Energy Measurements Campaign for PositioningMethods on State-of-the-Art Smartphones

Julien EberleEPFL - Lausanne

Email: julien.eberle@epfl.ch

Gian Paolo PerrucciNokia Research Center - Lausanne

Email: gianpaolo.perrucci@nokia.com

Abstract—Mobile phones have been undergoing a breathtakingevolution over the last years, starting from simple devices withonly voice services, nowadays they are referred as smartphones,powerful devices able to offer appealing services. One of theservices that has recently become popular is referred as LocationBased Services that use contextual and location information toenhance mobile user experience. As a basic assumption, this kindof services needs to be continuously aware of the phone’s location.The challenge emerges from the trade-off needed between thelocation precision and the energy consumption of the positioningmethods, the most precise being also the energy-hungriest. Thispaper presents the results of an extensive energy measurementscampaign that was conducted on different sensors and positioningmethods on a smartphone. Results will help researchers and de-velopers to design energy efficient and more accurate continuouslocation tracking algorithms to support Location Based Services.

I. INTRODUCTION AND MOTIVATIONS

Mobile phones have been undergoing a breathtaking evo-

lution over the last years, starting from simple devices with

only voice services towards the transition to smartphones

offering novel services such as mobile Internet, multimedia,

high datarate connectivity and geolocation among others. The

latter capability enables the so called Location Based Services

(LBS) that use contextual and location information to enhance

mobile user experience. One example of LBS application is

Foursquare[1] that logs visited places and allows users to

earn points and badges based on the frequency of the visits.

Other popular examples are Brightkite [2], a geolocalized

micro-blogging platform and Google Latitude [3] that can be

used to track friend’s location. All these services have one

thing in common: they all need to keep continuously track of

the position of the phone. However, smartphones are battery

driven devices and therefore have a limited amount of energy

available. For this reason in the past years there have been

several attempts to develop algorithms for continuous location

tracking that would not have a high impact on the battery life

of the devices.

The first algorithms were implemented using the single sensor

approach, as for example in the TRAC-IT project [4], a trans-

portation survey conducted in US, where GPS placed in cars

and trucks were used to track vehicles. As an other example, in

[5] and [6], Received Signal Strength (RSS) of the GSM signal

and triangulation are used for inferring the position of the

phone. Finally in [7], [8] and [9], Wi-Fi and RSS was used to

determine the position. However, each of the above mentioned

methods have limitations in terms of availability (Wi-Fi access

points are not ubiquitous, especially in rural areas), precision

(positioning using GSM does not always offer sufficient accu-

racy) or energy consumption (GPS is very energy hungry). In

recent years, algorithms developed are using a combination of

sensors available on smartphones to improve the performance

of continuous location tracking. For example Ben Abdesslem

et al.[10] for their algorithm always use the most energy

efficient positioning method when available. In practice they

turn GPS on only if the accelerometer has detected a motion

and if no Wi-Fi previously geolocated are sensed. Similarly

in RAPS (Rate-Adaptive Positioning System), a framework

developed by Paek et al. [11], the accelerometer, Cell-ID/RSS

(Received Signal Strength), user activity history and bluetooth

communications are used to decide whether to turn GPS on

or not. The CompAcc, algorithm developed by Constandache

et al.[12], uses the dead reckoning method. Starting from a

known location given by the Assisted GPS, the compass and

accelerometer evaluate the displacement and allow computing

the current location. The Assisted GPS is used from time to

time for recalibration.

One of the biggest challenges when developing this kind of

algorithms is that they can only be evaluated through field

testing. Although, when a comparison with other algorithms

is needed, it is not trivial to reproduce the same conditions of

the field testing used for the algorithm taken as a reference.

Therefore a fair comparison is not possible because a common

metric is missing. Battery life is often used for comparing dif-

ferent algorithms but the drawback is that often different phone

models are used, making the comparison unfair. Moreover, the

capacity of the batteries degrades with the time and therefore,

even if the same models are used, the battery capacity may be

altered.

Another challenge for researchers is the lack of knowledge

on the energy consumption for each of the smartphones’

sensors involved in the algorithms. In fact, to the best of our

knowledge, there is no prior work in the literature investigating

this issue in an exhaustive manner.

In this paper we present the results of an extensive energy

measurements campaign on several positioning methods and

sensors available on state-of-the-art smartphones. We believe

that by knowing the detailed energy consumption of the single

sensors, the development could be faster, the algorithms more

energy efficient and most importantly, the performance of

The 8th Annual IEEE Consumer Communications and Networking Conference - Special Session on Location Aware Technologiesand Applications on Smartphones

978-1-4244-8790-5/11/$26.00 ©2011 IEEE 937

TABLE IDESCRIPTION OF ALL THE EXPERIMENTS

Mode Environment Duration Duty cycleOff On

Offline Indoor 10 h - -

Online 2G Indoor 10 h - -

Online 3G Indoor 10 h - -

Net.Based(2G - 3G)

Indoor 2 min 10 s 10 s

Net.BasedContinuous

Indoor 1 min - -

Assisted GPS(2G - 3G)

Outdoor, not moving 1h 10 min 5 min

GPS with fix Outdoor, walking 15 min - -

GPS with fix1min interval

Outdoor, walking 20 min 1 min 5 min

GPS with fix10min inter-val

Outdoor, not moving 1 h 10 min 10 min

GPS with fix1h interval

Outdoor, not moving 3 h 1 h 10 min

GPS with fix10s interval

Outdoor, walking 15 min 10 s 5 min

Cell-ID Outdoor, driving 20 min 1 min -

WLAN Outdoor, walking 20 min 10 s -

Accelero-meter

Indoor, not moving 15 min 1 min 1 min

different algorithms can be compared with a common metric.

In the first part of the paper (Sections II and III), we present

the software used in our test-bed for the measurements and

the methodology for the experiments. Then in Sections IV and

V we summarize the results and discuss the most interesting

findings.

II. MEASUREMENTS CAMPAIGN

In this section we describe the hardware, software and the

methodology used for the measurements campaign.

A. Hardware

The smartphone used for the measurements is the Nokia

N95 8Gb. As most of the smartphones on the market, it

features the most common sensors and positioning capabilities

(GPS, WLAN, accelerometer, Bluetooth, etc.).

B. Software

For measuring the power levels we used a software devel-

oped by Nokia, namely the Nokia Energy Profiler (NEP)[13].

The NEP is able to log current, voltage, power, CPU usage

and network traffic, among others, directly on the phone. The

complete test-bed was built with Python for Symbian (PyS60)

[14] as it offered APIs that allow accessing the sensors and

positioning methods and can control the NEP. To avoid user

interaction which could lead to noise in the measurements

(backlight on, for example), a Python script was used for

switching on and off the sensors as well as starting the NEP.

The data logged was first kept in the memory and, at the end

0

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0 60 120 180 240 300 360 420 480

Pow

er [

mW

]

Time [s]

Power FixCPU

0102030405060708090100

CPU

Loa

d %

Fig. 1. CPU and Power measured as the GPS was on. The fix indicate whenit returned a position. The GPS was turned off after 300 seconds, but thepower dropped only after 330 seconds.

of the measurement, written in a file to avoid extra energy

consumption due to writing process on the file system.

C. Methodology

All the tests were done using the above mentioned test-bed.

To limit the noise in the measurements, all the parts not needed

for the experiment, such as Bluetooth and the screen are

turned off before measuring. The positioning methods tested

are: GPS, Assisted GPS and the Network based positioning

method, later on referred as the NET. In addition some

sensors that can be used in such algorithms were also tested:

accelerometer, WLAN scanning, Cell-ID scanning. Finally, to

compute the overhead of each positioning method or sensor,

the standby power level was also measured. Table I describes

the conditions in which each test was made. In this table, the

”Duration” refers to the total duration of the experiment, the

duty cycle is divided in two parts: the time during which the

sensor is turned off and the time during which the sensor is

on. All the tests were repeated several times and the results

presented are obtained by averaging all the measurements.

III. EXPERIMENTS DESCRIPTION

A. Positioning methods

1) GPS: The GPS (Global Positioning System) is a passive

location sensor. Nowadays the chip is so small that it can easily

fit in most of the newest smartphones. The two main issues

related to GPS are the availability of the signal and the Time

To Fix (i.e. the time from starting the GPS to the first position

returned by the system). The GPS receiver, hereafter GPS,

needs some information to calculate its position. Basically it

needs to know the timetable of all satellites (almanac), the

current trajectories of visible satellites (ephemeris) and the

time with a very high precision. The Time To Fix or TTF

depends on the state of the GPS (cold/warm/hot). T. Clark

gives a detailed explanation on his website [15]:

• Cold: the GPS has absolutely no information.

• Warm: the GPS knows already the almanac but still needs

to download the ephemeris for the satellites it needs to

listen to.

• Hot: the GPS has already all the data and can directly

connect to the satellites.

938

1 62500

data�DOWN data�UP Power Fix

0.6

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1

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1.6

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er�[W

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ata�

Rate

�[bit/

s]

0

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500

0 5 10 15 20 25 30 35 40 45 50 55 60

Da

Time�[s]

Fig. 2. A-GPS using network assistance (data UP and DOWN) in the first10 seconds on the 2G network, to allow the GPS to get quickly a fix. Thepower stays then at the same level as in the GPS only experiment.

The theoretical values for the TTF are typically a few seconds

for a hot start, around 3 minutes for a warm start and more

than 13 minutes for a cold start.

In our experiment, the GPS was first started before the

experiment, to get a fix. This way, the measurements are done

in the same conditions during the whole experiment (i.e. with

a hot start).

As one can see on the Figure 1, representing one minute

interval scanning, the time off between two scans in the power

plot is only 30 seconds. The GPS was turned off by the

software after 300 seconds, but the power level only gets down

after 330 seconds. Some other experiments were done and we

found that shutting down the GPS chip on the N95 8G needs

30 seconds1. This graph shows also clearly that the GPS needs

more CPU when it has a fix than for the search phase, but this

has almost no visible impact on the power consumption.

2) A-GPS: Most of the GPS-enabled smartphones also have

an Assisted GPS or A-GPS. The purpose is to use the other

communication channels like UMTS or GPRS2 to download

the satellites data directly from the network and therefore

reduce the time to get the first fix. Most of the time the

GPS receives enough information, like the ephemeris and the

almanac, to make a hot start in a few seconds.

This test was done on the roof of a building to ensure a clear

sky and the phone was connected to the 3G. The assisted GPS

was run every 15 minutes during 1h, but the network assistance

was only used before the first fix of each interval. As soon as

the GPS is synchronized and a location is computed, it doesn’t

need the assistance anymore for the subsequent fixes. At each

new start, after 10 minutes of inactivity, the assistance was

used in average 50% of the time.

This test was repeated a second time with the network

forced to use 2G and the trace of the power levels is shown in

Figure 2. The difference between the two experiments is that

the communication time is a bit longer when using 2G due

to the slower data rate compared to 3G, but this has a quite

1This is not the case for all smartphones. For example, the GPS chip onthe Nokia 5800 does not need any extra time to shut down.

2GPRS/EDGE are defined in the 2.5G protocol and UMTS in the 3G one.But for simplicity reason and as there are only two options to force the networkin the Nokia N95 8G, we will refer to 2G and 3G networks.

2500300035004000

1500

2000

bit/

s]

W]

Power Data�UP data�DOWN Fix

05001000150020002500

0

500

1000

0 10 20 30 40 50 60 70 80

Data

�rate

�[b

Pow

er�[m

W

Time�[s]

Fig. 3. Power used for getting the location with the NET positioning methodon the 3G network. In the 10 seconds interval, it gets twice the position.

low impact on the time to get the first fix (1 second more in

average).

3) Network based: The 2G and 3G networks define a set

of base services and among them, the Location Service. The

Nokia N95 8Gb proposes an implementation of an UE-assisted

(User Equipment) positioning method, accessible through the

GPS API interface. It behaves as if it was a GPS, able to get its

location as long as the phone is in a place covered by a cellular

network, but with a lower accuracy. It highly depends on the

density of the cellular Base Stations, and it can vary from 100

meters in a city to several kilometers in the countryside. This

method is called the Network based positioning method, but,

for simplicity reason, it will be referred in the following as

NET.

During this test, the phone was indoor, not moving and with

the network forced to 2G. Each time the positioning was turned

on, it initiated the connection and, after 5 seconds, returned

the location. Then it only needed 3 seconds more for the next

value to be returned. Like for the GPS, it needs some time to

shutdown: i.e. 2 to 4 seconds. Figure 3 shows the experiment in

which the positioning was turned on and off every 10 seconds.

Using the 2G instead of 3G increased the time to get a fix

from 5 to 6 seconds and the time to shutdown is from 5 and

8 seconds.

B. Sensors

1) Accelerometer: The internal accelerometer is able to

detect the accelerations on the 3 axes (X,Y and Z). They

can be accessed by a Python API and the values are updated

at a frequency of 30 Hz. The information given by the

accelerometer can be used in a lot of different ways by location

tracking algorithms, such as for example to count the steps or

to detect user’s activity and movements.

We made two experiments: one with continuous scanning

and one with 1 minute scanning intervals because we wanted

also to know the time to shutdown, until the power level goes

back to its idle value.

As we can see in Figure 4, the accelerometer needs no ad-

ditional time to turn on and the power level drops immediately

after shutting down the sensor. Switching off the accelerometer

doesn’t cost extra energy. So the phone can afford switching

on and off very frequently.

939

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d�%

CPU Power

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100

150

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Pow

er�[m

CPU

�load

0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240

Time�[s]

Fig. 4. The accelerometer was turned on an off every minutes. The powerand CPU load dropped immediatly.

2) WLAN scanning: Each wireless access point sends bea-

cons at regular time intervals, advertising the available WLAN.

WLAN scanning can be used as an input for positioning as

in [9] and [7], by having a mapping between a WLAN and

its location. The aim of this experiment is to know how much

energy a scan costs. During the test the user was walking

across the university campus of EPFL in Lausanne. This

exposed the phone to different conditions, from places without

any WLAN to some with up to 32 different WLANs visible.

By looking at the Figure 5 it seems that the number of WLAN

has an impact on the CPU load, but less on the power level.

Despite of the other sensors, for WLAN scanning is not that

important the power level as it is the energy spent for each

scan.3) Cell-ID scanning: In the 2G/3G network each Base

Station has a unique ID and covers a certain area, also referred

as a cell. The mobile phone being connected to that base

station can query the Cell-ID and, by using a geolocalized

database, find its position. The measurement was done in a

car driving from the campus to the city. To get the cell-ID no

special scan is needed, the values are anyway logged on the

phone for handover reasons, so it is enough to read the value

from the memory which does not cost any overhead.

C. Standby mode1) Online: In all the previous measurements, the power

needed for the phone to get connected to the network is

included. As we want to compute the overhead of each sensor

and positioning method, we need to know which part is used

by the phone just to handle the network connection when in

standby. Therefore some measurements were conducted with-

out triggering any sensors and leaving the phone in standby.

The Nokia N95 8Gb, like many other modern smartphones is

able to connect to both 2G and 3G network. As they need a

different power level, the experiment was repeated for both.For this experiment the phone was indoor, not moving for

10 hours. The network mode was firstly forced to 2G and for

the second test, to 3G.2) Offline: Some measurements, such as for the GPS, were

done with the phone in ”flying mode”, without any cellular

network connection. Therefore we needed to measure the

consumption of the phone in ”flying mode” as well, so that

the net consumption for GPS could be computed.

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Nbr

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Pow

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nd C

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Power CPU WLANs

Fig. 5. The CPU load of a WLAN scan depends on the number of WLAN,but the power level seemed less sensitive to that parameter.

IV. RESULTS AND DISCUSSION

In Table II, results from the measurements described in

Section III are shown. As explained on its home page the

values given by the Nokia Energy Profiler need to be adjusted3.

The values shown in Table II have been adjusted according to

the NEP guidelines.

The first column gives the average power consumption over

all measurements for each sensor. This is only the overhead,

obtained by removing the consumption measured in the tests

9, 10 or 11, depending on the case. The four next columns

give the time needed for the sensor to return the position. As

it can vary depending on how much time elapsed from the last

position returned, the column is divided into 4 parts. Typically

the Time To Fix of the GPS depends on which information is

already available and if it is still valid. The time to shutdown

is the time from the last position returned (or the time at which

the sensor was stopped) to the time at which the power level

goes again to its idle value. In fact it can happen that when the

software turns off the sensor, the hardware needs some time

to completely shut it down. The precision gives the accuracy

in case of a positioning method. And finally the battery life is

computed as if the experiment would continuously run with

a battery capacity of 4.4 Wh (as the one used by the N95

8 Gb). It is important to note that the Assisted GPS has the

same duration of the battery life as the GPS. This is due to

the assumption that A-GPS is used only to get the first fix and

then normal GPS is used. To summarize the results in Table

II:

GPS is the power-hungriest positioning method, but the

energy needed by the GPS to get a fix depends on when it was

turned off last time. Depending on the model of smartphone

used, the time needed to shutdown must be taken into account

when designing an algorithm that would turn on and off the

GPS.

A-GPS has a network assistance which helps saving time

and it has the same energy consumption as using 30 seconds of

3”For guidance, note that a Nokia Energy Profiler measured power (orcurrent) consumption of 0.05W (or 14mA) is 0.07W (or 20mA) in reality”[13].On the Forum Nokia the complete formula is given: ”Active use cases: -2%in 0.4W (100mA) region, ie. NEP mA = 0.98 x True mA. Standby accuracy:-30% in 0.04W (10 mA) region ie. NEP mA = 0.7 x True mA. This can varybetween phone models”[16]

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TABLE IISUMMARY OF EXPERIMENTS RESULTS. THE POWER CONSUMPTION IS ADJUSTED ACCORDING TO THE NEP RECOMMANDATIONS3

.

Test Power [mW] Time (Energy) to get position [s] ([J]) Time needed toshutdown [s]

Precision [m] Battery life [hour]

last call < 10s 1min 10min 1h

Positioning methods1. GPS ∼356 3 (1.07) 30 (10.7) 40 (14.2) 200 (71.2) 30 10 11.5

2. AGPS 3G ∼973 11 (10.7) 30 10 11.5

3. AGPS 2G ∼765 12 (9.18) 30 10 11.5

4. Net 2G ∼663 3 (1.99) 6 (3.98) 6 (3.98) 6 (3.98) 6 100 - 1000 6.2

5. Net 3G ∼1150 3 (3.45) 5 (5.75) 5 (5.75) 5 (5.75) 3 100 - 1000 3.7

Sensors6. Accelerometer ∼124 NA 0 NA 28.1

7. WLAN ∼800 0.85 (0.68) 0 100 38.7

8. Cell-ID ∼0 0 0 1000 58 - 75

Standby9. Online 2G ∼60 74.8

10 .Online 3G ∼77 58.3

11. Offline ∼36 124.7

GPS to get the first fix, but only needs 11 seconds. Of course

the 2G network is a bit slower, but as it doesn’t transfer too

much data, it consumes less energy.

Net based can quickly get the position everywhere there is a

2G/3G network for a bit less energy than the A-GPS, but with a

reduced precision (up to several kilometers in the countryside).

It needs less time to get the position if the connection is

already established (<10s column).

Accelerometer can be used for very short time interval to

detects phone’s motion, without spending too much energy to

start and stop the sensor.

WLAN scanning has a high power level, but for a short

time. Then it has a very low energy consumption. By using a

database of known WLANs with their location, it become the

second more precise positioning method.

Cell-ID scanning comes for free, but with a precision not

better than the NET, if used with a database of Cell-ID and

locations.

V. CONCLUSION

This paper presents the results of an extensive measurement

campaign that was conducted aiming at measuring power

levels, speed and accuracy of several positioning methods

and sensors present on State-of-the-Art smartphones. The

measurements were done on the Nokia N95 8Gb, using the

Nokia Energy Profiler and Python scripts as test-bed. Results

summarized in Table II, can be used as a guideline for

researchers and developers to design more accurate and more

energy efficient algorithms for continuous location tracking to

enable novel Location Based Services on smartphones.

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[11] J. Paek, J. Kim, and R. Govindan, “Energy-efficient rate-adaptive gps-based positioning for smartphones,” in MobiSys ’10: Proceedings ofthe 8th international conference on Mobile systems, applications, andservices. New York, NY, USA: ACM, 2010, pp. 299–314.

[12] I. Constandache, R. R. Choudhury, and I. Rhee, “Towards mobile phonelocalization without war-driving,” 2010.

[13] Nokia, “Nokia energy profiler quick start,” http://www.forum.nokia.com/Library/Tools and downloads/Other/Nokia Energy Profiler/Quick start.xhtml.

[14] ——, S60 Module Reference, Nokia, 2009.[15] T. Clark, “http://gpsinformation.net/main/gpslock.htm.”[16] Forum Nokia, “http://discussion.forum.nokia.com/forum/

showthread.php?t=160912&page=6.”

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