Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Abstract

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone's battery.

Full text

Received December 31, 2015, accepted February 10, 2016, date of publication February 19, 2016, date of current version March 17, 2016.
Digital Object Identifier 10.1109/ACCESS.2016.2532745
Greener and Smarter Phones for Future Cities:
Characterizing the Impact of GPS Signal
Strength on Power Consumption
LO’AI A. TAWALBEH1,2, (Senior Member, IEEE), ANAS BASALAMAH2,
RASHID MEHMOOD3, (Senior Member, IEEE), AND HALA TAWALBEH4
1Computer Engineering Department, Jordan University of Science and Technology, Irbid 22110, Jordan
2Computer Engineering Department, Umm Al-Qura University, Mecca 21955, Saudi Arabia
3High Performance Computing Centre, King Abdul Aziz University, Jeddah 21589, Saudi Arabia
4Computer Science Department, Jordan University of Science and Technology, Irbid 22110, Jordan
Corresponding author: L. A. Tawalbeh (latawalbeh@uqu.edu.sa)
This work is funded by grant number (11-INF2060-10) from the Long-Term National Science Technology and Innovation Plan
(LT-NSTIP), the King Abdul-Aziz City for Science and Technology (KACST), Kingdom of Saudi Arabia.
ABSTRACT Smart cities appear as the next stage of urbanization aiming to not only exploit physical and
digital infrastructure for urban development but also the intellectual and social capital as its core ingredient
for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city
systems. The most important of these sensing devices are smartphones as they provide the most important
means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The
battery lifetime of smartphones is one of the most important parameters in achieving good user experience
for the device. Therefore, the management and the optimization of handheld device applications in relation to
their power consumption are an important area of research. This paper investigates the relationship between
the energy consumption of a localization application and the strength of the global positioning system (GPS)
signal. This is an important focus, because location-based applications are among the top power-hungry
applications. We conduct experiments on two android location-based applications, one developed by us, and
the other one, off the shelf. We use the results from the measurements of the two applications to derive a
mathematical model that describes the power consumption in smartphones in terms of SNR and the time
to first fix. The results from this study show that higher SNR values of GPS signals do consume less
energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our
knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR)
of satellite signals will cause relatively higher power drain from a smartphone’s battery.
INDEX TERMS Green mobile computing, energy efficiency, smart cities, smart phones, signal strength,
power model.
I. INTRODUCTION
Smart cities appear as the next stage of urbanization,
subsequent to knowledge-based economy, digital economy,
and intelligent economy. Smart cities aim to not only exploit
physical and digital infrastructure for urban development but
also the intellectual and social capital as its core ingredient
for urbanization. A city can be defined as ‘‘smart’’ when
‘‘investments in human and social capital and traditional
(transport) and modern (ICT) communication infrastruc-
ture fuel sustainable economic growth and a high qual-
ity of life, with a wise management of natural resources,
through participatory governance’’ [1]. Smart cities can also
be seen also as ‘‘converged ubiquitous infrastructures’’ and
‘‘complex systems of systems’’.
A number of trends have contributed to the development of
smart cities. These include a pressing need for environmental
sustainability, and peoples’ increasing demands for personal-
ization and mobility. Several ‘smart cities’ around the world
are being built from scratch while many of the modern cities
are gradually moving towards becoming ‘smart’. We are
now used to of Google-Maps, which enables us to navi-
gate to our destination avoiding congested routes based on
real-time traffic data. Mobile applications such as
Citymapper [2] allows us to travel through the city using
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L. A. Tawalbeh et al.: Greener and Smarter Phones for Future Cities
public and other transport modes providing near real-time
information. Many other developments such as Internet of
things (IoT) for smart cities [3], semantic web for smart cities
data [4], smart emergency management systems [5], auto-
nomic transportation systems [6], traffic-aware street lighting
scheme [7], planning and land administration [8], strategies
for smart cities [9], privacy-aware participation [10], crime-
sourcing [11], community resilience [12], smart grid and
metering, and many other proposals [6], [13]–[22] are shap-
ing our move towards the smart cities era.
At the heart of smart cities is the concept of harnessing
the power of data from sensors to understand and manage
city systems. Sensors therefore are playing a critical role
in enabling smart cities technologies and systems. Sensors
provide the pulse of the city helping it to apply some control
measures before a major breakdown happens. The city pulse,
enabled by sensors and streams of information, also facilitates
the citizens with a high quality of life.
Perhaps the most important of these sensing devices are
smartphones (and other personal devices such as tablets).
This is mainly because smartphones provide the most impor-
tant means to connect the smart city systems with its citizens,
allowing personalization and co-creation. Smart phones cur-
rently have 14 or more sensors to monitor the environment
and provide various facilities to the user [23]. These include
the accelerometer, gyroscope, magnetometer, proximity
sensor, light sensor, barometer, thermometer, air humidity
sensor, pedometer, heart rate monitor, fingerprint sensors,
harmful radiation detector, hall sensor, gesture sensor, micro-
phone and the cameras. It is expected that gas sensors
will also be integrated into smartphones enabling indoor air
quality monitoring [24]. Smartphones also provide powerful
computing abilities and are being used in plethora of appli-
cations and use cases, such as smart car parks [25], accelera-
tors for ecommerce, fitness and health [26], [27], connected
cars, participatory smart citizenship, social behavior change
interventions [28], and many of the smart city applications
that we have mentioned above. Moreover, while the UN
statistics about doubling of the global urban population by
2050 is causing nightmares for city managers and politi-
cians, this increasing number, considering the decreasing
smartphone prices, is likely to provide fine grained, dense
information about the city to the public and other stakeholders
(due to the increasing smartphone ownership among urban
populations).
The discussions given above suggest that the popular-
ity and applications of smart mobile devices, and hence
the industry, will continue to grow at extraordinary rates.
Different studies also show that in the near future handheld
devices will be much more marketable for web browsing
and other functionalities compared to the personal computers.
The contemporary smartphones are increasingly considered
as handheld computers rather than as phones. This is in
part due to their powerful on-board computing capability,
large memories and screens, and open operating systems that
support application development [29]. Unfortunately, as a
result, applications that are designed for mobile devices are
getting computationally heavier and their complexity is on the
rise. The common applications that are run on mobile devices
include voice and video based applications (skype, whatsapp,
etc.), video games, navigation applications, internet access
and web browsing applications.
Many applications for mobile devices make use of the
user’s location information to provide various services and
enhance user experience. These applications include, among
others, mapping applications (e.g. Google maps), chatting
applications (e.g. tango, WhatsApp, and Viber), and social
network applications (e.g. Facebook, and twitter). For exam-
ple, travel and navigation related applications make use of
the users’ locations to guide them throughout their journeys;
informing them, based on their preferences, of the best routes
and means to get to their destinations. Similarly, user’s loca-
tion can be used by an application to provide them with a near-
est point of interest or the physical proximity of their friends.
There are multiples technologies to find user’s location using
smartphones. These include GPS, its variants, and WiFi.
Mobile devices support portability by using rechargeable
batteries. Batteries obviously need to be very small in size to
keep the handheld devices light and small. A Mobile device
consumes energy from its battery as long as the device is
on and running. The energy drawn from a battery depends
on the number of applications and their energy requirements.
Some applications are much heavier than others in terms of
their energy requirements. Applications that require identifi-
cation of user location to provide their services are among
the top power-hungry applications. This is because of the
fact that localization technologies, particularly GPS (Global
Positioning System), have high processing and communi-
cation costs. A continuous use of localization applications
typically leads to energy drain from a battery in few hours.
The battery lifetime for handheld devices is one of the most
important parameters in achieving good user experience for
the device. For these reasons, management and optimiza-
tion of handheld applications in relation to their power con-
sumption is a highly researched topic in mobile handheld
computing.
In this paper, we assert that GPS signal strength not
only affects location sensing performance but also the actual
consumed power from a smartphone battery. Specifically,
we investigate and analyze the quantitative relationship
between the SNR (Signal-to-Noise-Ratio) of the GPS satel-
lite and the amount of power consumption while using a
localization service.
We develop an Android mobile application for the power
consumption related measurements. We use the results from
the measurements of our developed application, as well as
from an off the shelf application, to derive a mathematical
model that describes the power consumption in smartphones
in terms of SNR and the TTFF. The results from the study
show that higher SNR values of GPS signals do consume
less energy while low GPS signals causing faster battery
drains. To the best of our knowledge, this is the first study
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that provides a quantitative understanding of how the poor
strength (SNR) of satellite signals will cause relatively higher
power drain from a smartphone’s battery.
The rest of this paper is organized as follows: section II
presents the motivation behind this research followed by
the literature review in section III. Section IV gives related
background, and section V presents the trace analysis. Our
mathematical model is presented in Section VI. We conclude
this paper in section VII.
II. MOTIVATIONS
Nowadays smartphone users search for power plugs instead
of network connectivity, because of the gap between smart-
phone development and battery enhancement and because
they use their smartphones almost for everything, so battery
is a critical challenge for this technology. Many emerging
applications for different services are implemented for smart-
phones. Several smartphone new applications demand loca-
tion positioning systems to provide location based services.
Many methodologies are used to provide such services but
GPS stays the best among its alternatives even it is the
hungriest for power because of its accuracy.
In our work, we try to highlight the problem of
GPS component high power consumption by concentrating
on GPS satellites signal strength effect on battery energy
drain in order to help in saving energy in the component level
so user’s high expectations can be met. To proof our concept
we used one android location based application of the appli-
cations available on Google play store and we built another
one and named it ‘‘GPS SNR’’. Our application uses GPS
hardware as a location provider that monitors GPS satellites
and requests location coordinates update every 20 seconds.
In this part, we motivate our work by highlighting some
results. Let us meditate our major experiment that is running
one LBA (location based application) on LG Nexus 4 smart-
phone for 1 hour indoor and outdoor. In one hour, running
the LBA we choose from Google play store indoor where
satellites SNR didn’t exceed 25 (which is considered as bad
SNR) consumes 21% of the mobile battery and such high
power consumption can bring down the battery in like 5 hours
for continues GPS sensing. Running the same application
outdoor where satellites SNR gets stronger and reaches 41
(which is considered as good SNR) consumes only 13% of the
mobile battery. From this experiment, we can simply observe
that power consumed under good satellites signal strength can
be reduced to about 38% as compared to power consumed
under bad satellites signal strength.
III. LITERATURE REVIEW
Lately, smartphones spreads widely and rapidly for many
uses, at the same time using periods of smartphones is
decreasing continuously as screens get wider and bigger
and loads get heavier. Many researches showed how energy
consumption in smartphones battery can be much efficient
and surveyed several techniques and solutions to reduce
energy consumed from battery and increase its lifetime
without affecting any functionalities in order to optimize
smartphone’s architecture and software such as what is pro-
posed in [30] that finds out how system’s components waste
power for unnecessary usage.
Different studies have analyzed energy drain from smart-
phone battery and many researches have been made about
what apps and services drain energy from batteries the most
and different works measured weak and strong WiFi and
3G and other wireless interfaces signal strength impact on a
battery power consumption. Researchers in [31] performed a
measurement study for WiFi and 3G signal strength experi-
mented by 3785 users used their smartphone daily for peri-
ods between 1 and 19 months. This research showed that
variations of WiFi and 3G signal strength cause variations of
power consumption rates from smartphone batteries by quan-
tifying and breaking down the impact of poor WiFi and 3G
signal strength on all relevant layers of the network stack.
Authors of [32] established the relationship between power
consumption and signal strength and they showed that energy
cost of communicating is affected by cellular network sig-
nal strength. In other words, poor signal strength raises
the energy cost of communicating and good signal strength
reduces it. On the other side, they developed a track-based
signal strength prediction and energy-aware scheduling algo-
rithms. In [33], the authors analyzed energy consumed for
different workloads in different components of WiFi based
phones and measured the power draw of WiFi-based phones
to increase slightly under poor signal strength, when dynamic
power control is enabled. In [34], an in-depth study of power
dissipation of smartphone components is performed and the
researchers found that GSM dissipates 30% more energy
when transferring at poor signal strength. Choi [31] studied
the waste power from different smartphone components by
setting different usage scenarios and analyzing each compo-
nent behavior. Components such as CPU, LCD, GPS, WiFi,
Bluetooth, etc. . . .
In our research, we focused on another source of power
consuming sources for smartphone battery. We studied GPS
satellites signal strength effect on the smartphone battery. All
smartphones use different locating methods to estimate loca-
tions precisely to provide location based services. However,
it is power hungry; GPS is the preferred positioning system
because it is the most accurate among all the alternatives.
Our work is one of few measurement studies of GPS satel-
lites signal strength effect on battery drain of smartphones but
there are many researches provided location sensing frame-
works that improve energy efficiency of location sensing.
Authors of [35] considered the power starving location sens-
ing process and succeeded to reduce GPS usage for location
determination by up to 98% and to improve battery lifetime
by to 75%. In [36] authors concentrated on the less accuracy
issue of GPS in urban areas so they designed a rate-adaptive
positioning system that uses different techniques to decide
when to turn on GPS and when not and then evaluated their
implemented system for different experiments on modern
smartphone. Their experiments showed that battery lifetime
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is increased by the factor 3.8 comparative to it when GPS is
always on. In research [26], GPS power model was described
for the first time. They studied the effect of many GPS-
related variables on power consumption such as number of
satellites detected, and signal strength of each satellite while
considering the state of GPS (active with many satellites
available, active with few satellites available and sleep). Their
study showed that energy drained for GPS depends strongly
on weather GPS component is active or in sleep mode and
it has little dependency on the number of satellites available
or the signal strength. In our research, we are proving the
opposite of this idea. We will show in measurements that GPS
signal strength affects power consumption in smartphone
battery.
IV. BACKGROUND
In this section, we review power states that smartphone expe-
riences while sensing for GPS satellites signals to determine
a specific location.
As smartphones commonly use rechargeable batteries for
power supplying and batteries mostly take from one hour
and a half to four hours to be recharged and ready to sup-
ply phones with demanded energy, discharge behavior and
rates must be realized and analyzed in order to understand
how each component consumes power and how it wastes
power [37].
Each device has several power states and in each state
different amounts of power is consumed. The main two states
a smartphone experiences are idle state and productive state.
In the idle state, a specific device requires minimal possible
power. In the productive state many modes a smartphone
experiences according to the workloads the device handles.
Permanently after each workload and before getting back to
the idle state, a device passes through a period during which
it keeps consuming power in high rates [37]–[39].
We measured power states for GPS on LG Nexus 4 smart-
phone depending on the power model presented by Ning
Ding in [40]. Figure 1 shows power states a user equip-
ment (UE) experiences for GPS: (1) Inactive GPS power
state: where GPS antenna is disabled and a device is not
sensing using GPS for a specific location. In this state,
GPS consumes no rower. After pressing the GPS button
once to start sensing and according to our power model, the
device moves to the next state which is (2) Fixing power
state: the state in which GPS is activated and its antenna is
enabled consuming specific amount of energy considering
TTFF–the time required for finding InView satellites and
deciding which are the InUse satellites and then starting to
calculate the location coordinates- according to our Energy-
TTFF relationship that we will describe latter in this paper.
In this state, power consumption increases in a high rate and
it takes like 20 seconds between each TTFF and another.
(3) Working (sensing) power state: this state comes right after
satellites acquisition where power consumption is measured
according to our Energy-SNR relationship that is described
latter.
FIGURE 1. GPS power state machine for LG Nexus phone.
V. TRACE ANALYSIS
In this section, we present our experiments in details. We will
talk about the two kinds of applications we used and the traces
we have done to reach our goals.
A. TRACE COLLECTION/ENERGY IMPACT OF WEAK
AND STRONG GPS SATELLITES SIGNAL STRENGTH
DEPENDING ON BATTERY CHARGE LEVEL
We depend here only on battery charge level that we read from
the smartphone in order to realize the changes that occur on
the battery charge level while locating the device using GPS
satellites under weak and good signal strengths.
We used a location based application LBA that is available
on Google play store. This application uses the user current
location to find and track people nearby. We ran this LBA
on fully charged LG Nexus 4 smartphone and observed its
battery for one hour and we recorded the battery every six
minutes. We repeat this experiment inside where GPS satel-
lites signal strength is weaker (less than 25) and outside where
it is stronger (around 42). Figure 2 presents the results of
this experiment. This figure simply shows that when running
the same location based application for one hour inside with
weaker GPS satellites signal strength (less than 25) and out-
side with stronger GPS satellites signal strength (around 42),
battery consumption rates is differing according to the device
location (inside or outside). When running the application
inside, battery level decreases from 100% to 79%, in other
words, battery charge decreases by 21%, and when running
the location based application outside, battery level decreases
from 100% to 87%, in other words, battery charge decreases
by 13%. Consequently, running this LBA under good satellite
SNR reduces power consumed by like 38% compared against
power consumed under bad satellite SNR.
In addition to using Location Based Application from
Google play store, we build our android application and
named it GPS SNR. Our application uses GPS hardware as
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FIGURE 2. Energy impact of weak and strong GPS satellites signal
strength depending on reading battery charge level.
a location provider and it monitors GPS satellites and request
coordinates update every 20 seconds and Figure 3 shows the
interface of GPS SNR.
FIGURE 3. GPS SNR interface.
As shown in Figure 3, our application reports any loca-
tion latitude and longitude every 20 seconds and shows the
number of InView and InUse satellites for a specific point.
InView satellites are all satellites that cover device’s location
and InUse satellites are satellites that are used for location
determination.
We ran GPS SNR for 30 minutes and recorded the decreas-
ing of battery charge every 5 minutes. Just like the previous
experiment, we ran this application indoors and outdoors.
In the next two tables (1 and 2), we present the results.
TABLE 1. GPS SNR results indoors.
From Table 1, we can see that satellite’s SNR mainly was
at the first and second range and rarely reach the third range,
which can be considered as a bad SNR. Under these bad SNR
under these bad SNR and after 30 minutes the consumed
power by this application is 7% of the battery.
From Table 2, we see that satellite’s SNR become stronger
and enters a new range (31-40), which can be considered as
good SNR. The overall battery consumption when running
the application for 30 minutes under these circumstances
was 4%.
B. TRACE COLLECTION/ENERGY IMPACT OF
WEAK AND STRONG GPS SATELLITES
SIGNAL STRENGTH USING MONSOON
The process of finding mobile location coordination – as per
any application request – consists of the following steps:
1- Finding the InView satellites.
2- Determine which of the InView satellites can be used
to find the current location (InUse satellites).
3- Start calculating the location coordination.
These steps take variable time that is called Time To First
Fix TTFF.
As in the previous section, we noticed that there is a
relationship between the amount of the consumed power and
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TABLE 2. GPS SNR results outdoors.
the signal strength of satellite used in location determination.
However, the question is how the SNR affects the process of
location finding and hence the consumed power?
To answer the previous question we have used a power
monitor tool called Monsoon, this tool monitor the power
consumed from the mobile device battery. Therefore, we
connect the monsoon to Samsung S4 mobile and monitor the
power consumption while running our GPS SNR application
indoor and outdoor.
Following are 2 screen shuts that are taken while running
Monsoon to monitor power consumed from battery while
running GPS SNR application.
Indoor (inside 2 floors building with 25 cm thickness
walls): figure 4 shows that there are three fixing periods
that are directly proportional to SNR. In the first fix period,
fixing time (TTFF) was 12619 milliseconds, and there were
3 satellites that have SNR in the range of 21-30 and 3 satellites
that have SNR in the range of 11-20 and both are considered
weak.
In the second fix period, fixing time (TTFF) became longer
(63620 milliseconds) because SNR got weaker as just two
satellites have SNR in the 21-30 range. In the third fix period,
fixing time (TTFF) is the shortest (9605 milliseconds) as
there are 4 satellites of SNR in the range 21-30.
Outdoor: figure 5 shows that there are three fix periods
having almost similar and short fixing times (TTFF) (around
2600 milliseconds) as when using GPS outdoor, satellites
SNR becomes stronger and can reach 40 or more.
From both figures 4 and 5, we can see that during fix-
ing time (TTFF) the device consumes more power, so that,
longer fixing time (TTFF) means more power consumption.
Thus the answer to the question of what is the relation
between satellite SNR, fixing time (TTFF), and the power
consumption is; weaker satellite SNR leads to longer fixing
time (TTFF) and hence more consumed power, and vice
versa.
VI. MATHEMATICAL MODEL
To make our experiment’s findings clearer we represent it
using mathematical equations. This process done in three
phases: A) SNR and TTFF relationship. B) TTFF and
Consumed Energy relationship. Depending on the previous
two phases, we found the third relationship C) TTFF and
Consumed Energy relationship.
A. SNR AND TTFF RELATIONSHIP
We ran GPS SNR application in different places and recorded
satellites SNRs and fix-time needed to find the location.
This time we concentrate on how to describe and formulate
the relationship between the SNR and TTFF. However, we
could formulate the relationship between TTFF and lowest
signal strength among all the InUse satellites signal strengths,
and we could formulate the relationship between TTFF and
highest signal strength among all the InUse satellites signal
strengths.
Following are two figures for the both previously men-
tioned relationships. In figure 6 we formulate the relationship
between TTFF and lowest signal strength among all the InUse
Satellites signal strengths using liner regression modeling,
and we got the following equation:
SNR = −679.8∗TTFF +21831
Where SNR is minimum GPS satellite signal strength and
TTFF is Time To First Fix. In figure 7 we formulate the rela-
tionship between TTFF and highest signal strength among
all the InUse Satellites signal strengths using liner regression
modeling, and we got the following equation:
SNR = −561.01 ∗TTFF +22756
Where SNR is maximum GPS satellite signal strength
and TTFF is Time To First Fix. Both following figures
(figure 6 and figure 7) support our assumption of higher GPS
satellites SNR needs less time and lower GPS satellites SNR
needs more time as actual and linear regression curves in both
figures decreasing. In other words, as sensing for location is
carried out by high signal strength of GPS satellites, TTFF
will be as short as possible and thus power consumption will
be minimized.
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FIGURE 4. Monsoon screen shut of power consumption rates while running GPS SNR application indoor.
FIGURE 5. Monsoon screen shut of power consumption rates while running GPS SNR application outdoor.
B. TTFF AND CONSUMED ENERGY RELATIONSHIP
We also ran GPS SNR application while connecting the
mobile device to the monsoon, but this time we recorded
the value of TTFF and the consumed energy during this
time. Then we formulate the relationship between the both
variables using liner regression modeling, and we got the
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FIGURE 6. Minimum SNR-TTFF relationship.
FIGURE 7. Maximum SNR-TTFF relationship.
following equation:
E=0.0797 ∗TTFF
Where Eis the consumed energy during TTFF and TTFF
is Time To First Fix. It is clear from Figure 8 that there is
a strong linear relationship between the fixing time - time
needed to find InView satellites and decide which satellites
to use (InUse satellites) and finally calculate the location
coordinates- and energy consumed from battery. Long TTFF
implies weak GPS satellites signal strength and according
to the next figure (figure 8) energy consumptions increases
as TTFF increases which means that energy consump-
tion increases as signal strength gets weaker (long TTFF).
Conversely short TTFF implies strong GPS satellites signal
strength and according to the next figure (figure 8) energy
consumption decreases as TTFF decreases which means that
energy consumption decreases as signal strength gets stronger
(short TTFF).
C. SNR AND CONSUMED ENERGY RELATIONSHIP
In the previous two subsections, we formulate the relation-
ship between maximum and minimum SNR and TTFF and
between TTFF and energy. From these relationships, we can
find the relationship between maximum and/or minimum
SNR and energy.
By using the relationships Energy-Minimum SNR and
Energy-TTFF, we found the following formula:
SNR = −679.8+21831
E=0.0797 ∗TTFF
E=0.0797((SNR −21831)/−679.8)
Where Eis energy, SNR is minimum GPS satellite signal
strength, and TTFF is Time To First Fix. Figure 9 validates
our assumption of GPS satellites signal strength impact on
energy consumption rates and it is clear that consumed energy
decreases when SNR increases.
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FIGURE 8. Energy-TTFF relationship.
FIGURE 9. Minimum SNR-energy relationship.
VII. CONCLUSION AND FUTURE WORK
Smartphones are increasingly playing an essential role in
our move towards smart cities era. The battery lifetime of
handheld devices is one of the most important parameters
in achieving good user experience for the handheld devices.
Therefore, the management and optimization of handheld
device applications in relation to their power consumption is
an important area of research.
In this paper, motivated by the fact that location-based
applications are among the top power-hungry applications,
we have performed a measurement study of GPS satel-
lites signal strength. Our analysis has shown that users
encounter large variations in the strength of the GPS signal
while using various applications requiring access to their
locations. We also performed experiments to quantify the
energy consumption of locating specific points using GPS
under poor and good signal strengths. Our experiments on
running two LBAs outdoors and indoors and observing
battery consumption rates show that only 13% of the mobile
battery is drained under good signal strength and about 38%
of the mobile battery is drained under weak signal strength.
We designed a new android application, GPS listener that
gives a detailed account of localization processes for specific
locations. Using this application, and the Monsoon appli-
cation, we observed power consumption rates and how it
relates to TTFF lengths under various signal strengths of
InUse satellites. Subsequently, we developed a mathematical
model to investigate the relationship between the energy con-
sumption of a localization application and the strength of the
GPS signal. The results demonstrated that higher SNR values
of GPS signals do consume less energy while low GPS signals
causing faster battery drain.
To the best of our knowledge, this is the first study
that provides a quantitative understanding of how the poor
strength (SNR) of satellite signals will cause relatively higher
power drain from a smartphone’s battery. This work is an
866 VOLUME 4, 2016

L. A. Tawalbeh et al.: Greener and Smarter Phones for Future Cities
important step towards understanding the power usage of
location based applications. Future work will look into further
evaluation of the proposed model and explore strategies to
reduce power consumption of location based applications.
ACKNOWLEDGMENT
We thank the Science and Technology Unit at Umm A-Qura
University for their continued logistics support.
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LO’AI A. TAWALBEH (SM’–) received the
B.Sc. degree in electrical and computer engi-
neering from the Jordan University of Science
and Technology (JUST), Jordan, in 2000, and
the M.Sc. and Ph.D. degrees in computer engi-
neering from Oregon State University, USA,
in 2002 and 2004, respectively, under the supervi-
sion of Prof. C. K. Koc, with GPA 4.0/4.0. From
2005 to 2012, he was a part-time Professor to
teach different information security courses in the
Master program with the New York Institute of Technology, DePaul’s
University, and the Princes Sumaya University for Technology. He is cur-
rently a Visiting Associate Professor with the Department of Computer
Engineering, Umm Al-Qura University, Mecca, Saudi Arabia. He is also
a Tenure Associate Professor with the Computer Engineering Department,
JUST. He is the Director of the Cryptographic Hardware and Information
Security Laboratory, JUST. He has many research publications in many ref-
ereed international journals and conferences. His research interests include
information security, cryptographic applications and computer forensics,
cloud security, and mobile cloud computing. He won many research grants
and awards. He is chairing many international conferences and workshops
in mobile cloud security and management, SDS, and power optimization.
He is a Reviewer and member of the Editorial Boards of many international
journals.
ANAS BASALAMAH received the M.Sc. degree
in computer systems and network engineering and
the Ph.D. degree from the SatoLab, Waseda Uni-
versity, Japan. He focused on multicast reliability
in wireless networking with Waseda University.
He is an Assistant Professor with the Computer
Engineering Department, Umm Al-Qura Univer-
sity. He is currently leading the Data Collection
Research Thrust of Geo Informatics Innovation
Center with Umm Al-Qura University. His areas of
interest include embedded networked sensing technologies, scheduling and
MAC layer design for wireless sensor networks, real-world applications of
networked sensing, participatory, and urban sensing. He won many research
grants and awards, including the Fellowship for Visiting Research funded
by 21 Universities to spend a research summer with The University of British
Columbia and the Fulbright Visiting Research Fellowship.
RASHID MEHMOOD (SM’–) is the Research
Professor of Big Data Systems and the Director for
Research, Training and Consultancy with the High
Performance Computing Centre, King Abdul Aziz
University, Saudi Arabia. He has gained qualifica-
tions and academic work experience from univer-
sities in the U.K., including Oxford, Birmingham,
Swansea and Cambridge. He has over 20 years
of research experience in computational modeling
and simulation systems coupled with his expertise
in high performance computing and distributed systems. His broad research
aim is to develop multidisciplinary science and technology to enable better
quality of life and smart economy with a focus on real-time intelligence and
dynamic system management. He has authored over 100 research papers,
including four edited books. He is a Founding Member of the Future Cities
and Community Resilience Network. He is a member of ACM and OSA,
and was the Vice Chairman of IET Wales SW Network. He has orga-
nized and chaired international conferences and workshops, including
EuropeComm 2009 and Nets4Cars 2010 to 2013. He has led and con-
tributed to academia-industry collaborative projects funded by EPSRC, EU,
U.K. regional funds, and Technology Strategy Board U.K. with value over
£50 million.
HALA TAWALBEH received the B.Sc. degree
in computer information systems and the M.Sc.
degree in computer science from the Jordan Uni-
versity of Science and Technology(JUST), Jordan,
in 2010 and 2012, respectively. She was a Lecturer
with the Faculty of Computer and Information
Technology, JUST. She is a Research Member with
the Cryptographic Hardware and Information and
Information Security Laboratory, JUST. She was
part of the Security Laboratory with the Muenster
University of Applied Sciences, Germany. Her research interests include
information security, mobile cloud computing, trusted computing, and man-
agement of business process.
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