Simula Research Laboratory AS

Resilient Networks

How robust are the mobile networks that carry the Internet of Things?

Amund Kvalbein, PhD Senior Research Scientist

Internet of Things value creation network, Telenor Expo, 19. March 2013

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Resilient Networks develops methods that make networks more robust when facing unexpected challenges

The rise of mobile broadband

• Global mobile data traffic grew 70% in 2012

• 2012 mobile data traffic was nearly twelve times the size of the entire global Internet in 2000

• The average smartphone will generate 2.7 GB of traffic per month in 2017, an 8-fold increase over the 2012 average of 342 MB per month

• 84% say they cannot go one day without their smartphone

• 20% say they check their phone every 10 mins

[Sources: Cisco Visual Networking index, Time Magazine poll]

Mobile broadband carries IoT

• Many of the envisioned IoT applications will use MBB for communication – Unrivalled coverage, global presence

– Increasing capacity

• Requirements vary widely among applications – Capacity, response time, stability, availability

• Some challenges are particular for m2m applications – Provider lock-in

– Suboptimal network engineering

Reliability of mobile broadband network is essential

2. Can we increase robustness by connecting to

multiple operators? Methods for seamless use of multiple networks

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1. Can we trust the mobile networks today?

Measuring the experienced stability and quality in all operators in Norway

“Measure the measurable, and make measurable what is not” - Galileo Galilei

• 400+ measurement nodes – 100 active today

• Wide geographical coverage – Dense deployment in major cities

• Connected to multiple MBB operators – 2-5 per node

• Active measurements – Focus on availability

Following results are from a previous deployment

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0,1

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f m

eas

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Downtime (%)

Downtime per operator May-July 2011

ICE

Netcom

Telenor

Mobile broadband is less stable than it should

1 in 3 connections are down more than 10 mins per day

Operator A

Operator B

Operator C

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ure

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Downtime (%)

Downtime per operator May-July 2011

ICE

Netcom

Telenor

Classify nodes based on relative performance

Operator A

Operator B

Operator C

0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 30 35 40 45

Fraction of time

Monitor

BadMedium

Good

0 0.2 0.4 0.6 0.8 1 1.2 0 5 10 15 20 25 30 35 40 45 50

Fraction of time

Monitor

BadMedium

Good

Monitors are good at different times

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ice.net

Telenor

Operators are good at different places

13

0 0.2 0.4 0.6 0.8 1 1.2 0 5 10 15 20 25 30 35 40 45 50

Fraction of time

Monitor

BadMedium

Good

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 0 5 10 15 20 25 30 35 40 45 50 55 60

Fraction of time

Monitor

BadMedium

Good

ice.net

Telenor

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2000

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05/0718:00

05/0800:00

05/0806:00

05/0812:00

05/0818:00

05/0900:00

05/0906:00

05/0912:00

05/0918:00

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05/1006:00

05/1012:00

Avg

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Avg. UDP RTT - 1 sec IAT and 20 byte payload

Telenor

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09/0800:00

09/0812:00

09/0900:00

09/0912:00

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09/1012:00

09/1100:00

09/1112:00

Avg

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Avg. UDP RTT - 1 sec IAT and 20 byte payload

Telenor

Networks not always tuned for m2m traffic

May 2012 September 2012

Governments need data to guide regulation

Customers need data to make informed choices

M2M applications need data about reliability

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Measurements can give increased competition and innovation

Measurements are needed to understand the robustness of mobile broadband networks

Coming soon: http://robustenett.no