UNDERSTANDING INTERNET USAGE AND NETWORK LOCALITY IN A RURAL - - PowerPoint PPT Presentation

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UNDERSTANDING INTERNET USAGE AND NETWORK LOCALITY IN A RURAL - - PowerPoint PPT Presentation

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UNDERSTANDING INTERNET USAGE AND NETWORK LOCALITY IN A RURAL COMMUNITY WIRELESS MESH NETWORK Adisorn Lertsinsrubtavee, Liang Wang, Nunthaphat Weshsuwannarugs, Arjuna Sathiaseelan, Apinun Tunpan, Kanchana Kanchanasut, Jon Crowcroft Computer

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SLIDE 1

UNDERSTANDING INTERNET USAGE AND NETWORK LOCALITY IN A RURAL COMMUNITY WIRELESS MESH NETWORK

Adisorn Lertsinsrubtavee, Liang Wang, Nunthaphat Weshsuwannarugs, Arjuna Sathiaseelan, Apinun Tunpan, Kanchana Kanchanasut, Jon Crowcroft

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Computer Laboratory, University of Cambridge intERLab, Asian Institute of Technology

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SLIDE 2

OUTLINE

➤ Internet in Rural Area of Thailand ➤ Community Network ➤ TakNet CWMN ➤ Social Interview ➤ Traffic Measurement & Data Analysis ➤ Discussion and Takeaways

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SLIDE 3

INTERNET IN RURAL AREA OF THAILAND

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28.92%

ranked 6th in AEC, as of 2014

ITU -D

Thailand’s Internet Penetration

High cost for infrastructure investment Low demand of using Internet

It is not cost-effective for ISP to invest network infrastructure Digital Divide

Thai National Statistical Office, http://web.nso.go.th/

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SLIDE 4

COMMUNITY NETWORK

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Internet

Wireless ad-hoc network sharing the Internet gateway Successful Community Networks !

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SLIDE 5

TAKNET CWMN

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Thai Samakhee a small rural village in northern Thailand

2 ADSL links provided by ISP

50 households with 300 population

Before 2013

28$/month for a subscription

TakNet CWMN Internet cost is shard among villagers

5$/month for a subscription

Attract villagers to use the Internet

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SLIDE 6

TAKNET CWMN

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Core Router Access Router ADSL gateway

R1 ADSL Gateway R2 R3 R4 R5 R7 R8 R9 R10 R12 R13 R14 Core Router R6 R11

Ad hoc link OLSR WiFi 16 GB external storage 14 access routers (TPlink MR 3040) 1 core router (Unifi UAP) OpenWrt, Attitude Adjustment 12.04

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SLIDE 7

UNDERSTANDING THE INTERNET USAGE

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Traffic Measurement Social Interview

➤ Lightweight measurement ➤ Traffic volume ➤ ifconfig - 60 sec interval ➤ Internet usage ➤ tcpdump - HTTP request ➤ Filter out the URL ➤ Well-defined questionnaire ➤ Personal information ➤ Typical usage ➤ User feedback ➤ 30 mins interview ➤ Free-style conversation

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SLIDE 8

SOCIAL INTERVIEW

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18 34 52

8

30

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Kids Teens Adults 8-16 16-21

  • ver 22

140$ - 560$

40%

60%

VS

91%

Popular content Device used

interviewees

User information

Teens Adults Kids

Monthly wages

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SLIDE 9

SOCIAL INTERVIEW

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User feedback

85% of users install CM battery application

(expect to improve their WiFi speed)

4 users opted out due to the extra cost incurred by

electricity bill (just 1-2$/month)

Social Communications

87% 71% 33% 81% of Line users

have local contacts within the same village

10-20% of messages

exchanged among local users

7.6% 21% 30%

Usage pattern

80% 17:00 - 22.00

22:00 - 06.00 12:00 - 17.00 06:00 - 12.00 12.5%

4 hours per day

  • n Internet usage
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SLIDE 10

TRAFFIC USAGE

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10 20 30 40 50 60 70 80 90 2000 4000 6000 8000 10000 12000 14000 Day (1 Febuary 2015 − 30 April 2015) Traffic load (MB/day) Feb Mar Apr

Download

10 20 30 40 50 60 70 80 90 500 1000 1500 Day (1 Febuary 2015 − 30 April 2015) Traffic load (MB/day) Feb Mar Apr

Upload

Traffic Growth Feb - Mar 15 Mar - Apr 15 Upload 25% 20% Download 28.9% 15%

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SLIDE 11

TRAFFIC USAGE PATTERN

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0.5 1 1.5 2 2.5 3 3.5 Traffic load (Mbps) Time of day (Hours) 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 01.00 07.00 13.00 19.00 Mon Tue Wed Thu Fri Sat Sun

Dual off-peak hours 13:00 - 16:00 22:00 - 07:00 Traffic almost reached the committed speed (4Mbps) How can we efficiently utilise these resources?

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SLIDE 12

CONTENT POPULARITY

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ksmobile naver baidu google 3g.cn umeng instagram fb youtube animaljam 2 4 6 8 10 12 14

Domain (url) Fraction of requests (%)

Zipf distribution with alpha = 0.57

➤ Commonly known alpha (0.9 - 1.1) ➤ Mixture of misbehaviour domains ➤ Some valuable domains such as education and local newspaper

are pushed to the tail

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SLIDE 13

CONTENT POPULARITY

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naver google instagram fb youtube apple msn ytimg thscore dek−d 2 4 6 8 10 12 14 Domain (url) Fraction of requests (%)

Zipf distribution with alpha 1.05 Education website appears

Remove all suspicious domains

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SLIDE 14

ANALYSIS OF SUSPICIOUS DOMAINS

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Jain’s fairness index

The higher value indicates the requests are more uniformly distributed

ksmobile naver baidu google 3g instagram umeng fb youtube animaljam 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Jain Fairness Index

Large amount of requests were generated by some specific users

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SLIDE 15

APPLICATION BEHAVIOUR

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Inter arrival time of suspicious domain baidu.com Almost 80% of requests are made with an inter arrival less than 2s These requests were generated by the baidu browsers

5 10 15 20 25 200 400 600 800 1000 1200 1400

Number of requests Interval (s)

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SLIDE 16

MISINFORMED KNOWLEDGE

➤ Several users misuse an application ➤ Considering ksmobile domain ➤ Android application —> CM Battery ➤ To save the battery power ➤ But! the villagers believe that it can use to accelerate the WiFi

speed

➤ Fact! it generates a lot of request to ksmobile domain ➤ Observation! advertisements are automatically downloaded to

users’ mobile phone

➤ 85% of villagers use this application

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LOCALISED COMMUNICATION

17 2 4 6 8 x 10

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1 2 3 4 5 6 7 8 9 10 11 12 13 14

Rotuer ID Time of day (second)

Line application (naver) HTTP request to Line server from each router Line server Sender Receiver Send msg Notification Retrieve msg

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SLIDE 18

LOCALISED COMMUNICATION

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3.2 3.205 3.21 3.215 3.22 3.225 3.23 x 10

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1 2 3 4 5 6 7 8 9 10 11 12 13 14

A pair of communication represents the localised communication Achieve10% - 15% of identified pairs Varying window size 1s - 5s From interview, 10% - 20% were sent to the local contacts

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SLIDE 19

TAKEAWAYS

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What are the impacts of TakNet ?

➤ TakNet is able to create a demand within the community for

Internet access

➤ Number of Internet users in TakNet is increased significantly ➤ Villagers gain significant benefits form the Internet ➤ TakNet is a catalyst for changes: ISPs expand more backhaul to

cover the villages

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SLIDE 20

TAKEAWAYS

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Is there a universal model for all rural settings?

➤ Traffic pattern ➤ Asia1 - TakNet: Dual-peak pattern ➤ Africa2 and Europe3: Single peak: ➤ Localised communication ➤ TakNet: 10 - 15%, Africa2: ~50% ➤ Social Communications ➤ OSN (e.g., FB, Twitter) and email are popular services in rural

Africa2.

➤ Instant messaging is the most dominant service in TakNet

1 B. Du, et al. Analysis of www traffic in cambodia and ghana. In WWW ’06. ACM, 2006. 2 D. L. Johnson, et al. Network traffic locality in a rural african village. In ICTD. ACM, 2012. 3 A. Sathiaseelan, et al. A feasibility study of an in-the-wild experimental public access wifi network. In ACMDEV

, 2014

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SLIDE 21

TAKEAWAYS

➤ The available 4 Mbps bandwidth may be saturated soon in the near

future.

➤ Can we simply expand the the link capacity or add more gateway? ➤ Villagers are very sensitive to the cost ➤ Can we utilise the off-peak hours with content/service caching ? ➤ Identify the true valuable contents ➤ Efficiently remove the suspicious domains ➤ New technologies ➤ Information Centric Network ➤ Service migration - virtualisation, container

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What are the potential solutions to improve TakNet?

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SLIDE 22

THANK YOU Q&A

Adisorn Lertsinsrubtavee al773@cam.ac.uk

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