Mobile Device Report
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Transcript of Mobile Device Report
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Mobile device report June 2015
Ran
k by signaling ac
tivity
Rank by increasing data usage
10
9
8
7
6
5
4
3
2
1
00 1 2 3 4 5 6 7 8 9 10
Symbian
Mobile Wi-Fi
TabletM2M
BlackBerry
Dongle/datacard
iPhone
AndroidSamsung
Androidother
AndroidHTC
Feature Phone
AndroidLG
Windows Phone
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ContentsAbout this report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
Network impact of mobile devices . . . . . . . . . . . . . . . . . . . . . . . . .6Network impact rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
Digging deeper: Inside the Android network impact . . . . . . . . . . . . . . . . . . . . . . . . . .9
Digging deeper: How network impact varies across individual networks . . . . . . 10
A closer look at devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Radio inefficiency scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Individual device network cost rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Digging deeper: Inside the Android cost bubble . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Digging deeper: Regional variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
The LTE factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18LTE versus 3G: Network impact scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
LTE versus 3G: Device costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Signaling analysis: How Androids and iPhones are different . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Top signaling applications of Androids and iPhones . . . . . . . . . . . . . . . . . . . . . . . . 25
Application signaling cost analysis for Androids and iPhones . . . . . . . . . . . . . . . . 27
Digging deeper: Googles power to impact network signaling . . . . . . . . . . . . . . . . 28
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2Motive mobile device report | June 2015
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3Motive mobile device report | June 2015
About this reportMobile data growth continues at an incredible rate as
mobile devices have evolved. No longer just tools for
personal communication, they have become high-
performing, multimedia platforms that enable consumers
to stream high definition (HD) video, surf the web with
high performance, engage in social media, participate in
online gaming and do banking securely, to name just a few
capabilities. The total number of active wireless connected
devices is expected to exceed 40.9 billion in 2020, up
from 13 billion in 2013.1 With this projected growth
in mind, this report examines how each mobile
device category impacts the network it connects to.
Table 1 provides a glossary of the device categories
considered in this report.
The findings in this report are derived from mobile-
network and device analytics provided by the Motive
Wireless Network Guardian (WNG) from Alcatel-
Lucent. Motive WNG gives us a unique vantage point
for measuring how mobile data traffic is used in live,
commercial mobile data networks, because it sees all
traffic used by cellular mobile devices, irrespective of
application, device capability or corresponding traffic
endpoint. This comprehensive view contrasts with
similar industry device reports that are based on surveys,
sales reports or traffic measurements at selected web
server sites.
All analytics from this report were taken in March 2015
and are based on data from live 3G and LTE networks.
The 3G analytics are drawn from more than 30 million
subscribers, who generate over 1 petabyte of mobile
data daily on 3G networks around the world. All results
are aggregated and anonymized, and they are not
representative of any specific network. Instead, they
represent a composite, aggregated view of a single
global network, which will be referred to as the global
composite 3G network. This network will be the prime
basis of study in this report.
Ta b le 1 . M o bi le d evice c a t e g o r ie s
Device category Description
Android OS smartphone (Android)
Google Android-based smartphones across all Android OS versions and manufacturers
iOS smartphone (iPhone)
Apple iOS-based smartphones across all iOS versions
Tablet Tablet-sized (>6.9 in.), cellular-capable mobile devices across all OS vendors and manufacturers
Mobile Wi-Fi Cellular-capable wireless routers that act as a Wi-Fi hotspot for Wi-Fi aggregation
Dongle/ Datacard
All dongles and datacards that attach to a computer, TV or other electronic device to offer cellular access
Feature phone
A general class of phones with limited capabilities, when compared to modern smartphones. Feature phones typically provide voice calling and text messaging functionality, as well as basic multimedia and Internet capabilities
BlackBerry OS smartphone (BlackBerry)
All BlackBerry phones running BlackBerry OS
Windows Phone OS smartphone (Windows Phone)
Windows Phone-based smartphones across all Windows Phone-based OS versions and manufacturers
Machine-to-machine (M2M)
M2M-based mobile devices that are not associated with a specific consumer and geared toward commercial use
Symbian OS smartphone (Symbian)
Symbian-based smartphones across all Symbian-based OS versions and manufacturers
Other An aggregate of devices that are not called out specifically in certain charts. This category includes Symbian, Windows Phone, laptops and PCs with embedded SIMs, and other less statistically significant smartphones
1 Source: https://www .abiresearch .com/press/the-internet-of-things-will-drive-wireless-connect/
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SummaryThe Alcatel-Lucent Mobile Device Report examines the
impact of mobile devices on service provider networks, in
terms of data usage and signaling activity . Together, these
two aspects provide the key to understanding the devices
overall behavior and impact on the network, as well as the
devices individual network cost .
Data usage represents the actual amount of data packets
delivered downstream and upstream to and from
the mobile device as identified by Motive WNG . This
consumption drives the service providers bandwidth-
related capital expenditures and the consumers data
usage fees .
Signaling activity measures the network-to-device
bidirectional exchanges that occur to set up a radio
connection to a mobile device for data use . Signaling
uses spectral, hardware and processing resources in
service providers networks, and it is a significant
cause of battery depletion on the mobile device .
This report provides an aggregated view of each device
categorys overall network impact, in terms of data usage,
signaling activity and subscriber share (that is, device
popularity) . Then it looks more closely at each devices
individual data usage and signaling activity, which is
also defined as the devices network cost . The influence
of LTE on mobile devices is then examined, and the
report concludes with analysis of the top smartphones
signaling activity .
The findings benefit three distinct, yet interconnected
stakeholders: mobile service providers, mobile device
owners and mobile device manufacturers .
4Motive mobile device report | June 2015
Mobile service providers gain a better understanding of the
impact that each mobile device category has on their network
and how they consume data delivery and signaling resources
from their network infrastructure . For example, they get answers
to the following questions: Which devices consume the most
signaling resources? Which use the largest amounts of data?
What is the most signaling-efficient device in the market? How
does LTE impact the behavior of devices in their network? The
answers and insights can help service providers find ways to
maximize network efficiency, minimize network cost and increase
subscriber satisfaction . In other words, they can optimize their
networks to accommodate crucial device characteristics .
Mobile device owners can see how their specific devices
behave in the network . And this awareness may encourage
changes in their own behavior for example, to minimize
signaling to preserve their battery life . Or they may become
more conscious of the bandwidth they use to lower their data
costs . Furthermore, this new understanding may influence
device selection, because certain device characteristics
may be better suited to specific uses .
Mobile device manufacturers will learn the impact that their
devices have on the network, and they can compare their
efficiency to other devices in the study . Although device
behavior is due to many things, including consumer behavior
and application use, inherent device design is also a factor .
New insights may help these manufacturers optimize their
designs, increasing efficiency in the network and make them
more attractive to service providers for promotion . More
efficient designs will also be more attractive to users, because
their usage costs will be reduced, and their devices battery
life may be extended .
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5Motive mobile device report | June 2015
Key findings Most popular devices Androids and iPhones dominate the
global composite 3G network with a combined subscriber
share of 86 .2 percent of the total device population .
Androids are the most popular with almost a 50 percent
share of all devices, and iPhones are second with
36 .8 percent . Other mobile device categories are not even
close, with the next highest being M2M at 3 .3 percent .
Devices with highest network impact Android and
iPhone device categories dominate the global composite
3G network with a combined data-usage share of more
than 80 percent of total daily usage . They also represent
an almost 90 percent share of the networks total daily
average signaling activity . In large part, their dominance is
due to the massive popularity of these devices . Androids
have a larger network impact than iPhones . Their share of
signaling is more than 30 percent higher, and their share
of data usage is nearly 15 percent higher .
Variance across networks When considering each
customer network independently, data usage and
signaling activity vary significantly for Android and
iPhone device categories . These variations are primarily
driven by the differences in their popularity among
provider networks . For Androids, the subscriber share
ranges from 30 percent to over 70 percent . For iPhones,
the range is from 9 percent to over 50 percent .
Devices network costs Each devices network cost is
measured with the daily average user traffic . Specifically,
it is measured as the daily average data usage and the
daily average signaling activity . By comparing network
costs across Androids and iPhones, we found that Androids
use 56 percent more signaling than iPhones . However,
Androids and iPhones use about the same amount of data .
In the other device categories, the dongle and datacard
and mobile Wi-Fi categories have the highest data usage
and signaling activity by far . Specifically, the amount of
signaling used by the dongle and datacard category is
well over two times the amount used by Android and
three times the amount used by iPhone .
Radio inefficiency scores Radio inefficiency scores can
be calculated for each device category as a ratio of the
average daily signaling activity to the average daily data
usage . It measures how much signaling is used per unit
of data or how chatty a device is . It was found that
the M2M category is the most radio-inefficient device
category, eclipsing all other categories in this measure .
The iPhone is a more radio-efficient device than the
Android, using more than 50 percent less signaling for
the same amount of data .
Overall device cost rankings Mobile Wi-Fi and the dongle
and datacard categories have the highest cost ranking,
followed by Androids, tablets, and Windows Phones . The
iPhone category is in the bottom half of the cost ranking,
placing sixth . M2M and feature phones are ranked the lowest .
Within the Android category, the HTC Android is more costly,
in terms of data usage and signaling activity, than Samsung
and LG devices, with LG being the lightest of all . iPhones cost
the network less than any of the top three Android brands .
Regional variations The study revealed significant
trends across major regions of the world . Androids are the
most popular device in all regions of this study . In North
America, Android is still most popular, but iPhone is almost
as popular . African users use the most data across all
categories, with the exception of the dongle and datacard .
The dongle and datacard and mobile Wi-Fi categories rank
highest in data usage, with the biggest users in the Middle
East . Average daily data usage of their dongle and datacard
users is almost 550 MB . In North America, the dongle and
datacard category shows, by far, the most signaling activity
of any device category and region .
Impact of LTE networks on top devices When comparing
device behavior in LTE networks to our findings for 3G
networks, Androids have a 4 percent lower share of data
usage, but they gain a 5 percent in share of signaling .
iPhones gain a significant 11 percent share of data usage
and a 3 percent share of signaling . Androids gain a 1 percent
share of subscribers, and iPhones gain a 4 percent share of
subscribers . In LTE networks, iPhones have a higher share of
data usage than Androids, and they are tied with Androids
as the category with the highest overall network impact .
Impact of LTE networks on other devices In LTE
networks, the impact of the dongle and datacard category
is significantly lower . Its share of data usage falls below
1 percent, and its share of signaling drops below 0 .5 percent .
This can be explained by a significant drop in its share
of subscribers . The tablet category shows an increase in
popularity in LTE networks, and its subscriber share almost
doubles . Despite this popularity, it shows a decrease in its
share of data usage, while its share of signaling activity
remains about the same . The BlackBerry and M2M categories
are less popular in LTE, with M2M almost disappearing .
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6Motive mobile device report | June 2015
How LTE changes device costs There is a massive
increase in data usage for devices on LTE networks,
compared with devices on 3G networks . On LTE,
a devices average daily data usage is almost four
times greater than its 3G counterpart . Signaling activity
also increases, but not as much as data usage . For
Androids and iPhones, data usage increases 3 .5 times
and 4 .5 times, respectively, while signaling activity
increases by 2 .3 times and 2 .1 times .
Top signaling applications For Android-based
smartphones, Facebook Messenger has the highest
share at 17 percent, followed by Google Cloud
Messaging (GCM) at 13 percent, Google at 12 percent,
HTTPS at 11 percent, Facebook at 10 percent . For
iPhones, Apple Push Notification Service (APNS) has
the highest share at 38 percent, followed by HTTPS at
12 percent, Facebook Messenger at 9 percent, Apple
at 6 percent, and Facebook at 6 percent .
Application signaling costs Applications running on
Androids exhibit a larger signaling cost than the same
applications running on iPhones . Our data suggests that
this is partially due to the effective and broad use of
the APNS for most iPhone applications .
2 Source: https://econsultancy .com/blog/64376-65-of-global-smartphone-owners-use-android-os-stats/3 Source: http://marketshare .hitslink .com/operating-system-market-share .aspx?qprid=8&qpcustomd=1&qpsp=2015&qpnp=1&qptimeframe=Y
Network impact of mobile devicesThis section examines the overall impact of each major
device category on the network . Data usage is measured
by percent share of total average daily data usage, and
signaling activity is measured by percent share of the
total average daily connection requests . The popularity
of each device category is also discussed . Figure 1
shows these three factors across all device categories .
The device popularity or subscriber share bar in Figure 1 shows
the dominance of Androids and iPhones within the global
composite 3G network . Combined, these devices make up over
86 percent of the total distribution of devices . This finding is
consistent with those of other industry reports .2,3 When looking
at data usage, Androids represent an almost 50 percent share
of total network data usage . Combined with iPhones, they
account for over 80 percent share of total network data usage .
Androids and iPhones also dominate when looking at
signaling activity, with Androids representing an incredible
59 .7 percent of signaling . Combined with iPhones, they
account for almost 90 percent of total signaling activity .
These extremely high percentages of total data usage and
signaling activity no doubt correlate with the popularity
of these devices .
F ig u r e 1 . N e t wo r k im p a c t o f d evice s in th e g lo b a l co m p o si t e 3 G n e t wo r k
49.4
36.8
3.3 2.2 2.0 1.8 1.7 0.52.3
47.9
34
0.2 0.3
9.4
2.10.4
4.11.5
59.7
28
1.4 0.7
4.51.5 1.5 1 1.5
Device
Android iPhone M2M Featurephone
Dongle/Datacard
Tablet MobileWi-Fi
OtherBlackBerry
Device popularity Percent share of data usage Percent share of signaling activity
Percent
60
30
40
50
20
10
0
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7Motive mobile device report | June 2015
Androids have a larger impact on the global composite network
than iPhones . Their share of signaling activity is 59 .7 percent,
compared to 28 percent for iPhones, and their share of data usage
is 47 .9 percent, with 34 percent for iPhones . These differences
represent a 31 .7 percent higher share of signaling activity and
a 13 .9 percent higher share of data usage for the Android category
over the iPhone category .
Figure 1 also shows that, despite only a 2 percent subscriber share,
the dongle and datacard category has a 9 .4 percent share of data
usage . This may be because these devices are typically attached
to PCs or laptops, which have larger screens and are less mobile
than smartphones . As a result, these devices tend to consume
proportionally larger amounts of data than other categories by
streaming video, playing online video games, downloading and
uploading high-resolution pictures, and so forth .
The mobile Wi-Fi category shows a similar trend . With only
0 .5 percent of subscriber share, this category still manages to
consume a 4 .1 percent share of data usage the largest ratio of data
usage to subscriber share across all device categories . To understand
this trend, keep in mind that each mobile Wi-Fi device can aggregate
many mobile Wi-Fi devices behind it . Thus, it collectively consumes a
large amount of data for a relatively small subscriber share .
The M2M category represents non-personal mobile devices that are
used commercially for monitoring and control purposes . For example,
theyre often deployed in industrial automation, healthcare imaging,
banking and finance, smart homes, logistics, security and more . In
Figure 1, this category has a small subscriber share, only 3 .3 percent,
which tells us that M2M may not yet have penetrated service
provider networks in a really significant way .
The data also reveals that M2M devices signaling activity is
relatively much greater than their data usage . In the global composite
3G network, they consume 0 .2 percent share of data usage and
1 .4 percent share of signaling activity . In other words, these devices
are signaling a lot more than they are using data . This makes sense,
because many M2M applications establish mobile connections
frequently, then send very little data . For example, home smart
meters send automated updates several times a day, generating
multiple signaling messages to establish network connectivity,
with very little data to send each time .
Network impact rankingsTo provide another perspective on each
device categorys impact on the global
composite network, we have established
an overall network impact score between
1 and 10 for each device category . This score
is calculated by first computing a network
impact score between 1 and 10 for both data
usage and for signaling activity . The overall
network impact score is then an average of
both of those individual scores .
Device categories are then ranked . The
device category with the highest score is
ranked Number 1, which means it has the
highest network impact . Table 2 shows these
rankings, and as expected, Androids and
iPhones are at the top .
To offer a deeper, more visual understanding
of the network impact rankings, Figure 2
plots the data usage score and the signaling
activity score for each device . The size of
the bubble on the chart reflects the device
popularity of that category .
Ta b le 2 . N e t wo r k im p a c t ra n k in g s
Rank Device category Overall score
1 Android 10
2 iPhone 9
3 Dongle/Datacard 8
4 BlackBerry 6
5 Tablet 5.5
6 Mobile Wi-Fi 5.5
7 M2M 4.5
8 Windows Phone 3
9 Feature phone 2.5
10 Symbian 1
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8Motive mobile device report | June 2015
F ig u r e 2 . N e t wo r k im p a c t s co r e s p lo t t e d f o r th e g lo b a l co m p o si t e 3 G n e t wo r k
Rank by increasing signaling activ
ity
Rank by increasing data usage
0 1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
0
SymbianWindows Phone
Mobile Wi-Fi
Tablet
M2M
BlackBerry
Dongle/Datacard
iPhone
Android
Feature phone
More popular Less popular
Figure 2 offers service providers and device manufacturers a quick snapshot of the
impact that various devices have on the network, while also showing which devices are
most popular . With this plotting, it is obvious that Android and iPhone are the dominant
categories . But it also makes clear that dongles and datacards have a relatively significant
impact on the network, even though theyre not as popular .
The mobile Wi-Fi, tablet and BlackBerry categories come next in terms of network impact,
although their bubbles in Figure 2 are quite small, indicating a small subscriber share value .
This overall network impact study is a good starting point for understanding the impact
of various devices . Later, this report establishes individual network costs for each device
category, independent of the influence of popularity .
With this plotting it is obvious that Android and iPhone are the dominant categories .
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9Motive mobile device report | June 2015
Digging deeper: Inside the Android network impactWithin the Android category, several device manufacturers implement the Android OS .
This section of the report examines the major manufacturers and provides their individual
network impact scores . Specifically, they include Samsung, HTC, and LG, along with a category
called other that includes approximately 60 more Android-based device manufacturers .
Table 3 adds these new categories to the network impact scores and ranking analysis .
In this new ranking, the Android Samsung category is the most popular manufacturer
of Android, with almost 30 percent share of all 3G mobile devices . This is not surprising
as Samsung is a marketing juggernaut, dominating social video marketing, and was
ranked as one of the top two shared brands in 20134 and 2014 .5
In terms of data usage, the iPhone category has the greatest impact which contributes
to its being tied with Android Samsung as the device with the greatest network impact
overall . However, Android Samsung remains most impactful with respect to signaling
activity, despite being 8 .3 percent less popular than the iPhone .
Android HTC and LG are the next most popular Android device manufacturers, with
subscriber shares of 4 .6 percent and 5 .5 percent, respectively . HTC and LG rank fourth
and sixth in their network impact, respectively, and Android other ranks third in
network impact, with a 10 .3 percent subscriber share .
Figure 3 provides a scatter diagram showing the Android bubble of Figure 2 broken into
its representative manufacturers . It makes clear that the Android Samsung category is the
most dominant Android category . It is also tied for greatest overall network impact with
the iPhone category and has the greatest signaling impact . The other Android categories
all remain in the upper right quadrant of the graph, representing their high impact in both
data usage and signaling activity .
F ig u r e 3 . N e t wo r k im p a c t s co r e s p lo t t e d , in c lu din g m o r e s p e c i f i c A n dr o id c a t e g o r ie s
Rank by signaling activ
ity
Rank by increasing data usage
0 1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
0Symbian
Mobile Wi-Fi
TabletM2M
BlackBerry
Dongle/datacard
iPhone
AndroidSamsung
Androidother
AndroidHTC
Feature Phone
AndroidLG
Windows Phone
More popular Less popular
Ta b le 3 . N e t wo r k im p a c t ra n k in g s , in c lu din g A n dr o id m a n u fa c t u r e r s
Rank Device category
Overall score (1-10)
1 iPhone 9.62
2 Android Samsung
9.62
3 Android other
8.46
4 Android HTC 7.31
5 Dongle/Datacard
9.62
6 Android LG 6.54
7 BlackBerry 4.62
8 Tablet 4.23
9 Mobile Wi-Fi 4.23
10 M2M 3.46
11 Windows Phone
2.31
12 Feature phone
1.92
13 Symbian 0.77
4 Source: https://econsultancy .com/blog/64064-how-samsung-owns-social-video-with-youtube-and-vine/
5 Source: http://www .thedrum .com/news/2014/12/03/activia-samsung-and-nike-most-shared-social-video-brands-2014
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10Motive mobile device report | June 2015
F ig u r e 4 . R a n g e o f da t a u s a g e a c r o s s a l l s e r v ice p r ovid e r n e t wo r k s
Percent sh
are
of data
usa
ge
70
60
50
40
30
20
10
0
-10Android iPhone Mobile
Wi-FiDongle/Datacard
Tablet BlackBerry M2M Featurephone
Other
0-50th percentile 50th - 100th percentile MedianMean
66.74
50.81
7.78
33.96
27.14
2.48
18.79
4.12
41.37
0.34 0.39
9.43
9.43
13.20
1.51
2.09
0.18
0.43
0.10 0.01 0.03 0.36
0.32
0.72
1.510.90
2.78
0.03
1.66
0.07
0.25
2.48
0.89
25.94
47.88
36.61
Digging deeper: How network impact varies across individual networksThe previous section analyzed aggregated data from all the networks in this study . This
approach provides a macro view of the devices and their overall behavior . However, a
devices impact within each provider network can vary significantly, because they are
influenced by a variety of factors, ranging from service providers device promotion strategy
and data plans to cultural differences that influence usage patterns and application use .
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11Motive mobile device report | June 2015
This section examines the variance across networks by
showing data usage for each device category within
each mobile network studied . In this specific analysis,
data from each network has equal weight, so that
exceptionally large networks do not dominate smaller
networks when the results are analyzed . With this
information, a range of percentage share values,
from high to low, can be established, along with the
mean and the median . (The mean is the average of
the shares of data usage across each network . The
median indicates the exact middle across all of the
share values .)
The vertical bars in Figure 4 show the varying
percentage share of data usage across each network
for each device category . These findings indicate that
the Android and iPhone device categories collectively
dominate the networks where theyre deployed, as
shown by the mean values of 47 .88 percent and
33 .96 percent, respectively . However, these data
usage figures range widely across individual networks
for both these device categories . For Android, the
percentage share range extends from 25 .94 percent
to 66 .74 percent . For iPhones, it ranges from
7 .78 percent to 50 .81 percent .
Comparing the mean with the median reveals more
about the distribution of percentage share values across
the networks . For Android, the median of 36 .61 percent
is much lower than the mean . This difference suggests
that there are more networks that have a percentage
share value below the mean than above the mean . This
shows that there is a small number of networks that
have very high share values that pull the mean value
well above the median . The network at the top end
of the range, with 66 .74 percent share, is an example
of one . For the iPhone, the median of 27 .14 percent
is closer to the mean, indicating that the percentage
shares are more evenly distributed across the range .
Figure 4 also makes clear how significant the mobile
Wi-Fi, dongle and datacard, and even tablet device
categories can be in some networks in terms of
data usage . In networks where these devices have
the largest impact, the highest percent share is
18 .79 percent for the mobile Wi-Fi device category,
along with a whopping 41 .37 percent for the dongle
and datacard device category . BlackBerrys, M2M and
feature phones make up a very small share of data
usage across all networks . (This reports section on
regional variations presents some reasons for these
observations .) The results are very similar when
considering signaling activity . In fact, the network
impact for both data usage and signaling activity
correlates strongly with device popularity across
each of the networks . For Androids-based devices,
the range of popularity varies from 32 .84 percent
to 71 .37 percent . For iPhones, the range varies from
9 .15 percent to 51 .01 percent . In general, networks
showing larger ranges of device popularity generally
had larger ranges of data usage and signaling activity
for that device . Likewise, when networks have smaller
ranges of device popularity, they generally had smaller
ranges of data usage and signaling activity for that device .
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12Motive mobile device report | June 2015
A closer look at devicesThe analysis presented in the previous section offers a great
way to understand the impact that each device category has
on the network . However, these results are heavily weighted
by the impact of device popularity . This limits the analysis
to a more general understanding of the characteristics and
impact that devices have as an aggregated group . A specific
device may initially appear quite innocuous when it is
unpopular and not widely deployed, but what happens when
it is actively promoted and its popularity skyrockets? Some
devices may appear quite costly, but they are really quite
efficient in terms of network cost, on a per-device basis .
To really understand how each device behaves in the
network, it is important to consider each device separately
and determine its individual network cost . This cost is
defined and measured across two dimensions, the average
daily data usage and the average daily signaling activity .
With this type of information, service providers can predict
how shifts in popularity and usage trends of a specific
device will impact their networks . Figure 5 reveals the
individual network costs of each device category .
MB or se
tups
Device
Android iPhone BlackBerry Dongle/Datacard
M2M Featurephone
MobileWi-Fi
Symbian Tablet WindowsPhone
Data usage cost (MB) Signaling activity cost (setups)
100
200
300
400
500
0
30.4 29.78.2
140163
111.1
2.4 3.8
80
52
10
111
144
23.3
191
32
233
355
475
219
Figure 5 shows that Androids use 56 percent more
signaling on average, on a daily basis, than iPhones
do . (In a later section, we will examine some reasons
for this difference .) Both categories consume about
the same amount of data .
The dongle and datacard and mobile Wi-Fi categories
use by far the most data and generate the most
signaling activity . In fact, the amount of per-device
signaling activity exhibited by the dongle and datacard
category is well over two times and three times the
amounts for Android and iPhone devices, respectively .
M2M, BlackBerrys and feature phones exhibit very little
data usage with respect to their signaling activity . Thats
because unlike smartphones these devices are not
used as data-intensive multimedia platforms .
F ig u r e 5 . In d iv id ua l n e t wo r k co s t s a c r o s s a l l d ev ice c a t e g o r ie s
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13Motive mobile device report | June 2015
Radio inefficiency scoresThe network costs just described are used to establish radio inefficiency scores for each device . The
amount of daily signaling activity is simply divided by the amount of daily data usage . This score
measures the amount of signaling per unit of data usage and demonstrates how chatty certain
devices are on the network . Figure 6 shows these inefficiency scores across each device category .
The M2M category immediately stands out in Figure 6, because its radio inefficiency score of 33
makes it, by far, the most inefficient or chatty . This may be explained by the nature of certain
M2M services . In some cases, these services establish connections while having relatively little
data to transmit . For example, a home monitoring appliance may send an update many times per
day to a centralized server, transmitting small bits of information on home temperature, natural
gas use and so forth .
BlackBerrys and feature phones are also relatively inefficient, with scores of 20 and 14,
respectively . These devices do not signal more than other categories . Their high scores reflect
the fact that they do not use a lot of data in an average day . That is, these devices are not used
like the more data-intensive multimedia platforms that Androids and iPhones have become .
Androids and iPhones are relatively efficient with scores of 7 and 5, respectively . These scores
also indicate that the iPhone is a more radio-efficient device, using over 50 percent less signaling
than Androids for the same amount of data usage .
The inverse of this score, a devices radio efficiency, is measured by the relative amount of
data delivered per unit of signaling . Radio inefficiency and efficiency scores are a quick way
to understand what the network impact will be relative to signaling activity when rolling out
specific mobile devices in new markets .
F ig u r e 6 . R a dio in e f f i c ie n c y s co r e s a c r o s s d evice s
Radio ineffi
ciency
(se
tups/MB)
Device
Android iPhone BlackBerry Dongle/Datacard
M2M Featurephone
MobileWi-Fi
Symbian Tablet WindowsPhone
15
10
5
20
25
30
35
0
7
5
20
4
33
14
2
11
5
8
-
14Motive mobile device report | June 2015
Individual device network cost rankingsIn this section, an individual network cost score from 1 to 10 is established for each
device . This score represents the individual cost that the device has on the global
composite 3G network, and it reflects both data usage and signaling activity by taking
the average of the individual cost scores for these dimensions . Similar to network impact
rankings, device categories are ranked from 1 to 10, and the device category with the
highest network cost score has the highest rank .
Table 4 clearly shows that the mobile Wi-Fi and the dongle and datacard categories are most
costly, followed by Androids, tablets and Windows Phones . The iPhone category is ranked sixth,
in the bottom half of network cost scores . M2M and feature phones exhibit the smallest cost .
Figure 7 takes data usage and signaling activity cost scores and plots them on a scatter
diagram . This view offers a deeper, more visual understanding of a devices network cost
rankings . It also further demonstrates the enormous cost of the dongle and datacard and
mobile Wi-Fi categories, compared with other categories . The reason for the extremely
high cost for dongles and datacards is twofold . First, these devices are naturally data
intensive, because their larger screens promote video use, and their lower propensity for
mobility also encourages data usage . Second, in the North American market these devices
are used by business road warriors who have been shown to be heavy on signaling .
(See this reports section on regional variations for more detail .) Mobile Wi-Fi will naturally
consume a large amount of data and generate a lot of signaling activity as it effectively
represents many Wi-Fi devices that are aggregated behind it .
These individual costs can help service providers determine the potential impact to the
network, when a new device is promoted and expected to increase in popularity . Of course,
device costs are determined by many things, including mobile application use, user behavior,
individual traffic patterns, and the inherent design of the device and its OS . As a result,
mobile device manufacturers do have some degree of control over the individual network
cost of their devices, and insights like these may be leveraged to influence their designs .
F ig u r e 7. In d iv id ua l d evice co s t s : D a t a u s a g e a n d s ig na l in g a c t iv i t y
Rank by increasing signaling activ
ity
Rank by increasing data usage
0 1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
0
Symbian
Windows Phone
MobileWi-Fi
Tablet
M2M
BlackBerry
Dongle/Datacard
iPhone
Android
Feature phone
Ta b le 4 . In d iv id ua l d evice n e t wo r k co s t s co r e s a n d ra n k in g s
Rank Device category
Overall score (1-10)
1 Mobile Wi-Fi 9.5
2 Dongle/Datacard
9.5
3 Android 7.5
4 Tablet 6.5
5 Windows Phone
6.0
6 iPhone 5.0
7 BlackBerry 4.5
8 Symbian 3.5
9 M2M 1.5
10 Feature phone
1.5
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15Motive mobile device report | June 2015
Digging deeper: Inside the Android cost bubbleThis section examines the network cost scores of the top device manufacturers within
the overall Android category . These subgroups are Android HTC, Android Samsung and
Android LG . Table 5 shows the individual device scores and rankings, while Figure 8
plots data usage and signaling activity cost scores .
Table 5 shows that, in terms of network cost, the mobile Wi-Fi and dongle and datacards
categories are still ranked at the top, while Android HTC remains the third most costly
category . That makes Android HTC the most costly Android-based device, with Android
LG being the least costly .
F ig u r e 8 . A n dr o id in d e t a i l : In d iv id ua l co s t s co r e s p lo t t e d
Rank by increasing signaling activ
ity
Rank by increasing data usage
0 1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
0
Symbian
Mobile Wi-Fi
Tablet
M2M
BlackBerry
Dongle/Datacard
iPhone
AndroidSamsung
AndroidHTC
Feature phone
AndroidLG
Windows Phone
Ta b le 5 . A n dr o id in d e t a i l : In d iv id ua l n e t wo r k co s t s co r e s a n d ra n k in g
Rank Device category
Overall score (1-10)
1 Mobile Wi-Fi 9.58
2 Dongle/Datacard
9.58
3 Android HTC 7.92
4 Tablet 6.25
5 Windows Phone
6.25
6 Android Samsung
6.25
7 Android LG 4.17
8 iPhone 4.17
9 BlackBerry 3.75
10 Symbian 2.92
11 Feature phone
1.25
12 M2M 1.25
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16Motive mobile device report | June 2015
Digging deeper: Regional variations
As expected, the Android and iPhone categories are
the most popular across all regions . Androids are by
far the most popular category in Africa and in AMEE
with percentage shares of 75 percent and 59 percent,
respectively . In North America, the Android is still the
most popular category but only slightly more than the
iPhone . It has a 48 percent subscriber share compared
with iPhones 42 percent . The other device categories
are not very popular, with the exception of the dongle
and datacard category in the African region .
F ig u r e 9 . D evice p o p u la r i t y a c r o s s m ajo r r e g io n s
Device
Tablet MobileWi-Fi
M2M Dongle/Datacard
BlackBerry iPhone Android
AfricaNAAMEE
Subsc
riber sh
are
(popularity
)
30
20
10
40
50
60
70
80
0
This section organizes our analysis across major regions
of the world by creating separate regional composite 3G
networks . The regions included in this study include Africa
and North America, along with a grouping that represents
the other major regions of our study including Asia, the
Middle East and Europe (AMEE) . Figure 9 shows the device
popularity across these regions .
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17Motive mobile device report | June 2015
Some of the regional variations in popularity between
iPhones and Androids can be attributed to how each
device is marketed and promoted . In North America,
smartphones are usually sold with a yearly data plan
attached to the device . In addition, the iPhone has a very
small range of phone models and cost points, and Apple
typically targets users who are willing to pay more for
a phone that has more features and capabilities and
who are also willing to spend more on applications at the
iStore . This approach, embraced by Apple and their iPhone
marketing strategy, is well received in North America, as
reflected by the iPhones popularity in this region .
In other regions of the world, the concept of pay as you go
with prepaid data is more popular, because flexibility and
cost effectiveness are paramount . Androids have embraced
this approach and offer a very large range of devices from
different manufacturers with a broad spectrum of capabilities
and cost points . This may help explain why Androids are
significantly more popular than iPhones in regions outside
of North America .
Figure 5 provides the daily averages for data usage and
signaling activity, calculated across the entire global composite
3G network . In this section, the same calculation is applied
to each major region of our study: Africa, North America and
AMEE . Figures 10 shows the results of this analysis .
F ig u r e 10 . D a i ly ave ra g e da t a u s a g e ( le f t ) a n d s ig na l in g a c t iv i t y ( r ig h t ) a c r o s s r e g io n s
Tablet MobileWi-Fi
M2M Dongle/Datacard
BlackBerry iPhone Android Tablet MobileWi-Fi
M2M Dongle/Datacard
BlackBerry iPhone Android
AfricaNAAMEE
100
200
300
400
500
600
0
-
18Motive mobile device report | June 2015
One point that immediately stands out is how much more
data users in Africa and AMEE use each day than users in
North America . This can be explained by examining some
of the cultural usage patterns within these regions . In AMEE
and, especially, within the Middle East, users consume a
very large amount of video . Delving deeper into this trend,
we found that, within Middle Eastern networks, the top
applications all involved video use, such as YouTube, Apple
QuickTime and video downloads . The dongle and datacard
category was the top device used for video, resulting in an
average daily data usage of 541 MB .
African users consume the most data across all device
categories, with the exception of dongle and datacard .
Video viewing still contributes to this consumption more
than all other forms of data . In addition, this heavy use
of mobile data supports descriptions of Africa as the
mobile continent,6 where many people first connect
to the Internet through mobile devices . Lack of fixed
The LTE factorUp to this point, the findings weve discussed have
been restricted to 3G technology, which is deployed
by service providers worldwide in almost all countries .
LTE, however, is not widespread enough to enable
comparisons across all the regions within this study .
Nevertheless, it is important and interesting to
understand how different technologies can impact
the behavior of mobile devices . So in this section, we
compare our baseline 3G analysis with an LTE network
consisting of a smaller group of LTE networks .
The study uses actual data from more than 24 million
subscribers, generating over 3 petabytes of mobile
data daily on live LTE networks across North America,
the Middle East and Asia . All results are aggregated
and anonymized and are not representative of any
specific network . Instead, they represent a composite,
aggregated view of a single global LTE network, which
we refer to as the global composite LTE network .
infrastructure, unreliable electricity, and increasingly
cheaper smartphones are likely reasons that mobile
data usage is much higher in certain parts of Africa
and preferred over wireline connections .7
Daily signaling activity is more evenly distributed across
regions and device categories than daily data usage .
However, in North America, the dongle and datacard
category shows the most signaling activity, far more than
any other device category and region . Closer examination
of the data points to the large number of road warriors
in the North American market who regularly use their
laptops on the go for business . The applications they use
are very signaling-intensive, like chatty mobile VPNs that
typically send a constant keep alive signaling heartbeat,
VoIP, and messaging applications like Google Talk, as well
as lots of web surfing that generates significant HTTPS
and HTTP traffic .
6 Source: http://www .ssireview .org/articles/entry/the_mobile_continent7 Source: http://www .theguardian .com/world/2014/jun/05/internet-use-
mobile-phones-africa-predicted-increase-20-fold
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19Motive mobile device report | June 2015
LTE versus 3G: Network impact scoresBefore any comparisons are made with our 3G results, Table 6 reveals
the network impact scores (from 1 to 8) and rankings for devices in
the global composite LTE network . As in Table 2, these scores reflect
both data usage and signaling activity . The score is calculated by first
computing a network impact score between 1 and 8 for both data
usage and for signaling activity . The overall network impact score is
then an average of both of those individual scores . As Table 6 shows,
there are no Symbian or feature phones in this network .
Figure 11 plots data usage and signaling activity for each device,
with the size of the plotting point reflecting the popularity of the
device category .
Table 6 and Figure 11 show that Androids and iPhones are tied, when
measuring which devices have the highest overall impact on the
global composite LTE network . The Android category has the highest
signaling impact, and the iPhone category has the highest data usage
impact . The impact of M2M and dongle and datacard categories is
noticeably smaller in LTE than on the 3G network .
Ta b le 6 . N e t wo r k im p a c t ra n k in g s f o r th e g lo b a l co m p o si t e LT E n e t wo r k
Rank Device category
Overall score (1-8)
Subscriber share
1 Android 7.5 52.31%
2 iPhone 7.5 42.69%
3 Tablet 5.5 3.09%
4 Mobile Wi-Fi 5.5 0.15%
5 Windows Phone
3.0 0.38%
6 Dongle/Datacard
3.0 0.15%
7 BlackBerry 3.0 0.80%
8 M2M 1.0 0.07%
F ig u r e 11 . N e t wo r k im p a c t s co r e s p lo t t e d f o r th e g lo b a l co m p o si t e LT E n e t wo r k
Rank by increasing signaling activ
ity
Rank by increasing data usage
0 1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
0
More popular Less popular
Mobile Wi-Fi
Tablet
M2M
BlackBerry
Dongle/Datacard
iPhone
Android
Windows Phone
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20Motive mobile device report | June 2015
Using this network impact baseline, our study made a direct
comparison of device network impact across 3G and LTE
networks . Table 7 shows the percentage share values for both
3G and LTE networks, providing side-by-side comparisons of
data usage, signaling activity and device popularity . Please
note that because there are no Symbians, feature phones,
and other devices studied within the global composite LTE
network, the percentage shares calculated for 3G in Table 7
are calculated across a smaller number of devices and thus
will differ slightly from those presented earlier .
As shown in Table 7, Androids lost 4 percent in its share
of data usage but gained 5 percent in its share of signaling
activity . iPhones gained a significant 11 percent in its share
of data usage and also gained 3 percent in its share of
signaling activity . Androids gained 1 percent in its share of
subscribers, and iPhones gained 4 percent in its share of
subscribers . In general, we found no dramatic changes in
percentage share values, except that iPhones increased their
network impact, driven primarily by their larger data usage .
Among other device categories, the dongle and datacard
category declined significantly in the global composite LTE
network . Its share of data usage decreased below 1 percent,
and its share of signaling dropped below 0 .5 percent . This
decrease can be explained by the sizable drop in its share
of subscribers . This drop probably results from a slower
transition to LTE, and the fact that many of these devices
are provided by users employers who have a mandate to
maximize the life of the device . The BlackBerry and M2M
categories are also less popular in LTE, with M2M almost
disappearing, as it drops from 3 .4 percent to 0 .07 percent .
The reduced popularity of M2M devices is easy to explain
as it is about economics and coverage . M2M applications
Ta b le 7. N e t wo r k im p a c t a c r o s s 3 G a n d LT E n e t wo r k s a co m p a r is o n
Device category Data usage share 3G
Data usage share LTE
Signaling activity share 3G
Signaling activity share LTE
Subscriber share 3G
Subscriber share LTE
Android 50.00% 46.12% 60.40% 64.92% 51.33% 52.31%
iPhone 37.09% 47.98% 29.27% 31.76% 38.89% 42.69%
Tablet 1.91% 0.86% 1.44% 1.46% 1.86% 3.09%
Mobile Wi-Fi 3.67% 4.04% 0.94% 0.70% 0.49% 0.52%
Windows Phone 0.29% 0.20% 0.40% 0.31% 0.39% 0.38%
Dongle/Datacard 6.31% 0.64% 4.51% 0.23% 1.77% 0.15%
BlackBerry 0.46% 0.15% 1.52% 0.62% 1.74% 0.80%
and services usually dont need a lot of bandwidth
and performance, but they certainly need coverage .
Economically, 3G networks are best suited for both
cost and coverage for these types of services .
The tablet category actually increased in popularity
in LTE networks, with subscriber share growing from
1 .8 percent to 3 .09 percent a 66 percent increase .
Despite this increase in popularity, however, the category
shows a decrease in its share of data usage, while its
share of signaling activity remains about the same .
These findings reflect that iPhones claimed a greater
share of data usage from the tablet category .
The mobile Wi-Fi and Windows Phone categories
both remain about the same from 3G to LTE .
The tablet category actually increased in popularity in LTE networks, with subscriber share growing from 1 .8 percent to 3 .09 percent a 66 percent increase .
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21Motive mobile device report | June 2015
F ig u r e 12 . N e t wo r k im p a c t co m p a r is o n p e r ce n t a g e s h a r e ra t io s o f 3 G t o LT E
Device
Android iPhone BlackBerry Dongle/Datacard
M2M Mobile Wi-Fi WindowsPhone
Tablet
LTE/3G share of data usage LTE/3G share of signaling activity LTE/3G share of subscribers
Ratio of LT
E to 3G netw
ork impact
0.8
0.6
0.4
0.2
1.0
1.2
1.4
1.6
1.8
0
Figure 12 shows the ratio of percentage shares between LTE and 3G
for each device category .
Any value above 1 represents an increase in network impact, while
any value below 1 represents a decrease in network impact . Clearly,
the dongle and datacard, M2M, and BlackBerry categories all decreased
their network impact dramatically across all dimensions . However, the
Android and iPhone categories remain relatively stable from 3G to LTE,
with respect to their network impact, except for the iPhones increased
share of data usage . The most noticeable item on this chart may be the
increase in popularity of the tablet category from 3G to LTE . But even
with this increase, its share of data usage has decreased .
-
22Motive mobile device report | June 2015
LTE versus 3G: Device costsIn January 2014, an Alcatel-Lucent blog8 projected that the growth of data usage for
devices on LTE networks would be three times that of devices operating on 3G networks .
This section of our report examines that projection .
Figure 12 already compared device impact on 3G and LTE networks, finding that, in
general, there were no major shifts in network impact across the top device categories .
However, the network impact of BlackBerrys, dongle and datacards, and M2M devices
was significantly lower on LTE . In this section, a similar comparison is made, but this
time comparing the devices network cost .
Figure 5 shows device network costs established from the global composite 3G network .
Figure 13 now compares those costs with device network costs from the global composite
LTE network . The data in Figure 13 was calculated for each device category by establishing
the ratio of its average daily data usage and its average daily signaling activity on the global
composite LTE network to the global composite 3G network . Values greater than one represent
a cost increase on the LTE network, and values smaller than one represent a decrease .
Figure 13 shows a massive increase on the global composite LTE network for both data
usage and signaling activity across almost all categories . An average Android-based device,
for example, will use 3 .5 times more data and generate 2 .3 times more signaling activity
when on an LTE network, rather than a 3G network . An average iPhone will use 4 .5 times
more data and generate 2 .1 times more signaling when on an LTE network .
F ig u r e 13 . D evice co s t s a c r o s s 3 G a n d LT E a co m p a r is o n
Device
Android iPhone BlackBerry Dongle/Datacard
M2M Mobile Wi-Fi WindowsPhone
Tablet
LTE/3G data usage cost LTE/3G signaling activity cost
Ratio of LT
E to 3G costs
4
3
2
1
5
6
7
0
3.5
2.3
4.5
2.1
2.7
1.9
4.8
1.4
5.8
0.6
4.0
1.5
1.0
1.3
2.7
1.7
8 Source: https://www .alcatel-lucent .com/blog/2014/proof-4g-speed-brings-consumers-content-and-cash
-
23Motive mobile device report | June 2015
Dongle and datacard, M2M, and mobile Wi-Fi devices will
also use 4 .8 times, 5 .8 times, and 4 .0 times more data,
respectively, on LTE networks . The only decrease on
LTE networks is for the M2M category, where there is a
40 percent reduction in signaling activity . In general, all
categories, except tablets, use more data, with increases
ranging from 2 .7 times to 5 .8 times more on LTE networks .
All categories except M2M use more signaling, with
increases ranging from 1 .3 times to 2 .3 times more .
The primary driver for this massive increase in data
usage on LTE networks is the performance capabilities
of LTE, which promote greater use of video . 3G and LTE
performance were compared in the same blog from
Analytics Beat, which found that LTE networks deliver more
than four times the speed of 3G, on average (that is, 3 .7 to
6 times faster, depending on the network) . Because of this
performance improvement, LTE networks can deliver data-
intensive experiences, such as video streaming, on mobile
devices . This same blog projected that LTE users would
consume three times more data than 3G users by the
end of 2014 . Another blog from Analytics Beat9 reported
finding that on LTE networks, video use represents the
highest share of traffic of all applications categories
and generates over a third of all daily traffic usage .
The aforementioned blog projection seems to be
validated by the analysis described in this section
of our report . Specifically, if the data from Figure 13
is aggregated across all device categories for March
2015 (the month the data was based on), and the ratio
of the average daily data usage for devices on LTE
networks is compared with that of 3G networks, the
result is 3 .74 times more data usage per user in LTE .
When this result is compared with the blogs projection
(3 times more data usage), it is clear that the growth of
data usage for devices in LTE networks is even larger
than projected .
9 Source: https://www .alcatel-lucent .com/blog/corporate/2013/05/lte-video-netflix-coming-soon-mobile-screen-near-you
-
24Motive mobile device report | June 2015
Signaling analysis: How Androids and iPhones are differentThis section is dedicated to the analysis of signaling activity and the
top applications that contribute to it on Androids and iPhones . Because
Androids and iPhones behave very differently with regard to signaling,
we are focusing our analysis on this topic, which is more revealing than
examining data usage . For example, the top ten applications by data usage
for Androids and iPhones are almost the same on every network, indicating
that users of both device categories have very similar application choice
preferences . These top ten applications include YouTube, HTTPS, Facebook,
Google, and several video applications from Facebook and Instagram . In
addition, the average daily data usage per user for Androids and iPhones is
very similar, as observed previously in Figure 5 . On the other hand, the top
ten applications by signaling activity have only five applications in common
across Androids and iPhones: WhatsApp, HTTPS, HTTP, Facebook and
Facebook Messenger . In addition, the daily signaling activity of Androids is
notably higher than on iPhones in every single network that was examined .
The question were asking is this: Why is the signaling behavior so
different across Androids and iPhones? Many factors influence the amount
of signaling exhibited by a device, including the nature of the applications
used, the networking efficiency of the application client implemented on
the device and how the device is configured to interact with the radio
network . (For example, when and how does it release radio channels?)
It is difficult to pinpoint how all these factors weigh in to make Android-
based devices exhibit higher signaling as configuration and design aspects
can vary across the implementation of smartphones . For instance, 3GPPs
network-controlled fast dormancy feature was endorsed by Apple in
201010 and adopted by many other smartphone manufacturers . This
feature was designed to reduce the chattiness of smartphones by setting
parameters on how, and how often, a smartphone switches between idle
and active modes while also preserving device battery life . Although
endorsed by Apple, its implementation and configuration can vary across
smartphone manufacturers and OS versions thus creating variance on how
it behaves in the network with respect to signaling .
In this section, we take a closer look at the top signaling applications
to reveal important differences in how applications on these device
categories interact with the network . Our traffic measurements suggest
that the push-notification infrastructure used by the applications on these
devices is likely an important contributing factor to the amount of signaling
each device generates .
. . .the top ten applications by data usage for Androids and iPhones are almost the same on every network, indicating that users of both device categories have very similar application choice preferences .
10 Source: http://www .lightreading .com/apple-cuts-iphone-signalling-chatter/d/d-id/682145
-
25Motive mobile device report | June 2015
Top signaling applications of Androids and iPhonesFigures 14 and 15 show the top ten applications that account for the largest amount of daily signaling seen on iPhones and
Androids . These are the applications that generate the largest amount of signaling activity in the network over any given
day for each respective device category . The Y axis shows the percentage share of signaling activity each application is
responsible for, from among the top 90 heavy-signaling applications on that particular smartphone category .
Figure 14 shows which applications have the highest percentage share of signaling activity for Android . Facebook Messenger
has the highest share at 17 percent, following by Google Cloud Messaging (GCM) at 13 percent, Google at 12 percent, HTTPS
at 11 percent, Facebook at 10 percent . Rounding out the top ten is HTTP, WhatsApp, Google Play, Extensible Messaging and
Presence Protocol (XMPP), and Viber . XMPP is a protocol that is primarily used by Google Talk and Viber .
F ig u r e 14 . A p p l ic a t io n s wi th h ig h e s t p e r ce n t a g e s h a r e o f s ig na l in g a c t iv i t y f o r A n dr o id
FacebookMessenger
GCM Google HTTPS Facebook HTTP GooglePlay
XMPP ViberWhatsApp
Perc
ent sh
are
of signaling activ
ity
14
12
10
6
8
4
2
16
18
20
0
17
13 12
1110
6 5
43 3
Figure 15 shows applications with the highest percentage share of signaling activity for iPhone . Apple Push Notification
Service (APNS) has the highest share at 38 percent, followed by HTTPS at 12 percent, Facebook Messenger at 9 percent, Apple
at 6 percent, and Facebook at 6 percent . Rounding out the top ten is Hotmail, HTTP, Apple Maps, Microsoft, and WhatsApp .
F ig u r e 15 . A p p l ic a t io n s wi th h ig h e s t p e r ce n t a g e s h a r e o f s ig na l in g a c t iv i t y f o r iP h o n e
APNS HTTPS FacebookMessenger
Apple Facebook Hotmail AppleMaps
Microsoft WhatsAppHTTP
Perc
ent sh
are
of signaling activ
ity
30
25
20
10
5
15
35
40
45
0
38
129
6 6 53 3 3 2
It is important to note that these are not all applications that users recognize . Some run in the background to provide
supporting services . The two background applications that show up in Figures 14 and 15 are APNS11 and GCM12, respectively .
These applications provide notification services to applications running on iPhones and Android-based smartphones .
11 Source: https://developer .apple .com/library/ios/documentation/NetworkingInternet/Conceptual/RemoteNotificationsPG/Chapters/ApplePushService .html12 Source: http://developer .android .com/training/cloudsync/gcm .html
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26Motive mobile device report | June 2015
When comparing Figure 14 and Figure 15, its clear
that the distribution of the share of signaling across
the top ten applications is very different . For iPhones,
APNS dominates and accounts for a 38 percent share
of daily signaling activity, while the share of signaling
activity drops significantly across all other applications .
For Androids, Facebook Messenger and GCM are at the
top with 17 percent share and 13 percent share,
respectively, while the share of signaling activity drops
gradually for the other applications .
This trend reveals that, on iPhones, a good portion of
the signaling is due to the delivery of push notifications
from APNS . On Androids, the effect of GCM is not as great,
and the signaling impact is more evenly spread across a
larger set of apps . In fact, GCM on Androids is the second
top signaling application, accounting for less than half as
much signaling share as APNS does on iPhones . Does this
mean that Androids handle fewer push notifications than
iPhones? To answer this, we need to examine how both
push notifications mechanism are handled .
Apple13,14 was first to develop a push notification feature
into smartphones, recognizing that it was critical for
applications to have a reliable, scalable and efficient
mechanism for delivering notifications to devices . The
core design principle behind Apples solution is its
centralized server, which coordinates the delivery of
notifications to applications on a phone . As a result, theres
no need for each application to develop and support
its own notification mechanism . A large base of iPhone
applications came to rely on this centralized mechanism .
After the Android entered the smartphone landscape,
Google developed its own push notification infrastructure,
called GCM, which also centralizes how notifications are
managed . In addition, a number of third-party notification
applications emerged, such as Xtify and Urbanairship . In
the usual spirit of openness and flexibility of the Android
community, this has led to a fragmented base of developer
preferences for how to handle push notification services .
The signaling impact of the APNS is quite large because
it accounts for the signaling done on behalf of a large
number of iPhone apps, whereas Googles GCM appears to
be serving a smaller set of applications . Xtify, for example,
handles a lot of signaling traffic on Androids even though
it does not appear in Figure 14 .
Apple was first to develop a push notification feature into smartphones, recognizing that it was critical for applications to have a reliable, scalable and efficient mechanism for delivering notifications to devices .
13 Source: http://www .apple .com/pr/library/2009/03/17Apple-Previews-Developer-Beta-of-iPhone-OS-3-0 .html
14 Source: http://venturebeat .com/2009/03/17/after-re-architecture-apple-finally-ready-to-push-push-notifications
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27Motive mobile device report | June 2015
Application signaling cost analysis for Androids and iPhonesTo further contrast Android and iPhone signaling behavior,
Figure 16 shows the per-application signaling activity cost,
measured by the average number of setups per day, for the
top signaling applications already identified .
For every application that is common across both devices,
signaling activity is heavier on Androids than on iPhones .
For example, WhatsApp has 33 connection setups a day on
Androids, compared to 19 on iPhones . Because WhatsApp
on iPhone uses APNS,15 much of the signaling required
for notifications is being accounted for within APNS, thus
lowering the overall signaling of the application . This is
similar with Facebook Messenger, HTTPS, HTTP, Facebook
and other common applications .
The net result appears to be that the aggregation of
notifications performed by APNS, coupled with well known
architectural limitations of APNS (which limit the number of
connections that are available for handling notifications),16
steer iPhone application developers to become more
network friendly and place a cap on the aggregate signaling
load across the registered applications .
Conversely, when push notifications are distributed across
different notification components, as appears to be the case
with Androids approach, multiple applications are likely to
compete for network resources and hit the network with
more frequent connection setup requests .
It remains to be investigated whether or not the
responsiveness of applications regarding notifications is
compromised on iPhones in a way that affects the users
perceived quality of experience . However, from a network
perspective, the centralization of push notifications under
APNS seems to have a net effect of lowering the overall
signaling activity on the iPhone while enabling radio
efficiencies that would not be viable in a more distributed
solution, such as the Androids GCM .
F ig u r e 16 . D a i ly p e r-a p p l i c a t io n s ig na l in g co s t f o r A n dr o id ( le f t ) a n d iP h o n e s ( r ig h t )
Facebook
Messenger
WhatsApp
Facebook
HTTPS
GSM
Google
Viber
XMPP
HTTP
Google
Play
APNS
Facebook
Messenger
Hotm
ail
HTTPS
WhatsApp
Facebook
Microsoft
HTTP
Apple
30
20
10
40
50
60
0
57
33
29 2826
24
18 18
16
9
56
25
2019 19
17
108
7 7
Apple
Maps
Sig
naling act
ivity (se
tups)
15 Source: http://www .whatsapp .com/faq/en/iphone/2095011616 Source: https://cloud .google .com/solutions/mobile/ios-push-notifications
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28Motive mobile device report | June 2015
Digging deeper: Googles power to impact network signalingFrom January 12 to February 19, 2015, a dramatic increase in signaling for the GCM application was observed across many
networks . Figure 17 shows a representative signature of this phenomenon .
F ig u r e 17. P e r ce n t a g e s u b s c r ib e r a n d s ig na l in g s h a r e s f o r G o o g le C lo u d M e s s a g in g
25
23
21
19
17
27
29
31
33
35
15
Percent sh
are
Subscriber shareSignaling share
January 12 February 4
6% erosion of network signaling capacity
February 19
The bottom line on the chart reflects the percentage share
of signaling activity for the GCM application over time .
On January 12, GCM experienced a significant increase in
signaling, as shown by its increase in signaling share from
17 percent to 20 percent . On February 4, GCM experienced
another signaling increase, as its signaling share went
from 21 percent to a peak of 23 percent . This increase
in signaling resolved itself on February 19, when GCMs
signaling share went back down to expected levels .
The top line on Figure 17 shows the percentage subscriber
share of the GCM application over the same time frame .
Clearly, there is no increase in subscriber share during the
time the signaling increase occurred . This indicates that
the increase in signaling activity for GCM was not due
to an increase in active subscribers .
Although a rise in signaling share from 17 percent to 23
percent on a single application may appear rather innocuous
at first, it does have a significant impact on networks .
During this period of signaling increase, an average erosion
of 6 percent in overall signaling capacity was experienced
across the networks that were analyzed . This is a costly
loss that can place a large strain on radio resources, and
it can even cause outages in locations that were already
operating close to capacity or where there was a
dominant proportion of Android users .
This signaling increase also impacts users, as individual
signaling activity costs increased anywhere from 6 percent
to 51 percent, with an average 32 percent increase across
all networks . This is important because signaling activity
is a significant contributor to battery drain, which is of
primary concern for mobile users .
The incident shown in this case study highlights the great
vulnerability of carrier networks to sudden changes in
the signaling behavior of popular applications . A similar
incident, featured in a previous blog17 in Analytics Beat,
occurred when Facebook released a chattier version
of its popular application . Developers of widely used
applications need to be aware of their responsibility to
ensure that software updates do not adversely affect
how their apps interact with networks .
17 Source: https://www .alcatel-lucent .com/blog/corporate/2013/01/new-facebook-not-only-draining-your-personal-time-mobile-network-capacity-well
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29Motive mobile device report | June 2015
ConclusionAs mobile devices continue to grow exponentially around the world, they place greater
demands on service providers data and signaling infrastructures . A detailed understanding
of the behavior and impact of these devices can benefit consumers and service providers
alike . Consumers can use device cost and efficiency information to adjust their usage
behavior and their application and device choices, so they can optimize their experience,
while getting the most from their personal investment . Service providers can benefit
in several key ways . For example, they can anticipate the impact of device growth and
popularity shifts as consumer trends shift . They will also be in a position to predict the
impact of device proliferation while optimally planning network growth and the promotion
of new devices . They can also use these insights to engage with device manufacturers,
discussing how to optimize the behavior of devices on the network .
This study highlights the power of knowledge with respect to the impact of mobile devices
on the global composite 3G and LTE networks . But each individual network is truly unique,
and device behavior and associated network impact is heavily influenced by market
coverage, data plan variety, population demographics and cultural preferences . To fully
harness the possibilities offered by insights like those contained within this report, service
providers need to conduct their own studies using data derived from their own networks .
Service providers can gain more powerful insights about their networks with the Motive
Wireless Network Guardian, a network analytics solution that can correlate the six key
dimensions of mobile intelligence . Then they can put that information to work across
all parts of their organization using Motive Big Network Analytics .
As mobile devices continue to grow exponentially around the world, they place greater demands on service providers data and signaling infrastructures .
-
www.alcatel-lucent.com Alcatel, Lucent, Alcatel-Lucent and the Alcatel-Lucent logo are trademarks of Alcatel-Lucent . Apple and iTunes are trademarks of Apple Inc ., registered in the U .S . and other countries . Google, Google Maps, Android and YouTube are trademarks of Google, Inc . The trademark BlackBerry is owned by Research In Motion Limited and is registered in the United States and may be pending or registered in other countries . All other trademarks are the property of their respective owners . The information presented is subject to change without notice . Alcatel-Lucent assumes no responsibility for inaccuracies contained herein . Copyright 2015 Alcatel-Lucent . All rights reserved . PR1505011672EN (June)
About this reportSummaryKey findings
Network impact of mobile devicesNetwork impact rankingsDigging deeper: Inside the Android network impactDigging deeper: How network impact varies across individual networks
A closer look at devicesRadio inefficiency scoresIndividual device network cost rankingsDigging deeper: Inside the Android cost bubbleDigging deeper: Regional variations
The LTE factorLTE versus 3G: Network impact scoresLTE versus 3G: device costs
Signaling analysis: How Androids and iPhones are differentTop signaling applications of Androids and iPhonesApplication signaling cost analysis for Androids and iPhonesDigging deeper: Googles power to impact network signaling
Conclusion