optimization profiles for mobile devices and identify optimization needs.

MLStar: Machine Learning in Energy Profile Estimation of Android Apps

Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2018

Improving the energy efficiency of smartphones is critical for increasing the utility that they provide to the users. With most mobile operating systems, users are responsible for managing their phone's battery efficiency by utilizing the various settings provided by the operating system, as well as selecting energy-efficient apps. However, current app marketplaces do not provide users with information about app energy efficiency, which makes it challenging for the user to make informed decision when selecting an app. This paper presents a novel machine learning approach to estimate app energy efficiency by utilizing textual information available in the Google Play store such as an app's description, user reviews, as well as system permissions. Our detailed analysis of the resulting system shows that hardware permissions, app description, and user reviews correlate well with energy efficiency ratings. We evaluate five models that represent popular classes of machine learning...

Exhausting battery statistics: understanding the energy demands on mobile handsets

Proceedings of the …, 2010

Despite the advances in battery technologies, mobile phones still suffer from severe energy limitations. Modern handsets are rich devices that can support multitasking thanks to their high processing power and provide a wide range of resources such as sensors and network interfaces with different energy demands. There have been multiple attempts to characterise those energy demands; both to save or to allocate energy to the applications on the handset. However, there is still little understanding on how the interdependencies between resources (interdependencies caused by the applications and users' behaviour) affect the battery life. In this paper, we demonstrate the necessity of considering all those dynamics in order to characterise the energy demands of the system accurately. These results indicate that simple algorithmic and rulebased scheduling techniques are not the most appropriate way of managing the resources since their usage can be affected by contextual factors, making necessary to find customised solutions that consider each user's behaviour and handset features.

User-Centric Prediction for Battery Lifetime of Mobile Devices

2008

Today, mobile devices are being used for various applications such as making voice/video calls, browsing Internet and so on. The operating time and battery consumption spent in those activities affect the battery life of mobile devices. In this paper, we propose a method for predicting the battery lifetime of mobile devices based on usage patterns. We define the possible states of mobile devices based on their operating functions and develop a method of predicting battery lifetime based on average battery consumption and duration of each state.

Empowering developers to estimate app energy consumption

2012

Battery life is a critical performance and user experience metric on mobile devices. However, it is difficult for app developers to measure the energy used by their apps, and to explore how energy use might change with conditions that vary outside of the developer's control such as network congestion, choice of mobile operator, and user settings for screen brightness. We present an energy emulation tool that allows developers to estimate the energy use for their mobile apps on their development workstation itself. The proposed techniques scale the emulated resources including the processing speed and network characteristics to match the app behavior to that on a real mobile device. We also enable exploring multiple operating conditions that the developers cannot easily reproduce in their lab. The estimation of energy relies on power models for various components, and we also add new power models for components not modeled in prior works such as AMOLED displays. We also present a prototype implementation of this tool and evaluate it through comparisons with real device energy measurements.