Mobile devices have become one of our
most usedgadgets, with their battery life becoming of a high con-cern for
users. In fact, battery life is traditionally known to beone of the major
factors influencing consumer satisfaction [1],and was just recently pointed
out, on top of usability, storageand durability, as the most important feature
for smartphoneowners [2]. Battery life is such a growing concern that ithas
been shown that 9 of 10 users suffer from low batteryanxiety [3], and is under
discussion as a potential clinicalcondition:nomophobia, the fear of being
without your mobilephone, in the Diagnostic and Statistical Manual of
MentalDisorders [4]. On the other end, developers are also very concerned
withtheir application’s battery life, as excessive battery consump-tion is one
of the most common causes for bad app reviewsin app stores [5], [6]. In fact,
developers are aware of thebattery consumption problem, and many times seek
help insolving this, even if they rarely receive adequate advice [7]–[9].
Mobile brands have actually caught sight of this issue andhave tried to offer
help by publishing developer guides aimedat extending battery life123.Reducing the energy that is
consumed by mobile devicesis also an important problem from a sustainability
point ofview. Indeed, the billions of phones that are in use these dayshave a
global massive environmental footprint, and our digitalconsumption (which
includes but is not limited to mobiledevice usage) is bound to have a greater
impact on globalwarming than the aviation industry [10].Despite its importance,
optimizing, or even analyzing en-ergy consumption for mobile devices is a
difficult and labor-intensive task for both users and/or developers.For once,
developers are using different monitoringtools [11]–[13] according to specific
needs which often re-sults in a non systematized procedure and context
specificfindings [13]–[15]. Monitoring the energy consumed by anapplication
often results in extensive tests under several differ-ent scenarios and devices
[16]–[18], both very time consumingand potentially requiring large initial
investments. Indeed, evenconsidering Android alone, this is already a heavily
heteroge-neous environment, as there exists thousands of potential
com-binations among manufacturers, devices, operating systems,features,
hardware components and apps, for example.For users, understanding the energy
consumption of theirdevices is an even harder exercise. For once, their
knowledgeregarding the hardware behavior is limited to their own de-vices, and
without the proper tools and skills they cannot com-pare the energy behavior of
their apps with others. Moreover, different usage contexts of the same app (e.g.,
within differentOS versions and with different hardware components switchedon)
results in different energy behaviors, and this has to betaken into account
when performing any comparison.In this paper, we present a large dataset which
is repre-sentative of real-world day-to-day usage of Android devices.Our
dataset entries include information such as active sen-sors, memory usage,
battery voltage and temperature, runningapplications, model and manufacturer,
network details, etc,.This raw data was obtained by continuous
crowd-sourcingthrough a mobile application. It is worth noting that all ourdata
is publicly available, while maintaining the anonymityand privacy of all its
users. Indeed, it is impossible to associateany data with the user who
originated it. Thus far, our datasetincludes unique 12 million+ samples, from
900+ differentbrands and 5,000+ models, across 160 countries.This dataset was
gathered within the GreenHub initiative4,acollaborative
approach to energy consumption analysis withinAndroid. Our vision is to use the
gathered data on the usageof mobile devices and application execution to help
analyzeand identify opportunities to optimize energy consumption inAndroid
devices, both for developers and users. Indeed, weexpect that useful
information can be mined from the datasetas to help influence users in adopting
more energy efficientbehaviors and to provide developers with indications of
howefficient their application is and how it compares to others.In the case of
developers, this is expected to triggerfurther analyses which are beyond the
dataset itself.
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