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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
2
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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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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0 0,5 1 1,5 2 2,5
Frac
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f m
eas
ure
me
nt
no
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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
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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
0
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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
05/1000:00
05/1006:00
05/1012:00
Avg
. U
DP
RT
T (
in m
s)
Avg. UDP RTT - 1 sec IAT and 20 byte payload
Telenor
0
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1000
09/0800:00
09/0812:00
09/0900:00
09/0912:00
09/1000:00
09/1012:00
09/1100:00
09/1112:00
Avg
. U
DP
RT
T (
in m
s)
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
15
Measurements can give increased competition and innovation
Measurements are needed to understand the robustness of mobile broadband networks
Coming soon: http://robustenett.no
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