mobile device report

Mobile device report June 2015

Ran

k by signaling ac

tivity

Rank by increasing data usage

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00 1 2 3 4 5 6 7 8 9 10

Symbian

Mobile Wi-Fi

TabletM2M

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AndroidSamsung

Androidother

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AndroidLG

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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


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/

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 .


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 .


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 .


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


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


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


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 .


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


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


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


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 .


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 .


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


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


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


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


ity


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



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


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


ity


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



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


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 .


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


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


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


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


ity


0 1 2 3 4 5 6 7 8 9 10

10

9

8

7

6

5

4

3

2

1

0


Mobile Wi-Fi

Tablet

M2M

BlackBerry

Dongle/Datacard

iPhone

Android

Windows Phone


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 .


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


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 .


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


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


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


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


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


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


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


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


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

mobile device report

Documents

android network impact

mobile device category

specific network

mobile data traffic

network impact scores

device analytics

petabyte of mobile data

cellular mobile devices