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Understanding Human-Smartphone Concerns: A Study of Battery Life


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This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected. We then describe how people charge their smartphones, the implications on battery life and energy usage, and discuss how to improve users’ experience with battery life.
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K. Lyons, J. Hightower, and E.M. Huang (Eds.): Pervasive 2011, LNCS 6696, pp. 19–33, 2011.
© Springer-Verlag Berlin Heidelberg 2011
Understanding Human-Smartphone Concerns:
A Study of Battery Life
Denzil Ferreira1,2, Anind K. Dey2, and Vassilis Kostakos1
1 Madeira Interactive Technologies Institute, University of Madeira, Portugal
2 Human-Computer Interaction Institute, Carnegie Mellon University, USA,,
Abstract. This paper presents a large, 4-week study of more than 4000 people
to assess their smartphone charging habits to identify timeslots suitable for
opportunistic data uploading and power intensive operations on such devices, as
well as opportunities to provide interventions to support better charging
behavior. The paper provides an overview of our study and how it was
conducted using an online appstore as a software deployment mechanism, and
what battery information was collected. We then describe how people charge
their smartphones, the implications on battery life and energy usage, and
discuss how to improve users’ experience with battery life.
Keywords: Large-scale study, battery life, autonomous logging, smartphones,
1 Introduction
Sustainability and energy reduction have emerged as important topics in the social,
political and technical agendas in recent decades. The ubiquitous computing research
community, with its focus on both design and development of technological systems
has had to systematically face a strain between sustainability and usability. On the one
hand, users express an interest in adopting more sustainable products and behavior,
but on the other hand, they do not wish to do so at the expense of their comfort.
Hence it is important that solutions tackling energy reduction take into accounts
users’ behavior and preferences before making an intervention. One area strongly
related to ubiquitous computing research where substantial energy savings can be
achieved by introducing more usable systems is smartphones.
Cell phones are increasingly popular and diverse, with worldwide sales
approaching 1.6 billion units, just last year [8]. Thanks to the rapid development of
wireless technologies, smartphones allow users to be reachable anywhere [3]. As
"convergent" devices, smartphones empower users with Internet access, music, audio
and video playback and recording, navigation and communication capabilities.
However, the growing functionality of smartphones requires more power to support
operation throughout the day. Processing power, feature-sets and sensors are
bottlenecked by battery life limitations, with the typical battery capacity of
smartphones today being barely above 1500 mAh [5]. This is an important limitation
because smartphones are increasingly regarded as a gateway to one’s daily life,
20 D. Ferreira, A.K. Dey, and V. Kostakos
providing networking access to email, social networking, and messaging, making the
management of battery life an important task.
Despite the important limitations that battery life imposes on users, previous
research has shown that existing battery interfaces present limited information, and, as
a consequence, users develop inaccurate mental models about how the battery
discharges and how the remaining battery percentage shown in the interface correlates
to application usage [20]. In addition, users do not completely understand how they
should charge their batteries to support their planned use of the phone. As a result,
every year $22 million are spent in electric utility costs due to keeping cell phones
plugged into outlets for more time than required, to maintain a full charge [8]. On
average, cell phone power supplies use 0.2 watts when the charger is left plugged into
an electrical socket and the phone is no longer attached, with less sophisticated power
supply designs reaching 1 watt [8].
We argue that there exists potential in reducing the energy consumption of
smartphones by better understanding users’ interactions with smartphones and
providing better feedback. While previous studies have focused on the shortcomings
of user interfaces in relation to battery life, there is a need to assess the real-world
behavior of a large number of users in terms of when, how and how long they charge
their batteries. By analyzing users’ battery charging behavior, we can assess the
extent to which energy is being wasted, explore how often users demonstrate less than
optimal charging behavior, how often they interrupt the charging cycle and when this
is more likely to happen. We hypothesize that by conducting such a study we can
identify design opportunities for reducing energy consumption, increasing battery life,
and also predicting when intensive computational operations and long data transfers
should be scheduled.
This paper starts by giving an overview of related work and current state of the art
on smartphone battery management, followed by a description of how was the study
deployed and conducted using the Android Marketplace, and a discussion of
implementation concerns. We then present the results and a discussion of users’
charging habits, how to tackle the issues of wasted energy and opportunistic
processing on smartphones. We conclude with a discussion of how the results can
affect the design of a future smartphone for an energy conscious world.
2 Related Work
Most smartphones offer the possibility to add new applications, through distribution
channels such as the Google Marketplace for the Android platform or App Store for
the iPhone platform. These applications often take advantage of the sensors available,
typically GPS and Internet connectivity to develop context-aware applications [10,5],
accelerometer for motion tracking [18], Bluetooth for distance measurements from the
device [15] and anomaly detection [3,19].
While devices are becoming increasingly mobile, many software developers have
limited experience with energy-constrained portable embedded systems such as
smartphones, which leads to unnecessarily power-hungry applications that rely on the
operating system for power management. In addition, users struggle to determine
which applications are energy-efficient, and typically users blame the operating
Understanding Human-Smartphone Concerns: A Study of Battery Life 21
system or hardware platform instead of unfortunate and unintentional software design
decisions [21].
Rahmati et al. [16] coined the term Human-Battery Interaction (HBI) to describe
mobile phone users’ interaction with their cell phones to manage the battery available.
According to a survey they conducted, 80% of users take measures to increase their
battery lifetime, and it can be expected that maximizing battery life will continue to
be a key concern for users due to the major usability issues involved in this task. One
approach to automatically deal with this issue is to rely on sensor data. For example,
recent devices act proactively to reduce their power consumption, either by turning
off the screen after a specific amount of time with no new interaction, switching to a
lower processing speed (CPU scaling), or disabling wireless interfaces such as
Bluetooth and WiFi when battery levels are low. These devices effectively take into
account sensed data regarding battery levels, idle time, etc.
Oliver et al. [7] highlighted the importance of using real user data collected from
the world and how it can influence application development, by introducing the
Energy Emulation Toolkit (EET) that allows developers to evaluate the energy
consumption requirements of their applications against the collected data. As a result,
by classifying smartphone users based on their charging characteristics, the energy
level can be predicted with 72% accuracy a full day in advance.
A study on the environmental impact of cell phone charging related to national
energy consumption and power plant greenhouse gas emissions reveals that the
energy consumed by cell phone charging has been reduced by 50% in the past years
due to two technology shifts: increased usage of power management and low-power
modes of battery chargers; and use of more efficient switch-mode power supplies [8].
Despite these efficiency gains, however, the US could save 300 million kWh in
electricity per year, which amounts to $22 million in electric utility costs, or 216.000
tons of CO2 emissions from power plants.
The study presented here complements Oliver’s study on user charging
characteristics [7] and Rahmati et al.’s [16] study on how users consume battery in
their devices. It aims to identify when, how, for how long and how frequently users
recharge their devices’ batteries, in order to assess the extent to which energy savings
can be achieved. At the same time, the collected information can be used to identify
design opportunities in order to achieve such energy savings.
3 Study
We conducted a study of battery charging behaviors with 4035 participants over a
period of four weeks, during which anonymous battery information was collected
from Android devices running Android 1.6 or higher. In total, more than 7 million
data points of battery information were collected. The Open Handset Alliance Project
“Android” is a complete, free, and open mobile platform, and its API provides open
access to the device hardware, abstracted from each device’s manufacturer or brand
[2, 13], therefore increasing the number of deployable devices. Although the study
was conducted solely with Android devices, most of the results should be similar to
other smartphone platforms with respect to battery information and user behavior over
time [11].
22 D. Ferreira, A.K. Dey, and V. Kostakos
There was no monetary compensation given to the participating users. The
developed application, OverCharged, which was developed to help users be more
aware of their battery usage, was made available for free on the Google MarketPlace.
The main function of the OverCharged application we developed is to inform
participants of their smart phone’s current battery level, for how long the phone was
running on battery and other miscellaneous information, such as temperature and
voltage. As such, the users who downloaded the application and opted in to sharing
their data are already concerned with the battery life on their mobile devices.
Therefore, they may in fact be atypical users, and our sample may not be
representative of what all smartphone owners would do. Nonetheless, our study does
serve as the first large collection of battery usage.
During the study, users had the option to opt-in to sharing their battery data
anonymously in order to contribute to a better understanding of battery usage patterns.
The application captured charging activity, battery level, device type, temperature,
voltage and uptime:
Charging activity captured when the user charged his device, either through
USB or an AC outlet.
Battery level reflects the remaining battery and how long it took to discharge or
Device type is the manufacturer, device board, model, Android version and build
and the carrier.
Temperature of the battery, both Celsius and Fahrenheit.
Voltage available in millivolts (mV).
Uptime is the amount of time the device was on until being turned off or
The combination of charging activity and battery level allows for the identification
of events such as “unplugged not full”, “charged just unplugged”, “finished
charging”, “charging” and “running on battery”, defined as follows:
Unplugged not full: when the user stopped charging, even though the battery
was not fully charged.
Charged just unplugged: when the user unplugged the charger and the battery is
fully charged.
Finished charging: the moment when the battery is fully charged.
Charging: when the battery starts charging.
Running on battery: when the battery is the only power source.
3.1 Implementation
Polling a device’s state can reduce battery life [10, 12]. The Android API is event-
driven, hence gathering the data had a negligible impact on regular battery life. By
programming a BroadcastReceiver attached to an Android Service running in the
background, whenever the Android OS broadcasts ACTION_BATTERY_
CHANGED, the following battery information was recorded: battery level, battery
scale (maximum level value), battery percentage, battery technology (i.e. Li-ion),
health rating of the battery, whether the phone was plugged to AC/USB, whether the
Understanding Human-Smartphone Concerns: A Study of Battery Life 23
phone is charging, temperature, voltage, uptime and usage uptime, battery status
(charging, discharging, full and not charging) and phone events related to battery
(fully charged and user just unplugged, charging, finished charging, running on
battery, unplugged when not fully charged).
As highlighted by Oliver [10], a large-scale user study distributed across the globe
requires the use of UTC timestamps. We captured the UNIX timestamp on the
participant’s device time zone, which results in consistent times across different time
zones (i.e., 8pm is the same for different users at different time zones). These
timestamps were used across all data collection and analysis operations.
The application was programmed to start automatically when the device was
turned on or rebooted. A small icon in the notification bar at the top of the screen kept
users informed that data was being collected and allowed users to view further
information [Figure 1].
Fig. 1. Notification bar information
3.2 Device Distribution
Of the approximately 17000 people that were using the application at the time the
study was conducted, 4035 opted in to participate on our study. After the installation
of the application from the MarketPlace, if the user opted in to participate in our
study, the application captured device details including device board, service carrier,
manufacturer, model, Android version and Android build.
Recent Gartner worldwide mobile device sales reports [7, 19] do not place HTC as
the leading sales manufacturer. Originally producing primarily Windows Mobile
phones, HTC has changed their focus to Android devices, by manufacturing the
Google Nexus One and EVO 4G more recently. Of the phones used by our
24 D. Ferreira, A.K. Dey, and V. Kostakos
participants, HTC devices and Sony Ericsson devices were the most popular (44.6%
and 29.8% respectively). In third place were Motorola devices with 14.8%, followed
by Samsung with 7.5% [Table 1]. Furthermore, Google’s statistics claim that Android
2.1 is the most popular version with 41.7%, while in our study we saw that 33% of
phones used this version [Table 2]. One surprise in the collected data is that Android
1.6 (Donut) is the leader with 36% of the participating devices using it.
Table 1. Most popular platforms recorded during the study
Platform Distribution
HTC 44.6%
Sony Ericsson 29.8%
Motorola 14.8%
Samsung 7.5%
Table 2. Google’s official Android distribution, as of September 1, 2010 [1]
Platform API Level
(Source: Google) Popularity
(Source: Study)
Android 1.5 3 12.0% -
Android 1.6 4 17.5% 36%
Android 2.1 7 41.7% 33%
Android 2.2 8 28.7% 31%
3.3 How Do Users Manage Battery Life?
Users mostly avoided lower battery levels, with the daily average of the lowest
battery percentage values being 30%. This is likely due to the fact that the Android
devices’ battery icon turns yellow at 30%, and prompts the user with a textual
notification to charge the smartphone by the time it reaches 15%.
The visualization in Figure 2 shows the average battery available at different hours
of the day, across all the users, and how frequently the percentage was observed,
when the battery was not being charged. Each bubble represents a different day of the
study, for a given hour (with a bubble created only when there were at least 1000
datapoints for the selected day-hour combination). Hence, the visualization contains
three dimensions (Percentage, Time and Frequency), with frequency (low to high)
highlighted both by size (small to big) and color (light yellow to dark red). The most
frequent battery averages are above the 30% battery level.
Understanding Human-Smartphone Concerns: A Study of Battery Life 25
Fig. 2. Average battery levels during the day (when not charging)
On average the lowest average battery level was 65% at midnight, while the
highest was 74% at 5AM. We expected that battery levels would be lowest at the end
of the day, and the results confirmed it. The average battery percentage is 67% across
all users throughout the day [Figure 3].
Fig. 3. Average battery levels throughout the day for the whole population
Despite the small variation of hourly battery levels across the whole population,
individual users exhibited varying charging patterns. Some prefer to charge for short
amounts of time throughout the day, while others allow the battery to discharge and
charge it for longer periods of time until full [Figure 4].
Hour of the day (0-23)
Battery percentage
Hour of the day (0-23)
Battery percentage
26 D. Ferreira, A.K. Dey, and V. Kostakos
Fig. 4. Battery level during a single day for three different users
The data reveals two major charging schedules: one between 6PM and 8PM, with
the majority of users initiating charging when the battery levels are at 40%, and
another charging schedule between 1AM and 2AM, with a majority initiating
charging when battery is at 30%. Another frequent charging event happens at 8AM,
with battery levels at 80% on average [Figure 5].
Fig. 5. Average battery levels during the day at the moment when charging begins
The majority of the charging instances occur for a very small period of time (up to
thirty minutes) or between one to two hours, which is the average required time to
recharge completely a battery (left side of the graph). [Figure 6].
Hour of the day (0-23)
Hour of the day (0-23)
Battery percentage
Battery percentage
Understanding Human-Smartphone Concerns: A Study of Battery Life 27
Fig. 6. Charging duration (amount of time the phone remains plugged in)
As expected, a lot of charging instances happen overnight, for 14 hours or more
(right side of Figure 6). The average charging time across the whole population is
approximately 3 hours and 54 minutes, but there is certainly a bimodal distribution,
with the majority of charging instances lasting less than 3 hours. By charging time,
we mean the time since the user plugged his device to charge until unplugged from
the outlet.
Most charging instances start between 5PM and 9PM, while the least popular time
to begin charging is from 3AM to 8AM [Figure 7], although the data in Figure 6
shows that it is likely that phones are being charged during this time.
Fig. 7. Charging schedule (times when users have their phones plugged in)
3.4 How Much Energy Do Users Waste?
Overall, in 23% of the charging instances, the phone is unplugged from the charger
(USB and AC) within the first 30 minutes after the battery is fully charged, while in
the remaining 77%, the phone is plugged in for longer periods thus leading to energy
waste. On average, users keep the phones plugged for 4 hours and 39 minutes after
charging has been completed [Figure 8].
Amount of charging time
Hour of the day (0-23)
28 D. Ferreira, A.K. Dey, and V. Kostakos
Fig. 8. Time until unplugged after the battery is full
Monitoring when the device has finished charging, we calculated how long the
user took to unplug the device from the charger (USB and AC). The amount of time is
greater as expected during the night, starting most often at 11PM and lasting until
8AM [Figure 9].
Fig. 9. Overcharging schedule
3.5 How Does Charging Happen?
As predicted, for longer charging periods AC is the preferred choice for phone
charging. For short charges (30 minutes or less), USB charging is much more
frequent. On average, users charge their phones 39% of the time using USB, and 61%
of the time using AC [Figure 10].
Fig. 10. Amount of time charging with USB (red) vs. AC (blue)
Amount of time
Hour of the day (0-23)
Amount of time
Understanding Human-Smartphone Concerns: A Study of Battery Life 29
In Figure 10, blue represents AC, and red is USB charging. The initial pair on the
left represents charging between 0-30 minutes, in which charging is mostly USB for
this specific period of time. AC charging has two peaks, one between 1-3h of
charging time and 14 hours or more for overnight charging.
3.6 How Often Is the Phone Rebooted/Turned Off?
Uptime is the time elapsed before the phone is rebooted or turned off. In our study, all
participants’ devices are on for at least up to a full day [Figure 11]. The results show
that the likelihood of having a device on for up to two days is 33%, 18% for up to
three and 11% for up to four days.
Fig. 11. Uptime in days
4 Discussion
The large-scale study described here was conducted in order to assess the extent to
which energy is being wasted, explore how often users demonstrate less than optimal
charging behavior, how often they interrupt the charging cycle and when this is more
likely to happen. We hypothesized that by conducting such a study we could identify
design opportunities for reducing energy consumption, increasing battery life, and
also predicting when intensive computational operations and long data transfers
should be scheduled.
Previous studies have shown that users have inadequate knowledge of smartphone
power characteristics and are often unaware of power-saving settings on smartphones
[16]. Users should be provided with options on how to better manage the remaining
battery, and, to some extent, automated power features can also help them use the
device as intended [12, 20]. Most smartphones alert the user that they need to be
charged when the battery reaches critical levels [16,17], but do not notify the user
when it has finished charging. For instance, explicitly notifying the user that the
device is running low on battery is something Android does when the battery is at
Battery management requires user intervention in two respects: to keep track of the
battery available so that users can decide how to prioritize amongst the tasks the
30 D. Ferreira, A.K. Dey, and V. Kostakos
device can perform; and to physically plug the device to the charger and surrender its
mobility [17]. There is an opportunity to optimize which functionality should remain
active based on the user’s lifestyle and battery charging habits, improving the human-
battery interface (HBI) with the user. Each user is unique and as such, the
optimization system must learn and adapt to the user. The results show important
differences between users’ behavior and preferences, but also highlight common
patterns that can be useful in understanding aggregate behavior and developing
software that taps into those behaviors.
The findings of this study show that users
demonstrate systematic but at times erratic charging behavior (mostly due to the
fact that charging takes place when the phones are connected to a PC);
mostly choose to interrupt their phones’ charging cycle thus reducing battery
aim to keep their battery levels above 30% due to an automatic ambient
notification; and
consistently overcharge their phones (especially during the night);
4.1 Users’ Charging Habits
The study shows that users charged throughout the day resulting in erratic charging
patterns and disrupted charging cycles that can reduce the lifetime of the battery
[Figure 4]. A potential design opportunity exists here, whereby erratic charging
behavior can be avoided by implementing a timer threshold that will prevent batteries
from charging for short periods of times, e.g., for less than 5 minutes. The results
[Figure 10] demonstrate that charging using USB could be triggered by command
from the user (a feature already seen with some HTC Sense® devices) or if the battery
percentage available is below 30%.
Interrupted charging cycles [Figure 11] leads to the necessity of battery calibration
(drain the battery until depletion and fully charging it). The “memory effect”, is a
term loosely applied to a variety of battery ills [9]. From Corey’s research [5],
overcharging, over discharge, excessive charge/discharge rates and extreme
temperatures of operation will cause the batteries to die prematurely. Users in this
study consistently kept the battery from reaching lower levels, with an average lower
percentage of 30% of battery power by charging throughout the day (e.g., plugging
their devices to the car dock for navigation at 8AM [Figure 5] or charging while
transferring files). Software updates and backup routines could take these moments to
run power intensive operations only if the user has his phone plugged in for more than
30 minutes, since according to the results, there is a very high probability the user will
charge for at least 1-2 hours.
4.2 Avoiding Energy Waste
Another problem that our study highlights in relation to charging duration is the
amount of time the users keep their phones connected unnecessarily. In the past,
charging a battery for a long period of time would damage the battery from
overheating and overvoltage [4, 5, 21]. Modern Li-ion and Li-poly batteries come
from the manufacturer prepared to interrupt charging as soon as they are fully charged
Understanding Human-Smartphone Concerns: A Study of Battery Life 31
[14], but this still results in unnecessary power consumption. This study shows that
this happens frequently, which suggests that manufacturers should make an effort to
improve their chargers to cutoff the charging as soon as the battery is full or after
some time in cases where the phone is being powered directly from the charger.
In addition, there is a design opportunity to give feedback to users the moment they
plug in their phone – they usually look for confirmation that the phone is charging. At
that moment feedback could be provided to change users’ behavior. For example, we
can predict when a “plugged in” event is likely to result in a long power consumption
session, specially if it happens around 11PM. At that moment a message could inform
the user that “your phone will be fully charged in X minutes”, prompting them to
remember to unplug it, to minimize the time when the phone is plugged in when it is
already fully charged.
The combination of erratic charging and unnecessary charging observed in this
study shows that users appear to have two types of charging needs: short bursts of
charging to get through the day, and long charging periods during the night. One
mechanism to reconcile these two distinct requirements is to allow for batteries to
have a “slow-charge” mode, whereby they do not charge as fast as possible, but
charge at a rate that will reduce the amount of unnecessary charging. A rule of thumb
can be derived from [Figure 6], which suggests that an effective rate for “slow-
charge” rate could kick-in after 30 minutes and aim for a full charge in 4 hours (the
average overcharging length). A more sophisticated approach could incorporate a
learning algorithm on the smartphone or even the battery itself.
4.3 Opportunistic Processing on Smartphones
In terms of identifying opportunities for intensive operations on the smartphone, the
results suggest that there exists an important 30-minute threshold once charging
begins. If a charging session lasts more than 30 minutes, it is very likely that it will
last for a substantially longer period. Charging that uses AC is also an indicator that
the user will be likely to charge for a longer period of time. Combined, the 30-minute
threshold and AC power source provide a good indication as to when applications
should perform power intensive operations on smartphones: large data transfers,
computationally intensive activities, etc.
5 Conclusion
More than ever, industry and academic research have an opportunity to resolve
numerous issues and conduct studies using published applications to support users’
needs. Marketing and mobile phone manufacturers study a variety of user needs,
focusing on the design of new handsets and/or new services [15]. Using automatic
logging, in which software automatically captures user’s actions for later analysis
provides researchers with the opportunity to gather data continuously, regardless of
location or activity the user might be performing, without being intrusive.
Asking users to anonymously collect battery information using a Google
Marketplace application was a success: at the time of writing, 7 million battery
32 D. Ferreira, A.K. Dey, and V. Kostakos
information points and 4000 participating devices from all over the world were loaded
into our database from which the battery charging patterns were explored.
The results provide application developers and manufacturers with information
about how the batteries are being charged by a large population. The design
considerations highlight how can we improve users’ experience with their battery life
and educate them about the limited power their devices have.
We look forward to seeing the next generation of smartphones, that learn from the
user’s charging routines and changes their operation and charging behavior
We thank all the anonymous participants that contributed for the study using our
application. This work was supported in part by the Portuguese Foundation for
Science and Technology (FCT) grant CMU-PT/HuMach/0004/2008 (SINAIS).
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... Despite such bugs need not crash the software, neglecting them can lead to losing the customers' trust. Moreover, the need of efficient software is nowadays becoming more important than ever due to the rising scale of software and growing pressure on saving resources such as, e.g., battery power on mobile devices [3] or the huge power consumption of computing centers [4]. Finally, software performance is also a matter of security as performance bugs can be exploited to effectively kill a system by some form of a denial-of-service attack (DoS), making it run exceptionally long on some inputs. ...
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In this paper, we present Perun: an open-source tool suite for profiling-based performance analysis. At its core, Perun maintains links between project versions and the corresponding stored performance profiles, which are then leveraged for automated detection of performance changes in new project versions. The Perun tool suite further includes multiple profilers (and is designed such that further profilers can be easily added), a performance fuzz-tester for workload generation, methods for deriving performance models, and numerous visualization methods. We demonstrate how Perun can help developers to analyze their program performance on two case studies: detection and localization of a performance degradation and generation of inputs forcing performance issues to show up.
... A battery-based system needs to be charged regularly while a mains-based system is dependent on the supply of electrical current. Smart handheld devices are mostly battery operated and use lithium-ion (Li-ion) as the implementation technology [31,32]. ...
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The advent of smart devices, interacting with each other as well as remote services, has paved the way for the Internet of Everything (IoE). IoE is a direct successor of Internet of Things (IoT), composed of smart devices interacting with remote services. The devices in an IoE environment are power constrained. At the core of an IoE environment, there is a context-aware system that gathers the context and classifies it. Various datasets have been published by authors for context-aware systems. This paper presents a mechanism that gathers a dataset of contextual information along with power information using smartphones. An Android application “PowerIpsum” is developed for gathering contextual information, power information, and user input activity labels. The dataset includes the sensor data as the contextual data, timestamps, average current, and average voltage as well as user activity labels. Time elapsed and power consumption is forecasted using Monte Carlo method. The results provide useful insights and demonstrate the advantages of power information within a context-aware system.
... The effect of such erratic behaviour on the operation of the QUAREM approach is discussed in Section 5.4. Besides, such disruptive charging cycles can reduce the lifetime of the battery [18], thus, the need for longer battery hours management that enables both energy-saving and regular charging cycles. In addition, we also discovered that none of the users failed to utilise one of the charging options (AC, USB, etc.) within a day, i.e., users with no plug-in activity during the day do not exist except if the user's data is missing entirely for the day. ...
Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE) , despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47% and 2.48% for the energy demand and plug-in times, respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within 20–25% energy demand variation with little or no QoE degradation.
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For many people, mobile platforms are now an essential part of everyday life. A defining feature of mobile platforms is their reliance on battery performance. Due to this reliance, there is a pressing need for mobile applications that minimise their own impact on batteries. While mobile platforms are improving their capabilities in terms of policing the energy use of applications and rationing energy-hungry devices, mobile application developers still lack knowledge in how to write energy efficient programs. Recent work in automatic program improvement using heuristic search over randomly generated program variants has shown some promise in terms of producing reductions in programs’ energy-use. A challenge in this work is accurately measuring the energy-use of program variants. One approach to measurement is to use each platform’s internal meter to assess variants on the device itself. This approach has advantages in terms of measuring actual energy-use on each platform but is not ideal for the search for program variants that perform well across multiple platforms. The work in this paper addresses this problem by using an island-like evolutionary search mode to simultaneously evolve variants on multiple platforms. Island models of evolutionary search conduct search on multiple platforms in parallel and share promising variants. The results show that this approach has advantages over isolated evolution in terms of speeding up evolution on each platform and improving the efficiency of search. Validation results show that the island-inspired model is able to evolve variants with good cross-platform performance. In addition, it evolves a solution that outperforms best found solutions using a sequential evolutionary algorithm on it is native platform with an effect size greater than 90%.
“Have you ever ordered something at a bar just so you can ask to plug in your phone? Do you argue with loved ones because your phone died and you missed their calls or texts? Are you regularly accused of secretly ‘borrowing’ someone else’s charger? If so, you may be suffering from ‘Low-Battery Anxiety’ ”, according to a survey conducted by LG. The survey also reported a shocking result—nine out of ten mobile users have the so-called low-battery anxiety (LBA), which refers to one’s fear of losing mobile phone battery power especially when it is already at a low level (20% for example). The fear of losing battery power further triggers the “no-mobile-phone phobia” (nomophobia), which is commonly considered as one social phobia and could lead to mental health problems. Considering the ever increasing number of smartphone users worldwide, the impact of accompanying LBA could be profound.
Traditional offline optimization frameworks rely on representative hardware, software, and inputs to compare different optimizations on. With application-specific optimization for mobile systems though, the idea of a representative testbench is unrealistic while creating offline inputs is non-trivial. Online approaches partially overcome these problems but they might expose users to suboptimal or even erroneous code. Therefore, our mobile code is poorly optimized, resulting in wasted performance and energy, and user frustration. In this paper, we introduce a novel compiler optimization approach designed for mobile applications. It requires no developer effort, it tunes applications for the user’s device and usage patterns, and has no negative impact on the user experience. It is based on a lightweight capture and replay mechanism. Our previous work [46], captures the state accessed by any targeted code region during its online stage. By repurposing existing OS capabilities, it keeps the overhead low. In its offline stage, it replays the code region but under different optimization decisions to enable sound comparisons of different optimizations under realistic conditions. In this paper, we propose a technique that further decreases the storage sizes without any additional overhead. It captures only the intersection of reachable objects and accessed heap pages. We compare this with another new approach that has minimal runtime overheads, at the cost of higher capture sizes. Coupled with a search heuristic for the compiler optimization space, our capture and replay mechanism allows us to discover optimization decisions that improve performance without testing these decisions directly on the user. Finally, with crowd-sourcing we split this offline evaluation effort between several users, allowing us to discover better code in less time. We implemented a prototype system in Android based on LLVM combined with a genetic search engine and a crowd-sourcing architecture. We evaluated it on both benchmarks and real Android applications. Online captures are infrequent and introduce ~5ms or 15ms on average, depending on the approach used. For this negligible effect on user experience, we achieve speedups of 44% on average over the Android compiler and 35% over LLVM -O3. Our collaborative search is just 5% short of that speedup, which is impressive given the acceleration gains. The user with the highest workload concluded the search 7x faster.
This qualitative study explored the use of mobile-based social media technologies in the library services at the Federal University Lafia, North Central Nigeria. The purpose was to understand the academic information-seeking behaviour of the student library users while using mobile-based social media and other mobile-based technologies. A further purpose was to identify some of the barriers the students encountered when seeking information. Meyer’s (2016) information behaviour model provided the conceptual framework for the study. For this reason, the literature review and the empirical data were structured according to the six major information behaviour components identified in the model. These are information, user, context, technology, information needs, and information activities. Meyer’s model recognises technology as an important enabler of information behaviour. After clarifying these concepts, the discussion explored mobile technologies and mobile applications to acquire an understanding of these technologies and applications and the way in which they could be employed by a library to enhance its services. After that, the literature review focused on the way in which mobile technologies and social media are employed by users (especially students) to seek information that is supportive of their information needs. In this discussion, the influence the interplay between context and the users’ cognitive, affective and sensorimotor structures have on the users’ information needs and seeking behaviour was explored. The study used a descriptive qualitative phenomenology research design to gain insight into participating students’ experiences using mobile-based social media technologies as a phenomenon. A total of 21 undergraduate students who were in their second to fourth year of study were interviewed using semi-structured interviews. The findings revealed that the use of mobile-based social media in information-seeking is affected by the participants’ cognitive and affective structures which shaped their attitudes towards information sources and the way in which they use them. In addition, certain personal and environmental barriers seem to affect the participants’ information-seeking activities. In order to get access to information that would satisfy their information needs, the participants relied on online social media groups. The use of Meyer’s information behaviour model enabled an understanding of the reasons why students use mobile technologies and social media when seeking academic information and the means they employ to deal with their academic task-related information needs when they lack the necessary knowledge and skills to seek information in the library. This understanding enabled the researcher to graphically illustrate the participating students’ information-seeking behaviour while using mobile technologies and social media. Some recommendations could also be made to improve the library’s services. These include the aggressive promotion of library services and information resources to users with frequently conducted user surveys. Such surveys would support the library management in keeping abreast with their users’ information needs and would guide the development of a reliable environment that is conducive to users’ information-seeking activities which are focused on satisfying their information needs.
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This paper introduces capabilities developed for a Battery-Sensing Intrusion Protection System (B-SIPS) for mobile computers, which alerts when abnormal current changes are detected. The intrusion detection system's (IDS's) IEEE 802.15.1 (Bluetooth) and 802.11 (Wi-Fi) capabilities are enhanced with iterative safe process checking, wireless connection determination, and an automated intrusion protection disconnect ability. The Correlation Intrusion Detection Engine (CIDE) provides power profiling for mobile devices and a correlated view of B-SIPS and Snort alerts. An examination of smart battery drain times was conducted to ascertain the optimal transmission rate for the B-SIPS client. A 10 second reporting rate was used to assess 9 device types, which were then compared with their corresponding baseline battery lifetime. Lastly, an extensive usability study was conducted to improve the B-SIPS client and CIDE features. The 31 expert participants provided feedback and data useful for validating the system's viability as a complementary IDS for mobile devices.
Conference Paper
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This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
Conference Paper
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Battery interfaces provide important feedback about how much time users can continue using their mobile devices. Based on this information, they may develop mental models of the types of activities, tasks, and applications they can use before needing to recharge. Many of today's battery interfaces tend to report energy in coarse granularities or are highly inaccurate. As a result, users may find it difficult to depend on the estimates given. We conducted a survey with 104 participants to understand how users interact with various mobile battery interfaces. Based on the survey results, we designed and prototyped a task-centered battery interface on a mobile device that shows more accurate information about how long individual and combinations of tasks with several applications can be performed. Our pilot study of eight users demonstrated that fine-grained information separated by tasks can help users be more effective with and increase their understanding of their device's battery usage.
Conference Paper
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Implicit in much research and application development for mobile phones is the assumption that the mobile phone is a suitable proxy for its owner's location. We report an in-depth empirical investigation of this assumption in which we measured proximity of the phone to its owner over several weeks of continual observation. Our findings, summarizing results over 16 different subjects of a variety of ages and occupations, establish baseline statistics for the proximity relationship in a typical US metropolitan market. Supplemental interviews help us to establish reasons why the phone and owner are separated, leading to guidelines for developing mobile phone applications that can be smart with respect to the proximity assumption. We show it is possible to predict the proximity relationship with 86% confidence using simple parameters of the phone, such as current cell ID, current date and time, signal status, charger status and ring/vibrate mode.
Battery system design and management is critical to the ultimate success of every energy storage application. However, the application itself must be properly evaluated to determine what type of battery should be used and how to manage the operational environment for the system. The application evaluation step is critical to the implementation of a successful system that uses lead-acid batteries. This paper details nine ways that improper design, implementation, and/or applied battery management techniques result in poor battery performance with focus on the selection of a battery for a particular power or energy application.
We present preliminary results of a large-scale smartphone user study that examines how users interact with and consume energy on their personal mobile devices. Our dataset consists of over one millennium of user interaction traces from over 17300 BlackBerry users. Despite the scale and detail of the dataset, there are many research questions that it cannot answer; further user studies are therefore needed. We detail our insight into the major challenges in conducting a large-scale user study on BlackBerry devices.
The body of mobile applications is growing at a near-exponential rate; many applications are increasing in both scale, com-plexity, and their demand for energy. The energy density of smartphone batteries is increasing at a comparably insignif-icant rate, and thus inhibits the practicality of these appli-cations. Despite the importance of energy to mobile appli-cations, energy is rarely considered by mobile applications developers primarily due to lack of knowledge about users and the absence of energy-aware developer tools. We conduct a large-scale user study to measure the energy consumption characteristics of 15500 BlackBerry smartphone users. Our dataset is several orders of magnitude larger than any previous work. Using this dataset we build the En-ergy Emulation Toolkit (EET) that allows developers to evaluate the energy consumption requirements of their ap-plications against real user energy traces. The EET com-putes the successful execution rate of energy-intensive appli-cations across all users, specific devices, and specific smart-phone 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 ad-vance, and through an Energy Management Oracle energy intensive applications can adapt their execution to achieve a near optimal successful execution rate.
Conference Paper
Mobile phone users have to deal with limited battery lifetime through a reciprocal process we call human-battery interaction (HBI). We conducted three user studies in order to understand HBI and discover the problems in existing mobile phone designs. The studies include a large-scale international survey, a one- month field data collection including quantitative battery logging and qualitative inquiries from ten mobile phone users, and structured interviews with twenty additional mobile phone users. We evaluated various aspects of HBI, including charging behavior, battery indicators, user interfaces for power-saving settings, user knowledge, and user reaction. We find that mobile phone users can be categorized into two types regarding HBI and often have inadequate knowledge regarding phone power characteristics. We provide qualitative and quantitative evidence that problems in state-of-the-art user interfaces has led to under- utilized power-saving settings, under-utilized battery energy, and dissatisfied users. Our findings provide insights into improving mobile phone design for users to effectively deal with the limited battery lifetime. Our work is the first to systematically address HBI on mobile phones and is complementary to the extensive research on energy-efficient design for a longer battery lifetime.