© 2009 TKK & NOKIA 1 Mobile Service Usage and Business Models in Wireless Local Area Networks Beyene Abebe 17.08.2009 Supervisor Prof. Heikki Hämmäinen

© 2009 TKK & NOKIA1

Mobile Service Usage and Business Models in Wireless Local Area Networks

Beyene Abebe17.08.2009

Supervisor Prof. Heikki Hämmäinen Instructors Timo Smura , Thomas Casey

M.Sc.(Tech.)

Projects MOMI & IMCOS


Outline

• Definition• Research questions and scope• Research methods and tools• Results from handset-based measurement analysis• System dynamics modeling of local area access value networks • Discussion


Definition

• Mobile Service Usage and Business Models in Wireless Local Area Networks

- the service that an end-user with a mobile device receives from the network operator or a 3rd party service provider.

e.g. voice/video calls, SMS and MMS messages, access to content on web, email, ticketing or booking and etc.

• Mobile Application

- “either network or handset-based pieces of software that run services”

e.g. Music Players, Logs, Calendars, Contacts and Handset Clocks


Definition (2)

• “Application is a more technical term referring to the technical solution, whereas service is the whole entity as seen by end-users.”

• Sometimes the terms service and application are used interchangeably, for example, in case of voice calling and SMS messaging applications.


-“ Description of how a company or a set of companies intend to create and capture value with a product or service by linking new technological environments to business strategies ”


- wireless networks which provide local area access with high data-transfer rates. E.g. Wi-Fi


Research questions and scope (1/2)

1) How do people use mobile devices (especially indoor located devices) with different access technologies?

(extension of the work reported in Smura (2008))

• Scope• Devices

• Handsets• Markets

• Finnish market


Research questions and scope (2/2)

2) How is network connectivity to indoor located devices provided in the future? What are the possible evolution paths?• The concept of system dynamics modeling is applied


• All mobile and portable devices included• But more focus on handsets and laptop devices

• Markets • Finnish market

• Traffic type • Data traffic

• Time Frame • 2009-2015


Research methods and tools

• Handset-based mobile service usage measurement• Data was collected from two panels of Finnish smartphone users during October – December

2008 • Finnish2008 panel• OtaSizzle panel

• A handset-based measurement platform (developed for Nokia S60 class of mobile devices) was used to collect the data

• The platform consists of a Symbian application monitoring software client which is installed to the mobile devices

• 223 panelists successfully installed the software client • 70 were excluded due to too few active days in the panel• 4 were excluded because they were only testing the software client and used unclear foreign language

settings. • Therefore , 149 panelists with S60 3rd edition devices were included in this study (105 and 44 from

Finnish2008 panel and OtaSizzle pannel,respectively)

• System Dynamics• Vensim PLE software

• Brainstorming session• Conducted with a group of experts from Nokia Research Centre (NRC) on 14th of May

2009

Results from handset-based measurements analysis



Usage of different applications (1/3)

• When and for which application do people use their mobile device?

Figure 1:Distribution of active smartphone usage time between application categories and hours of day, average over Monday-Sunday throughout the panel period. N=149

• Voice calling application is dominant on average.

• Voice calls accounted for 30.7% of the total average daily active usage seconds per panelist.

• Browsing, messaging and business and productivity accounted for 21.9%, 20.1% and 8.5%, respectively while multimedia accounted for 6.5%.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

40

80

120

160

200

Voice calls Browsing Messaging Business and productivity Multimedia Others

hour

activ

e us

age

seco

nds


Usage of different applications (3/3)

• The most actively used applications under different application categories

Application categories Application names Average usage_seconds/day/panelistWeb 405.53

BrowsingServices 36.36

Opera Mini 89.3Text message 272.82

Messaging MMS 7.21

Music player 55.61

MultimediaGallery 26.28

Camera 34.27Calendar 57.36

Business and productivity

Contacts 39.9Log 29.84

• Web, text message, music player and calendar are the most widely used applications under their category.

Table 1: Actively used mobile applications under different application categories. N=149


Usage of different access networks (1/3)

• When and which bearer technology do people use to start data session?

Figure 3:Distribution of daily data usage between bearers and hours of day, average over Monday-Sunday. N=130

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

20

40

60

80

WCDMA WLAN GPRS EDGE

hour

kB p

er p

anel

ist

per

day

• On average the daily data usage in kB for WCDMA is higher than other bearer technologies.


Usage of different access networks (2/3)

Figure 4: Distribution of active usage time in seconds between bearers and hours of day, average over Monday-Sunday for N=130

• The active usage (in seconds) for WCDMA is also greater than other bearer technologies on average

Figure 3 and Figure 4 show that WCDMA is the most widely used bearer technology.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

40

80

120

160

200

240

280

320

WCDMA GPRS WLAN EdgeGPRS

hour

activ

e us

age

seco

nds


Usage of different access networks (3/3)• Users’ choice between access networks while launching various applications

Table 3: Usage of application categories during WCDMA, WLAN, GPRS and EDGE connections. N=130

• More users for browsing applications

• For multimedia applications, less number of sessions per user

• However ,high amount of data transmitted per session

• A smartphone data session during WLAN connections was higher in terms of data volumes than during wide area network connections (for browsing and messaging)

• More than 85% of WLAN users used WLAN connections for web browsing, while 32% used it for communications services such as messaging and 15% for streaming multimedia content

WLAN is mainly used by those users who in general use large amounts of data services.

Access networks

Active users

Application categories

Applications users

Share of users

No of sessions per user during the panel

Average data per session (kB)

Browsing 102 90% 20.36 481.46

WCDMA 113 Messaging 71 63% 10.30 109.12

Multimedia 27 24% 0.85 2474.26 Browsing 46 87% 3.72 1420.92 WLAN 53 Messaging 17 32% 0.58 654.63 Multimedia 8 15% 0.15 1437.50 Browsing 60 74% 4.12 416.32 GPRS 81 Messaging 46 57% 1.54 78.45 Multimedia 8 10% 0.10 735.25 Browsing 57 77% 2.87 429.81 EDGE 74 Messaging 31 42% 3.13 28.64

Multimedia 6 8% 0.05 194.82


Daily data usage of WLAN vs. non-WLAN users

Figure 5: Daily data usage per bearer for panelists either using or not using WLAN during the panel. N=130

• The daily data usage of WLAN users was higher than that of non-WLAN users in all types of bearer technologies.

• The share of WLAN access increased from 12% of total network data traffic in 2007 (Smura, 2008) to 39% in 2008

• The share of WCDMA access dropped from 79% among non-WLAN users to 50% among WLAN users

WLAN can be considered as a potential substitute to 3G networks for usage of data services.


Users’ choice between alternative access methods

Figure 6: Distribution of WLAN usage events between users and access points. N=53

• On average, for each user, 80% of WLAN connections were made to the first access point, 12% to a second one, and 8% to other access points.

• 91% of all WLAN connections

were made from private access points while the rest was from public ones

Most of WLAN connections are made from private access points located at home or office.


Conclusions

• Voice call is still a dominant application category used by mobile users followed by browsing, messaging, business and productivity, multimedia and others in order.

• WLAN is a potential alternative access to 3G networks for usage of data services.

• WLAN is mainly used by those users who in general use large amounts of data services.

• 91% of all WLAN connections were made from private access points while the rest was from public ones

• The share of WLAN access increased from 12% of total network data traffic in 2007 (Smura, 2008) to 39% in 2008

• Limitation: limited number of panelists


System Dynamic Modeling of Local Area Access Value Networks


Basics of System Dynamics

• Causal loops diagrams

• Stock and flow


Background

• Goal to continue the Scenario planning work conducted by Smura & Sorri 2009• Research question

• How is network connectivity to indoor located devices provided in the future?• What are the possible evolution paths?• Time Frame: 2009-2015


• All mobile and portable devices included• But more focus on mobile and laptop devices

• Markets → Focus on Finnish market• Traffic type → Data traffic studied

• The overall goal is to understand dynamic relationships between forces not to model exact numerical values (quantitative modeling based on literature and expert opinion)

• System dynamic modeling is an iterative process and the results presented here should serve as the first iteration round

Scenarios

Horizontal industry structure

Vertical industry structure

WA-LAdivorce

WA-LAmarriage

1. Pick-n-mix - Internet rules

2. Complete bundles- Operator rules

3. Operatorsas bitpipes

4. Internet giants

Source: Smura & Sorri (2009)


Cause for disruptions? (”muljahdus”)

Vertical Integration (services +

NW access?) U1

Level of access techology fragmentation

(Wide Area (WA)+ Local Area (LA)?)

U2, U3, U4



Uncertainties Trends

T31: Num of non-3GPP LA APs

T33: Number of 3GPP WA BSs

T32: Number of 3GPP LA APs

Main Forces used in SD model

U2: Competition between technology substitutes

T1: Devices’ capabilities and performance improve

U1: Industry structure

T2: Wireless traffic will increase

T4: Importance of indoor wireless access increases

U3: Spectrum policy and regulation

U4: Role of unlicensed spectrum

T6: Operational costs will dominate over hardware costs

T3: Number of base stations / access points increases

U5: Number of connected devices

U6: Role of emerging markets in affecting technology choices

T5: Role of developing countries increasing

T7: Wireless emissions scare people

U22: Share of LA out of all indoor traffic

U23:market share of 3GPP operator of all indoor traffic

U21: Share of 3GPP LA out of all LA

T12: AP self optimizing capability

T11: Device access selection capabilities



Conceptual Model

• Background assumptions: • Unserved demand drives the supply• The capacity of backhaul network is not a bottleneck (since a major part of the traffic is

local)


The model has four domains:

1. User (Demand)

2. Infrastructure (Supply)

3. Spectrum Regulation and Technology

4. Market Share (defined in terms of traffic volume)


1.User (Demand)

• Demand based on two components:

• Primary devices (includes laptops, smartphones)

• Secondary devices (household appliances etc.)


2.Infrastructure (Supply)• Infrastructure is

expanded based on • Unserved

demand• Relative market

shares of each technology

• Yearly traffic volume supply of each technology


3.Spectrum Regulation and Technology• Yearly traffic volume

supply calculated for each technology based on:

• Capacity of the technology (spectrum and technology development)

• Simple traffic model (active yearly usage time)


4.Market Share

U23 = 0

In the model variable U23

(Market share of 3GPP operator)

indicates the level

of fragmentation in local

access provisioning

U23 = 1

Non-3GPP LA traffic share

3G/4G WA traffic share

3GPP LA traffic share

All indoor traffic

WA-LADivorce/ fragmented

access

WA-LAMarriage/

integerated access

Results and Sensitivity Analysis

• Base Case

- refers to our system dynamics model simulated with the initial values

• Base Case Configurations (Initial values)

• Typical Finnish suburban area with 2300 users/km²• Indoor access provisioning is fragmented (U23 ≈ 0.02)• T31: Num of non-3GPP LA APs (WiFis) = 300 APs/km²• T32: Number of 3GPP LA APs (Femtocells) = 10 APs/km²• T33: Number of 3GPP WA BSs = 10 sectors/km²


Base Case Results


Wireless indoor traffic demand and supply

8 B

6 B

4 B

2 B

02

22

22

22

22

22

2

22

2

1 1 1 1 1 11

11

11

1

11

1

2009 2010 2011 2012 2013 2014 2015Time (year)

MB

/(Km

2*ye

ar)

wireless indoor traffic supply : Base Case 1 1 1 1 1 1 1 1

wireless indoor traffic demand : Base Case 2 2 2 2 2 2 2

• The capacity of the infrastructure is sufficient until 2011 when demand surpasses supply

MB/(Km2*year) = MB/Km2/year (Vensim feature)

Unserved wireless indoor traffic demand

400 M

300 M

200 M

100 M

01 1 1 1 1

1

1

11

1 11

11

1

2009 2010 2011 2012 2013 2014 2015Time (year)

MB

/(K

m2

*y

ear)

unserved wireless indoor traffic demand : Base Case 1 1 1 1 1 1 1 1

Base Case Results (2)


T31: Num of non-3GPP LA APs

600

400

200

1 1 1 1 11

11

11

1

2009 2010 2011 2012 2013 2014 2015Time (year)

AP

/Km

2

"T31: Num of non-3GPP LA APs" : Base Case 1 1 1 1 1 1 1

T32 Number of 3GPP LA APs

60

45

30

15

0

1 1 1 1 11

1

1

1

1

1

1

2009 2010 2011 2012 2013 2014 2015Time (year)

AP

/Km

2

T32 Number of 3GPP LA APs : Base Case 1 1 1 1 1 1

T33: Number of 3GPP WA BSs

20

17

14

11

8

1 1 1 1 1

1

1

1 1 1 1 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

BS

/Km

2

"T33: Number of 3GPP WA BSs" : Base Case 1 1 1 1 1 1

• The maximum WA BS density is quickly reached after demand surpasses supply

150% growth

500% growth



Spectrum policy and regulation flexibility

0.1

0.085

0.07

0.055

0.04 1 1 1 1 1 1 1 1

1

1

11 1

1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"U3: spectrum policy and regulation flexibility" : Base Case 1 1 1 1 1 1 1 1

• After some delay unserved demand drives spectrum policy and regulation to become slightly more flexible



• The growth in the number of WA BSs increases the market share of3GPP operators but the installed base of WiFi access points dominates (role of femtocells marginal)

Note that when U23 = 0

→ fragmented caseAnd when U23 = 1 → integrated case

Level of technology integeration in access provisioning

0.04

0.0325

0.025

0.0175

0.01

1 1 1 11

1

1 1 1 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"U23:market share of 3GPP operator of all indoor traffic" : Base Case 1 1 1 1

Base Case Conclusion

• Indoor access provisioning is fragmented (U23 ≈ 0.03)


Sensitivity cases


Case 1: High device access selection capabilities


Case 1: High device access selection capabilities

1

0.5

02

2

2

22 2

11

11 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"T11:device access selection capabilities" : Base Case 1 1 1 1"T11:device access selection capabilities" : Case 1 2 2 2 2 2

Case 1 Results


Wireless indoor traffic demand/served demand

8 B

6 B

4 B

2 B

04

44

44

4

4

4

4

4

4

33

33

33

3

3

3

3

3

22

22 2

22

22

2

2

11

11

11

1

11

1

1

2009 2010 2011 2012 2013 2014 2015Time (year)

MB

/(K

m2*

year

)

wireless indoor traffic demand : Base Case 1 1 1 1 1 1

"T2: wireless indoor traffic" : Base Case 2 2 2 2 2 2

wireless indoor traffic demand : Case 1 3 3 3 3 3 3

"T2: wireless indoor traffic" : Case 1 4 4 4 4 4 4 4

• Higher device selection capabilities increase demand and supply• Less unserved demand

Case 1 Results (2)


• Favors the growth of Wi-Fi access points (non-3GPP LA APs)The market share of 3GPP operators declines which reflects the possible

evolution path towards fragmented access in the local area access provisioning

Number of base stations / access points

600

450

300

150

0 6 6 6 6 6 6 65 5 5 5 5 5 5

4 4 44

4

4

4

3 3 3 3 3 3 32 2 2 2 2 2 2 2

1 1 1 11

11

1

2009 2010 2011 2012 2013 2014 2015Time (year)

AP

/Km

2

"T31: Num of non-3GPP LA APs" : Base Case 1 1 1 1 1T32 Number of 3GPP LA APs : Base Case 2 2 2 2 2 2"T33: Number of 3GPP WA BSs" : Base Case 3 3 3 3 3 3"T31: Num of non-3GPP LA APs" : Case 1 4 4 4 4 4T32 Number of 3GPP LA APs : Case 1 5 5 5 5 5"T33: Number of 3GPP WA BSs" : Case 1 6 6 6 6 6

Level of technological integeration in access market

0.04

0.0325

0.025

0.0175

0.01

2 2 2 2 22

2

22

22 2 2 2

1 1 1 1 1 1

1

1 1 1 1 1 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"U23:market share of 3GPP operator of all indoor traffic" : Base Case 1 1 1 1 1 1 1"U23:market share of 3GPP operator of all indoor traffic" : Case 1 2 2 2 2 2 2 2 2

Case 1 Results (3)


Spectrum regulation flexibility

0.1

0.085

0.07

0.055

0.04 2 2 2 2 2 22

2

22 2

2

1 1 1 1 1 1 1

1

1

11 1

1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"U3: spectrum policy and regulation flexibility" : Base Case 1 1 1 1 1 1"U3: spectrum policy and regulation flexibility" : Case 1 2 2 2 2 2 2 2

• Better device access selection capability leads to less unserved demand and hence less pressure for flexibility spectrum regulation

Case 2: High AP self optimizing capability and high willingness of operators to subsidize femtocells


Case 2: High AP self optimizing capability

4

3

2

1

0

4 4 4 4 4 4 4 4 4 4 4

3 3 3 3 3 3 3 3 3 3 3

2 22

2 2 2

1 1 1 1 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"T12: AP self optimizing capability" : Base Case 1 1 1 1 1

"T12: AP self optimizing capability" : Case 2 2 2 2 2 2

femtocells subsidization factors : Base Case 3 3 3 3 3

femtocells subsidization factors : Case 2 4 4 4 4 4 4

Case 2 Results


Number of base stations / access points

600

450

300

150

0 6 6 6 6 6 65 5 5

5

5

5

4 4 44

44 4

3 3 3 3 3 3 32 2 2 2 2 2 2

1 1 11

1

11

2009 2010 2011 2012 2013 2014 2015Time (year)

AP

/Km

2

"T31: Num of non-3GPP LA APs" : Base Case 1 1 1 1T32 Number of 3GPP LA APs : Base Case 2 2 2 2 2"T33: Number of 3GPP WA BSs" : Base Case 3 3 3 3 3"T31: Num of non-3GPP LA APs" : Case 2 4 4 4 4 4T32 Number of 3GPP LA APs : Case 2 5 5 5 5 5"T33: Number of 3GPP WA BSs" : Case 2 6 6 6 6

• A better chance for femtocell technology to get off the ground The market share of 3GPP operators grows which reflects the possible evolution path towards

integrated access in the local area access provisioning

0.2

0.15

0.1

0.05

02 2 2 2 2 2

2

2

2

2

2

2

2

1 1 1 1 1 1 1 1 1 1 1 1 1 1

2009 2010 2011 2012 2013 2014 2015Time (year)

Dm

nl

"U23:market share of 3GPP operator of all indoor traffic" : Base Case 1 1 1 1 1 1"U23:market share of 3GPP operator of all indoor traffic" : Case 2 2 2 2 2 2 2 2


Discussion• The simulated evolution paths led to a rather fragmented indoor

access provisioning scenario• The large installed base of WiFi (T31: Num of non-3GPP LA APs) seems to dominate

(loop B_non_GPP catered to most of the unserved demand)• Only heavy subsidization and high AP self-optimizing ability (case 2) led to a

significant market share for femtocells within the study period• Femtocells were however on a positive growth path (e.g. in the base case the

number of femtocells grew 500 % during the study period)

• System dynamic modeling is an iterative process and the results presented here should serve as the first iteration round

• are a good basis for continuing the iterative SD modeling process

• One should focus on the usefulness of a model rather than on validation and verification

• Modeler’s skills limited• The first time the concept of system dynamics is being applied in the

topic area


Discussion (2)

• Recommendations for further work• Quantitative modeling could be improved

• Market share currently defined based on data volumes • → WiFi dominates • → should market share be defined differently (e.g. based on access points)?

• The traffic model is very simple at the moment (based on static active usage times)• The spectrum and technology domain needs more work

• Capacity of a technology should be based on spectral efficiency (i.e. Mbps/MHz) and the available blocks of frequencies

• Further calibration of the model based on more detailed data

• Modeling the y-axis (industry structure)• In the current model only operator femtocell subsidization could be seen as industry structure related

modeling• Willingness to pay, cost and revenue of transmission for each technology• Actors (end-users, 3GPP/local operators)

References

• T. Smura and A. Sorri, Future Scenarios for Local Area Access: Industry Structure and Access Fragmentation, in Proceedings of the Eighth International Conference on Mobile Business (ICMB 2009), Dalian, China, June 27-28, 2009, 2009

• T. Smura, Access alternatives to mobile services and content: analysis of handset-based smartphone usage data, in ITS 17th Biennial Conference, Montreal, Canada, June 24-27, 2008, 2008

• Sterman, J. D., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. The McGraw-Hill Companies, Inc.

© 2009 MOMI & IMCOS44

Back-up Slides

• Figure 5.1 Market share of the three technologies

Non-3GPP LA traffic share

3G/4G WA traffic share

3GPP LA traffic share

All indoor traffic

• U21: represents the ratio of Wi-Fi traffic to the sum of Wi-Fi and femtocell traffic.

• U22: represents the ratio of data traffic from Wi-Fi and femtocell access points to all indoor traffic.

• U23: represents the ratio of data traffic from femtocell and 3G/4G base stations to all indoor traffic. U23 can be seen as the combination of U2 and U4

Documents

© 2009 TKK & NOKIA 1 Mobile Service Usage and Business Models in Wireless Local Area Networks Beyene Abebe 17.08.2009 Supervisor Prof. Heikki Hämmäinen