QoE based energy reduction by controllin

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QoE-Based Energy Reduction by Controlling the 3G

Cellular Data Traffic on the Smartphone

Selim Ickin

School of Computing Blekinge Institute of Technology

Karlskrona, SWEDEN Email: selim.ickin@bth.se

Katarzyna Wac

Institute of Services Science University of Geneva Geneva, SWITZERLAND Email: katarzyna.wac@unige.ch

Markus Fiedler

School of Computing Blekinge Institute of Technology

Karlskrona, SWEDEN Email: markus.fiedler@bth.se

Abstract—One of the most influencing factors on the overall end-user perceived quality from applications and services, i.e., QoE, running on the smartphones is their limited battery life. Particular cloud-based applications/services on the smartphone with a constrained battery life might consume high energy even when the smartphone is in screen-OFF state. The cellular radio module of the smartphone is one of the most power-consuming components, which depends on the running applications’ informa-tion polling characteristics that eventually cause the radio module to toggle occasionally between the cellular data energy states even during a sleep state.

In this paper, we investigate the energy consumption of a set of applications that tend to retain up-to-date information via aggressive polling patterns. We show that limiting the network traffic and increasing the resource utilization efficiency amongst the applications and services can highly reduce the total energy consumption. We control the network activity of a smartphone with different cellular data-enabled anddata-disabled durations at the screen-OFF state. First, we run controlled-lab energy measurements to have a ground truth on the power consumption patterns of a set of cloud-based popular applications/services; and next we conduct a subjective study with our proposed solution (ExpCO2), to understand first the user behaviour on the smartphone and then present how the reduced polling intervals of applications and notifications influence the end-user perceived quality. We indicate that ExpCO2 has a potential to save energy.

I. INTRODUCTION

The user perceived quality of the mobile applications de-pends on the total energy consumption of the mobile handheld terminals, as the duration of user service time depends on the lifetime of the capacity-limited batteries [1]. There are huge efforts in industry and in academia dedicated to improve the battery life, however those efforts remain isolated on specific

particular domains,e.g., software, hardware, and radio network

without consideration of the other domains [2]. The software domain needs responsibility in energy-efficient application and service development, otherwise might cause lower end-user

perceived quality,e.g., Quality of Experience (QoE), resulting

in users giving up using particular apps [4]. An energy-efficient app is described in [2] as an app that is adopted to the characteristics of the cellular network, while minimizing the number of required connections and reducing the total con-nection duration to the network. The effectiveness of a system is achieved when there is an appropriate balance between the functionality [9] (a set of actions/services offered to the users [8]), and the usability (degree by which the system can be used efficiently and properly performing goals [8]). In order for

the application/service to lead to a high user-perceived quality, the cloud-based application developers need to reconsider their designs to minimize energy consumption and maximize QoE. As the radio cellular data module is one of the most power-consuming components of the smartphone [11], minimizing the energy consumption can be achieved through controlling directly the cellular data interface.

The cloud-based mobile applications rely on the Internet connectivity in order to keep their contents up-to-date and present them to the users through notifications. Therefore, the cloud-based applications require toggling the cellular radio module to the active state occasionally to transmit/receive data during the update intervals. In order to reduce the number of oscillations between the states, the radio module preserves its state for a certain amount of duration after being toggled ON, even if the flow of packets in a user session is completed [2]. Therefore, upon activation, the radio module consumes power at least for a constant period of time. As it is common to run a high number of cloud-based applications in the background simultaneously, high number of requests for toggling the radio

module to the full active (i.e., the most power-consuming)

state asynchronously would toggle the radio interface ON and OFF at different times, which in sum might increase the total energy consumption. However, a mechanism that can control the cellular network interface of the smartphone in a way to toggle the cellular data interface ON and OFF at particular periodicity with various durations would let all the applications’ network activities to be accomplished at fixed and predefined time intervals. In other words, this would shift small bursts that are spaced out with some intervals into rather fewer number of large bursts [2]. We hypothesize that this might reduce the total energy consumption; we build the study in this paper on this basis. As a drawback, this proposal might in parallel reduce the end-user perceived QoE as the end-users are notified by the updated information with some latency.

Controlling, i.e., in the sense of enabling / disabling,

the cellular data interface might reduce the overall perceived quality due to the increased burst size of data and latency during the polling of the content, given that the users are using these apps interactively. However, this might be pos-sible when the users are not interacting actively with the

smartphone, i.e., basically when the smartphone screen is in

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emails, even as an addiction [12], while some other users do it less often. Indeed, bombardment of notifications to users can keep people busy checking them, while preventing them to accomplish more important tasks, and eventually reducing their productivity. Therefore, reducing the number of interruptions caused by occasional notifications as a result of occasional

network activities, e.g., pull and push functionalities, would

possibly increase the focus on the task at hand and productivity. Dawidow in [15] claims that the users should control tools to accomplish tasks rather than letting the tools control the users, and our research contributes to that.

Our main goal in this study is to reduce the total energy consumption of a smartphone during its screen-OFF state, while investigating its influence on information polling latency on the end-user perceived QoE. We hypothesize that the background running applications information update intervals do not highly influence the user experience when the screen is in OFF state. The research contribution of this paper is as follows. In contrast with the previous related studies, we propose a power saving software that can save energy by controlling the cellular data interface in screen-OFF state, while keeping the end-user perceived quality unchanged. The proposed software does not require any major changes in the network stack or in the OS, and can be easily installed to most of the Android OS based smartphones.

The paper is structured as follows. The background in-formation on the energy states of cellular radio module of the smartphone is reviewed in Section II. The related work regarding earlier proposed solutions to control the energy states of the radio module as well as the work done to reveal the disadvantages of notifying users with interruptions are is discussed in Section III. The measurement data and the experiment methodology are given in Section IV. We introduce our two applications that are used to understand the power consumption characteristics of a set of network activities; and to understand the end-user perceived quality on the reduced network activities. The results of the in-lab ground truth energy ExpCO2 measurements are given in Section V. Next, the details of the latter subjective study is presented in Section VI. Section VII confers the subjective study results. We discuss on the overall results and present the limitations of the study in Section VIII. The paper is concluded and future work is presented in Section IX.

II. 3G CELLULARRADIOPOWERCONSUMPTION

The cellular radio is designed to minimize the energy consumption such that it transits between the most power-consuming state to a less power-power-consuming state depending on the network traffic activity. Radio Resource Control (RRC) Protocol in Universal Mobile Telecommunications System (UMTS) [3] has a state machine with three states that has different power consumptions, and these states depend on the data rate and the uplink/downlink queue sizes [17]. The

Idle state is assumed when no radio resource is allocated, no

connection between User Equipment (UE), e.g., smartphone,

and Radio Network Controller (RNC) is established, and the

UE cannot transfer any data. TheDCHstate is assumed when

UE-to-RNC connection is established, and the UE is allocated

with the dedicatedDCHtransport channels for both downlink

and uplink with the highest bandwidth and power. TheFACH

is the state for very low data throughput rate application

requirements, and inFACH, the RRC connection is established

but no channel is associated. The power consumption decreases

in the order of DCH, FACH, and Idle[2]. The cellular radio

module transits fromDCHtoIdlestate first by transiting into

an intermediate state (FACH) depending on the tail time (can

last up to 15 _{seconds [2]), which depends on the network}

provider, the traffic volume, or the smartphone manufacturer. The amount of the web content to be polled and the connection speed are important factors for the energy consumption during the polling periods. In addition, there exists a delay between the energy state transitions, which are due to the high number of control messages being transmitted and received between UE and the RRC.

III. RELATEDWORK

The power consumption of smartphones have been an-alyzed many times in previous works [19]. Some important work so far are related to leveraging the 3rd Generation Part-nership Project (3GPP) standards by proposing fast-dormancy.

This is basically forcing the transitions such asDCH to Idle

or FACH to Idle without waiting for the inactivity timeout

periods. Tail Optimization Protocol (TOP) is proposed by

Qian et al [5] to minimize the timeout periods, i.e., inactivity

times, between the states by invoking the fast dormancy support. On the other hand, according to a research done

by AT&_{T [2], for particular apps, reducing the tail timer by}

three seconds reduce the resource usage by40 %_{, but increase}

the number of state promotions by 31 %_{. In order to reduce}

the initial latencies, it is claimed by Gerber et al [2] that pipelined Hypertext Transfer Protocol (HTTP) requests into a single Transport Control Protocol (TCP) connection rather

than sequential HTTP requests. AT&T diagnosed the problem

via app profiler, bundled the scatter-burst transmission of small

packets into a single transmission, and achieved40 % _energy

saving. The missing part in the research is how the suggested solution influences the end-user perceived QoE.

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Fig. 1. Example of the power consumption of different states of the cellular radio module for a single Ping execution with RTT= 1429ms.

0 200 400 600 800 1000 1200 1400 1600 10ï2

10ï1

Power Consumption (mW)

C

D

F

www.bbc.com www.cnn.com www.slashdot.org www.csv.com PING (dedicated server IP)

Idle FACH DCH

Fig. 2. Power consumption patterns for a set of applications. Black solid line shows the approximate trend.

source) patterns of users through an app [10], and suggest solutions to reduce waste of energy during overcharging pe-riods, and also to reduce the damage the battery can get via overheating/overvoltage. The most important difference between the related work and ours is that we developed a

power saving toolkit,ExpCo2, conducted ground truth power

performance measures, and employed a subjective study in

order to assess the efficiency of the tool. ExpCO2 reduces

the total number of network activities by extending the Idle

periods. This prevents the user to be interrupted with push/poll notifications of cloud-based apps, and also aims at minimizing the total energy consumption and the network utilization.

IV. METHODOLOGY ANDMEASUREMENTDATA

We have developed two different apps for the purpose of this paper. The first app developed for the work presented in

this paper is used as a test softwareto understand the energy

states of the cellular radio module through ground truth mea-surements via the Monsoon Mobile Power Monitoring Tool (MPMT) [6] during the screen-OFF state of the smartphone. Our aim is to obtain background knowledge on the power consumption changes amongst different cellular radio states.

As the second app, the ExpCO2 software is developed and

used in a subjective study to record a set of QoE metrics on the users’ own smartphones.

A. Test Software and the MPMT-based Measurements

We developed a test software that initiates a set of

com-mands, such aspingorweb page download. By using MPMT,

we conducted in-lab energy measurements with the Samsung Galaxy S with Lithium-ion battery. During the smartphone’s

sleep state, i.e., screen-OFF state, a set of echo request

messages with 30 _{seconds inter-departing time, are sent to}

a dedicated server located at the BTH University campus in

Karlskrona, Sweden. The reason for 30_{s inter-departing time}

was a repeatability,i.e., waiting for the cellular radio to transit

back to Idle state, as each complete experiment repetition

initiates and ends at the Idle state. During the experiments,

the screen (backlight display), WiFi, GPS, Bluetooth, Audio, Camera, and unrelated apps were OFF. This way, we prevent additional power consumption caused by other components, which made it possible to focus exclusively on the cellular radio’s power consumption. During the experiments, we inves-tigate the transition latency between the energy states and the inactivity timeout periods amongst the three energy states of

the cellular radio module of the smartphone. Thetest software

recorded the Round Trip Time (RTT); and MPMT recorded the average power consumption during the ping execution to an IP address of a computer at BTH campus (to avoid any extra DNS

resolution latency). MPMT provided3.7V to the smartphone

and measures the average power consumption with a default

sampling rate of5_kHz

A transmission of only one echo request packet can wake up the radio module during the sleep state of the smartphone. It is possible to understand the minimum duration of a radio module in the active state, knowing via “tcpdump” that there is no further traffic after the reply message being received back. As a representative example from one test run (see Fig. 1),

even during one RTT, e.g., 1429_{ms (high value due to the}

first packet effect) of a 64 _{Bytes ICMP packet, the duration}

when the radio module state is notIdle, is roughly more than

10 _{seconds. Observe that the solid black line is the Simple}

Moving Average (SMA) [23] with window size of 0.5_s,i.e.,

smoothing over2500_{power data samples. Thereby, the energy}

is wasted during the remaining duration,e.g.,Idle-To-DCHand

FACH-To-Idle periods. In addition, not all the time spent at

the DCHstates are due to the data activity, rather due to the

inactivity timeout periods.

We further investigate the energy consumption during

web page download for a set of website URL’s. This

time, the test software is instructed to send GET messages to popular website URL’s and then downloads the con-tents of the websites: cnn.com (news); (ii) bbc.com (news); (iii) slashdot.org (RSS feed); (iv) cvs.com (retail). The test soft-ware records the total amount of download time and MPMT records the power consumption similar to the ping exper-iments. We repeated these experiments in the lab every

45_seconds, i.e., a time period so that the radio module is

switched completely to theIdlestate, before the next network

activity starts. In total, a minimum35_{iterations with} 45

sec-onds time gap were conducted and each iteration consisted of more than eight million power data points as the power metric

is recorded at a sample rate of 5_{KHz. The Complementary}

Cumulative Distribution Function (CCDF) plots for the power consumption for the mentioned experiments are depicted in Fig. 2. We classified the three energy states intuitively and showed with a black solid line.

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B. ExpCO2 Software and Power Measurements

The main purpose of ExpCO2 is to save smartphone energy, when it is not interactively used by the user, par-ticularly in screen-OFF state. Upon particular events, Ex-pCO2 logs various parameters: screen state (ON, OFF); cellular data state (ON, OFF); the connected network type (3G, WiFi); unique smartphone device ID; remaining battery level percent; battery state (plugged, unplugged to/from power source); the received application notifications (only corresponding package name); user feedback (free text); and the five-scale Mean

Opin-ion Score (MOS) (1_isbadand5_isexcellent) [22] together with

the impairment (1_isvery annoyingand5_isimperceptible). The

ExpCO2 services, which collect the metrics, are running in the background.

Together with the ExpCO2’s metric recording functionality, we added the feature that toggles the data state of the cellular radio module between ON and OFF states with different time intervals during screen-OFF state. ExpCO2’s aim is to limit the occasional network traffic activities of all applications on user smartphones, in a way that they will be activated at once for a particular period of time, and to assess its influence on the end-user perceived quality. Our proposal is that the occasional network activities can be reduced and grouped in a particular time window, which will minimize the number of toggling between the states by different running applications and services. Reducing the total number of network activities will reduce the total number of cellular data state transitions, the amount of inactivity timeout periods of the cellular radio, and eventually the total energy consumption of the smartphone. ExpCO2 does this preliminarily by disabling and enabling the cellular data module completely for specific period of

time. We refer to T_DataON _and T_DataOFF _{to DataON}

(data-enabled) and DataOFF (data-disabled) durations, respectively. Towards this aim, we launched a set of cloud-based applica-tions such as Facebook, Twitter, LinkedIn, Instagram, GMail, GTalk, Google Services, WhatsApp from a dedicated user application account. We also launched one Swedish news application (AftonBladet [21]) that also has polling function-ality. During the execution of the app, the ExpCO2’s extra power consumption characteristics are investigated as well. We first verify the power consumption of ExpCO2 with in-lab measurements as a ground truth. Next, we conduct a subjective study on users’ daily life natural environments; we employed ExpCO2 on the users personal smartphones.

For the verification of power consumption of ExpCO2, we conducted a set of ground truth energy measurements

in the lab with different scenarios via MPMT during the

screen-OFF state: (1)T_DataOFF = 3_{min; (2)}T_DataOFF =

9_{min; (3)}TDataOFF = 14min; (4)TDataOFF = 19min;

(5)T_DataOFF = 29_{min; (6) OFF,} T_DataOFF = T_ScreenOFF_,

i.e., data-disabled period is equal to the screen-OFF period

as parameterized by user; (7)NoToggling, i.e., with ExpCO2

but without toggling cellular data interface; (8)w/o ExpCO2,

i.e., without listening on sensors, without writing metrics to

the smartphone’s local storage, and without toggling cellular data interface to obtain the base-line power consumption. We collected samples for the eight different scenarios to such extent that each scenario has a Standard Error (SE) of the mean

values less than 1 %. The Standard Error (SE) of the sample

mean for each scenario is calculated as: the standard deviation

101 102 ExpCO2 : NoToggling. (SE = 0.07)

ExpCO2 : TDataOFF = 3 minutes; TDataON = 1 minute. (SE = 0.09)

Fig. 3. Power consumption patterns for different available cellular data durations in Screen-OFF state.

of the sample data divided by the square root of the number of the samples. The CCDF plots are presented together with the Standard Errors for each scenario in Fig. 3. In addition, we

calculate the Gain Factors for each scenario (over 10_million

samples each), while taking thew/o ExpCO2scenario as

base-line and is calculated in Eq. 1. P¯

w/o ExpCO2 is the average

power consumption without ExpCO2; and P¯

Scenario is the average power consumption of any of the eight scenarios. Therefore, the larger the gain factor, the higher the saved energy.

The Gain Factors obtained through in-lab measurements

for different DataOFF durations is presented in Table I. The

TABLE I. THEGAINFACTORS FOR DIFFERENT SCENARIOS.

w/o ExpCO2 NoToggling 3 m OFF 9 m OFF 14 m OFF 19 m OFF 29 m OFF OFF

1 0.34 0.35 0.54 0.63 0.83 1.34 2.57

Gain Factor is less than1_{for the scenarios where the}T_DataOFF

duration is set to less than 29_{min. One reason is ExpCO2’s}

itself extra power consumption, which is likely caused by the

logging (listening and writing) process,i.e., without the latter,

the energy consumption is expected to decrease. Based on the

in-lab experiments,T_DataOFF = 29_{min and}T_DataON= 1_min

combination has a Gain Factor of 1.34_{, and expected to be}

even more when the energy consuming logging process is disabled. By taking the results obtained from this part as basis, we conducted a user study to identify the influence of the

(29_{min DataOFF,}1_{min DataON) combination on the end-user}

perceived quality during the smartphone’s screen-OFF state.

VI. USERSTUDY WITHEXPCO2

TheExpCO2has two main phases in the user study: (i)

In-troduction Period (min.2 _{days), (ii) Actual User Study Period.}

The Introduction Period is important for us to construct a base-line on the end-user’s interaction with the smartphone in real

life,e.g., user feedback, screen state changes, received

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smartphones. During the Actual Study Period, the functionality of ExpCO2 has changed such that the cellular data is enabled as long as the screen is ON. Once the screen is turned OFF, the cellular data is disabled automatically. ExpCO2 applied

TDataOFF to29min, and theTDataONto1min, which means

the data is enabled again after 29_{min screen-OFF duration}

and then enabled again only for one minute; next the data

is disabled again for another 29_{min. This cycle continues}

until the user turns ON the screen manually. When screen is ON, the user is able to submit a user feedback or MOS on spot anytime via Experience Sampling Method (ESM) [16]. We provided a user-friendly ESM interface for the subjects along experience from pervious studies [1]. The participants, who own Android smartphones, are chosen through an online survey. Once the user reads consent and confirms participation

via the online form, the user is allowed to install ExpCO2

on their smartphones. We asked users to submit MOS, user feedback, and any impairment during the study period, as soon as they appear. We deliberately did not tell the users about the network traffic change of the ExpCO2, or about the battery life of their smartphone to prevent bias. After the study, in the final interview with the users, we obtained more detailed knowledge on their experiences. We asked to the users questions regarding the battery life and the overall user perceived quality of the smartphone. This interview and the feedback obtained through ESM helped us to address the question “Do the users really need to receive the notifications when the smartphone is not used interactively?” If not, we may conclude that maximizing the data-disabled duration can save energy without influencing end-user QoE. The data analysis relies on the user feedback, and the battery levels. The battery discharge curve depends on factors such as the battery age and temperature [18]; however we assumed that this did not change significantly during the subjective study.

VII. USERSTUDYRESULTS

As we have conducted the user study with five users and three phone types so far, we did not run any statistical significance tests, however, the results obtained are highly indicative. The subjects were reluctant to give MOS ratings, and when they were asked the reason they claimed that they did not experience any difference, and it was perceived by the users that the smartphone was functioning as in daily life. Therefore, we focused on the qualitative feedback rather than the quantitative ones. The data collected during the Introduction Period and the Actual Study Period are presented

as different sections for each subject,S_{, in Table II. All}5_users

in the study have used GMail, GTalk, WhatsApp, and Viber. Additional user apps are presented in Table II. The median data- OFF/ON duration during screen-OFF were close to the

nominal (29_min/1_{min) values:}29_min/1_min;29_min/1.25_min;

26_min/2.2_min; 25_min/1_{min for the subjects} S1_-to-S4_,

re-spectively. All of the users replied “No” to the question:

“Have you realized any change, e.g., regarding the

notifica-tions, when your smartphone screen was OFF?”. When asked to the users: “Can you prioritize the cloud-based apps that

you really want to be notified?”, S2 _{replied: “WhatsApp,}

LinkedIn, Email, Facebook, Twitter”. Based on the ExpCO2

logs, and the prioritization of the apps,S2_{has used WhatsApp}

frequently (received422WhatsApp notifications in119hours),

but the subject did not complain about any notification latency.

TABLE II. USERSTUDYRESULTS

S1 - Samsung Galaxy Note2 Introduction Period (without manipulating the cellular data)

T

Study = 116hrs Mean Median Std. Size (Detected Events) Model R2

T_ScreenOFF(min) 22 4.2 64.4 279 Exp. 0.93

T_ScreenON(min) 2.6 0.5 5.5 279 Exp. 0.96

Battery Level (%) 48 47 26 7621 -

-Used Apps CloudMe, Verisure, Skype, LinkedIn, Shoutcast

S1 - Samsung Galaxy Note2 Actual Study Period (with manipulating the cellular data)

T_{Study = 75}hrs Mean Median Std. Size (Detected Events) Model R2

T_DataON(min) 36.2 1 106 37 -

-T_DataOFF(min) 58 29 164.5 36 -

-Battery Level (%) 74 79 20 5363 -

-Total WiFi Duration (hours) 1.45

DeviceOFF Duration (hours) 0

Feedback “it was normal.” Not realised any difference.

S2 - Samsung Galaxy S3 Introduction Period (without manipulating the cellular data)

T_ScreenOFF(min) 7.7 1.6 23.5 656 Exp. 0.95

Battery Level (%) 42 36 30 15783 -

-Used Apps Facebook, Instagram, LinkedIn, Twitter

S2 - Samsung Galaxy S3 Actual Study Period (with manipulating the cellular data)

T_DataON(min) 42 1.25 78.5 13 -

-T_DataOFF(min) 193 29 444 13 -

-Battery Level (%) 40 32 31 8692 -

Feedback Not realized any difference

S3 - Sony Xperia Introduction Period (without manipulating the cellular data)

T_ScreenOFF(min) 10.5 1.7 36 235 Exp. 0.92

Battery Level (%) 46 46 24 3730 -

-Used Apps LinkedIn, Skype, Facebook, Facebook Messenger, Twitter, News/Sports app S3 - Sony Xperia Actual Study Period (with manipulating the cellular data)

T_DataON(min) 38 2.2 91.5 37 -

-T_DataOFF(min) 32 26 49 37 -

-Battery Level (%) 55 54 27 1582 -

Feedback Not realised any difference.

S4 - Samsung Galaxy S3 Introduction Period (without manipulating the cellular data)

T_ScreenOFF(min) 32 4.9 84 177 Exp. 0.92

Battery Level (%) 48 47 29 5737 -

-Used Apps Anydo, Facebook, Facebook Messenger, Twitter, Skype

T_DataON(min) 51 1 214 18 -

-T_DataOFF(min) 18 25 13 19 -

-Battery Level (%) 60 71 26 1596 -

-Total WiFi Duration (hours) 0

Feedback Not realised any difference.

S5 - Samsung Galaxy S3 Introduction Period (without manipulating the cellular data) Skipped for this user due to technical issues

Used Apps Using Facebook through the browser

T

Study = 62hrs Mean Median Std. Size (Detected Events) Model R2

T_ScreenOFF(min) 7.9 1.2 22.4 391 Exp. 0.93

T_DataON(min) 138 0.4 421 12 -

-T_DataOFF(min) 144 0.3 421 12 -

-Total WiFi Duration (hours) 0

Feedback “I do not want to get notifications. Notifications for example for Facebook would probably stress the life out of me

and are absolutely switched off on my phone”

This might be due to the fact that the user expectation to receive a notification decreases when the user turns the screen-OFF, which needs to be investigated in more detail with more subjects. Some users interact with their smartphones frequently, while some do it less often such as the difference

in the number of screen-ON/OFF toggles forS1₍279_{times in}

116_{hours) and}S2₍656_{times in}119_{hours). We assumed that}

the probability distribution of screen- ON and OFF durations

is memoryless, i.e., user’s current screen- ON/OFF duration

does not depend on the users’ previous screen- ON/OFF durations. Thereby, we fit the screen- ON/OFF durations to an

exponential curve. We show that, for the subjects S1-S5, the

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the Introduction Period (R2

(Coefficient of Determination) ≥

0.91_{) with median durations} 0.5_min/4.2_min,0.6_min/1.6_min,

0.5_min/1.7_min, 0.25_min/4.9_min, 0.3_min/1.2_min,

respec-tively. While the smartphone was running as being discon-nected from the power source, the mean battery levels have

increased by54 %_,20 %_{, and}26 %_{, for}S1_,S3_{, and}S4_,

respec-tively during the Actual Study as compared to the Introduction

Period.S2_{has used}2.6_{hours of WiFi and the smartphone was}

completely OFF for21 _{hours, therefore the results belong to}

S2_{are not indicative to understand the improvement in energy}

levels for this particular user. Thereby, we focus on qualitative analysis based on any possible perceived quality degradation. Although, we cannot claim statistical significant conclusions due to rather high standard error values, the results are highly indicative.

VIII. DISCUSSION ANDLIMITATIONS

ExpCO2’s power saving depends on human

user-smartphone interaction. We recommendExpCO2for the users

who check emails less frequent than every 29_{min to save}

energy. In addition, ExpCO2 might change user behaviour, particularly for those users who receive too many notifications and being interrupted by them often in daily life. For those users, ExpCO2 provides an extra benefit such as reduced

interruption by notifications ofe.g., cloud-based apps. Another

drawback of frequent interruption during screen-OFF state is that, each interruption possibly causes users to turn the screen ON each time, which also increases the power consumption

re-lated to other components,e.g., backlight display. As ExpCO2

merely controls cellular data only during screen-OFF state, it is suggested for particular users whose smartphone interaction is based on the received app notifications; ExpCO2 might help them change their user smartphone interaction in a good way, being less interrupted (save time), and in parallel save energy.

During the Actual User Study, subject S1 _{wasn’t able to}

listen to Internet radio while driving. Therefore, we added an extra feature to ExpCO2 so that the radio was kept ON even when the screen is OFF based on the user preference. But, we asked the user to disable that feature after driving. In

addition, the battery saving forS5 _{is unknown, as there were}

technical problems during the Introduction Period. Based on

the qualitative results of S5_{, the subject has not realized any}

degradation in the perceived quality on the cloud-based apps during the Actual Study Period.

IX. CONCLUSIONS ANDFUTUREWORK

In this paper, we present the power consumption patterns of Samsung Galaxy S smartphone during a set of data network activities on the cellular radio, and then developed ExpCO2 that enables or disables the cellular data at predefined

inter-vals, while extending the non-Idle periods. ExpCO2 reduced

asynchronous network activities of individual cloud-based apps and let them start network activities simultaneously during

the 1_{min DataON period. We showed that the energy gain}

of such solution (called ExpCO2) increases with the dura-tion of DataOFF periods during screen-OFF state. Based on the obtained quantitative results through in-lab experiments,

a DataON/DataOFF cycle with 29_{min DataOFF, and} 1_min

DataON duration during screen-OFF state indicated a 34%

energy gain. We confirmed with the qualitative subjective study

that the user perceived quality is not degragaded with this DataON/DataOFF duration combination.

The indicative results acquired in this paper have poten-tial for future work. We will investigate the network traffic, conduct a user study with more users, and tune the DataON and DataOFF durations based on user-smartphone interaction requirements in terms of notification latency. We encourage the cloud-based Android app developers to use the Alarm-Manager [24] in Android API, so that the apps can broadcast the network activity events to the other listening apps, which could let all the apps to synchronize their network activities in an autonomous and reliable manner, and save energy.

REFERENCES

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