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MPower: Gain Back Your Android
Battery Life!
Matteo Ferroni
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
matteo.ferroni@mail.polimi.it
Donatella Sciuto
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
sciuto@elet.polimi.it
Andrea Cazzola
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
andrea.cazzola@mail.polimi.it
Marco D. Santambrogio
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
santambr@elet.polimi.it
Domenico Matteo
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
matteo@elet.polimi.it
Alessandro A. Nacci
Politecnico di Milano
Via Ponzio 34/5
Milano, 20133 IT
nacci@elet.polimi.it
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UbiComp’13 Adjunct, September 8–12, 2013, Zurich, Switzerland.
ACM 978-1-4503-2215-7/13/09.
http://dx.doi.org/10.1145/2494091.2494147
Abstract
Nowadays, mobile devices are becoming more flexible and
rich in functionalities. As already presented in [6] those
devices are highly influenced by constraints, mainly
regarding power management. In fact, mobile batteries
are limited in time and there are no efficient methods able
to manage power consumption. Even knowing the device
Time To Live (TTL) is currently left to the user
experience. In this paper, we presented MPower, a system
able to predict the mobile device TTL, providing also the
user with suggestions on the optimal device configuration
w.r.t. the desired TTL. This allows the user to manage
the available power resources, according to his/her needs,
avoiding power wasting.
Author Keywords
Mobile, Power modeling
General Terms
Mobile, Context-awareness
ACM Classification Keywords
C.3.f [Special-Purpose and Application-Based Systems]:
Signal processing systems.
Introduction
Mobile devices, e.g., smartphones, PDAs and tablets,
increase in number of functionalities in the recent years,
making them appealing to a wide range of users. Thus,
even if they are becoming more and more powerful in term
of supported computation, they are constrained w.r.t. the
power consumption. In fact, batteries are limited in time
and their Time To Live (TTL) is highly influenced by the
current phone state, the user usage profile and external
conditions, e.g., signal strength. As a consequence, since
these devices are subject to sudden and unpredictable
changes of their conditions, a method for computing the
device TTL is necessary. A practical way of predicting the
TTL is to model the battery discharging curve, since TTL
coincides with the time the battery level reaches zero level.
Android OS [1], one of the most popular mobile OS, only
provides a percentage as remaining power indicator,
leaving to the user experience and knowledge to determine
the TTL of its mobile device. In order to know a TTL, the
discharge curve is needed, and consequently, a power
model of the device. Literature on mobile power
consumption estimation provides a wide range of works
about power modeling [6]. Most of them use benchmark
applications on a single device to gather data, providing
model which hardly generalize to the burden of devices
currently available. Recently, the need for considering
users’ behaviour is highlighted [7]. A first attempt in this
direction has been done by Kang et al. [4], where real
data are used to build a personalized power model. They
do not take into account power consumption specific to
mobile device, which can behave differently even with a
given user profile.
In this paper, we present MPower, the final
implementation of the idea presented in [3], i.e., a system
able to predict accurately an Android-based mobile device
TTL, without any dedicated hardware tool or modification
of the operating system. The model is focused on the
hardware component power consumption, rather than
users’ behavior, allowing comparison among devices. The
whole architecture is based on a mobile application
implementing a low power logger and a remote server,
where the main computations are offloaded.
System Architecture
MPower relies on a system architecture based on two
main components: an Android client logging application,
running on the mobile device, and a remote server, for the
data storage and the model computation. In Fig. 1the
information flow is presented. At first, the application on
the mobile device gathers data related to energy
consumption, e.g., battery level, CPU frequency, screen
brightness, and sends them to a remote cloud server,
responsible for the power model generation. Finally, the
model is sent back to the device and the currently
estimated TTL is shown by the MPower client.
Figure 1: The MPower system architecture
Power Model Generation
The power consumption model estimation phase is
performed on data gathered by the MPower Android app:
no controlled environment experiments are required.
MPower relies only on the everyday usage data, providing
a flexible system, adaptive w.r.t. new devices and new OS
versions and even able to handle battery degradation
effects. Data gathered from the device may be grouped
into two main categories: controllable actuators values
and uncontrollable variables. The first group determines
the device configurations: each assignment of actuators
values determines device state. For instance, a
configuration is WiFi OFF, 3G ON, bluetooth OFF, etc.
Given a configuration, the uncontrollable variables, such
as time elapsed, CPU usage, etc., are used as input for
the TTL prediction. For each configuration, a power
consumption model is computed, estimating an ARX
model [5]. The power model is sent to the device as a
look-up-table, containing TTLs corresponding to each
configuration. Thus, the mobile device does not have to
perform any further computation, but a simple query on
the look-up table.
The Android application
A preliminary version of MPower app is available on the
Google Play store [2] and the complete version will be
released in September1. Currently, it logs the phone
information and allows the user to visualize the phone
usage statistics. In the final version it will provide also the
TTL in the current configuration and will offer suggestion
for achieving the desired TTL by changing configuration.
Fig. 2(a) presents the application main page screenshot:
the battery TTL for the current configuration is reported
in the center of the screen, while on the lower part it is
possible to access phone statistics (buttons “Reports” and
“Charts”) and to allow the phone to refine the model if
needed (button “Collecting new data”). The button “Set
battery life” leads to the page in Fig. 2(b), where the user
1Further information available at http://mpower.necst.it/
can change the configuration, to modify TTL. Moreover,
the user can use the available filters to set constraints on
functionalities the device must provide.
Figure 2: MPower screenshots of the main page (a) and of the
configuration selection page (b)
Figure 3: MPower statistics on data usage distribution (a) and
battery consumption (b)
Fig. 3shows the third MPower feature: the visualization
of the device usage statistics. Specifically, Fig. 3(a)
reports a pie-chart with the different networks data usage
distribution, while Fig. 3(b) visualize the overall network
data usage and the power consumption.
MPower functionalities allows the user to keep the battery
consumption under control, without having an excessive
overhead in power consumption. In fact, since it runs
continuously on the mobile device, a low impact on the
battery is required. For this reason, to design a
non-power-hungry application, the following aspects have
been addressed during the application developing phase:
sampling frequency Sampling the device status every
ten seconds allow the application not to be listed in
the Android OS most battery draining apps;
wake-lock The application does not make use of any
wake-lock, which would requires the device to leave
the deep-sleep mode (an energy saving state);
computation The model estimation and adaptation is up
to the remote server. To display the TTL, the only
computation required by the device is a single query
on a lookup table.
data transfer Samples are logged on small files, to avoid
power consuming accesses to a single big file. These
information are then compressed and encrypted, to
create smaller packets, and are sent to the server
only if the device is on charge.
Conclusion
We presented MPower, an Android app for power
management on mobile devices. It is able to provide the
current TTL of the phone, as well as to suggest
configurations to save power and extend the battery life,
by basing on a power consumption model. It also provides
phone usage statistics, enhancing the user awareness on
the mobile power consumption. The application logs data
about the device, sends them to a server, which sends
back the estimated power model, ultimately providing the
TTL to the user. Thanks to this architecture the system
is power-friendly, flexible w.r.t. new phones and adaptive
to the specific user.
References
[1] Android os: http://www.android.com/.
[2] Mpower : https://play.google.com/store/apps/
details?id=org.morphone.mpower.
[3] Bonetto, A., Ferroni, M., Matteo, D., Nacci, A.,
Mazzucchelli, M., Sciuto, D., and Santambrogio, M.
Mpower: Towards an adaptive power management
system for mobile devices. In CSE (2012), 318–325.
[4] Kang, J.-M., seok Seo, S., and Hong, J. W.-K.
Personalized battery lifetime prediction for mobile
devices based on usage patterns. Journal of
Computing Science and Engineering 5, 4 (2011),
338–345.
[5] Ljung, L. System identification: theory for the user.
Prentice Hall 7632 (1987).
[6] Vallina-Rodriguez, N., and Crowcroft, J. Energy
management techniques in modern mobile handsets.
Communications Surveys Tutorials, IEEE PP, 99
(2012), 1–20.
[7] Vallina-Rodriguez, N., Hui, P., Crowcroft, J., and
Rice, A. Exhausting battery statistics: understanding
the energy demands on mobile handsets. In 2nd ACM
SIGCOMM workshop, MobiHeld ’10, ACM (New York,
NY, USA, 2010), 9–14.
