An overview on the relevancy of considering human interaction aspects to anticipate mobility management aspects.

1. Social Interaction and the Power of Mobility
Estimation
Rute Sofia
Rute.sofia@ulusofona.pt
09.01.2013
S-BRAIN 34, SITILabs, University Lusofona, Lisboa

2. Agenda
1. Social Interaction and Social Networking
2. Mobility Estimation
3. The Relevanced of Mobility Estimation

3. Social Interaction
The different aspects
Social Interaction is
Gazing at somebody
Talking at somebody
Gestures, etc.
In Social Networking, social interaction can be defined as
Engagement between Internet users
But. How to measure engagement? / see S-BRAIN 28 ☺
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But. How to measure engagement? / see S-BRAIN 28 ☺
And in general networking, social interaction is further reduced into
Times of interaction between users that are known or not
What matters are the devices and proximity, not the relation between their
owners

4. Social Interaction
A Few Tools…
And it is often also reduced to the use of Online Social Networks (OSNs)
Facebook
Linkedin
Orkut
But, how accurate is the data extracted from OSNs?
OSNs are mostly used to keep weak links – links that in real-life do not
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OSNs are mostly used to keep weak links – links that in real-life do not
correspond to a strong level of interaction
They can provide a form of engagement, but the derived graph does not
correspond to a social interaction graph in real-life

5. Social Interaction
Facebook inaccuracy
Slide extracted from the Study “User Interactions in Social networks and their Implications”, Christo
Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Zhao, UCL, 2009.
Almost nobody interacts with more
than 50% of their friends!
For 50% of users, 100% of interaction
comes from 20% of friends.
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4/2/2009University of California at Santa Barbara
5
For 50% of users, 70% of interaction
comes from 7% of friends.
comes from 20% of friends.

6. Top 10% of most well connected
users are responsible for 60% of
total interactions
Top 10% of most interactive users
are responsible for 85% of total
interactions• Social degree does not accurately predict
human behavior
• Interactions are highly skewed towards a small
percent of the Facebook population
• Social degree does not accurately predict
human behavior
• Interactions are highly skewed towards a small
percent of the Facebook population
Social Interaction
Facebook inaccuracy
Name, e-mail
4/2/2009University of California at Santa Barbara
6
Slide extracted from the Study “User Interactions in Social networks and their Implications”, Christo
Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Zhao, UCL, 2009.

7. Social Interaction
Accounting for Real-time User Interaction
Define social graphs with costs based on user interaction
Not necessarily based on friendship
e.g., the ULOOP project [5] considers a trust level for such interaction [6]
time is another measure that can be considered as cost link
And above all: engagement (see SBRAIN 38)
Additional relevant tools to acount for aspects of real-time user interaction
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Additional relevant tools to acount for aspects of real-time user interaction
Jabberwocky, for bluetooth: detecting in urban environments familiar
strangers [1]; provides the number of encounters with strangers in locations
Social Net [2], application which uses patterns of co-location to infer
common interests between users. Relies on time and duration of encounter.
Mtracker [3], application which relies on the history of visits by a device to
extract information about a visit, giving a rank (preference) to the location
visited.
Openbeacon.org and Dynamics of People Interaction, [4]
Crawdad, [5]

8. Mobility Estimation
Finding User roaming Habits
Internet users roaming behavior is based on specific routines
E.g.: Wi-Fi network at home, at affiliation regularly visited daily
By tracking such routine, it is possible to infer/estimate future behaviors
Mobility management can be improved based on estimation
QoS, routing, and other network operation aspects can be estimated by
adequate estimation
How to improve mobility management based on estimation?
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Better selection across available Mobility Anchor Points
Assisting handovers – reduce the required signaling

9. Mobility Estimation
Example
Visited network 1, rank 0.8
Visited network 2,
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Mtracker.txt
History of networks visited – shows the current list
adds new wireless APs around and starts ranking them
•Mtracker states that best gw (after GW1, active) is GW3 and that
A handover is expected to occur in x seconds
Visited network 2,
rank 0.3

10. Mobility Estimation
The Mtracker Plugin
User equipment application that passively ranks visited Wi-Fi
networks over time, by collecting a few parameter
Number of visits
Average visit time in seconds
Information concerning the visited network e.g. BSSID and SSID
Attractiveness of the network to the user: trust level the user has on
that ULOOP gateway
Rank is periodically updated based on the collected parameters
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Rank is periodically updated based on the collected parameters
Mtracker estimates a potential move
Selects best Access Point based on rank*
Considers average visit time as the indicator that a move can occur
* Paper under submission, contact: rute.sofia@ulusofona.pt

11. Mobility Estimation
Tracking User Behavior in Real Time
Mtracker is a standalone
application
History of a single
device
Ranks APs
Tracks roaming of a
single device
Can dump it into a
Visualizing Mtracker
Behavior inference
Visualizer: Work by Ana Luz and
Patricia Simoes, guided by Prof. Ines
Oliveira/Prof. Rute Sofia
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Can dump it into a
server (data platform)

12. Mobility Estimation
Integrating Social Interaction
Mtracker is a standalone application
History of a single device
Ranks APs
Tracks roaming of a single device
Can dump it into a server (data platform)
In SITI, the DTN-Amazon project [8] is working on social behavior
inference
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Based on a data repository, algorithm extracts a behavior
Based on individual patterns, will develop a proximity graph
Currently based on volume/frequency of contacts
In the future, based on trust level - derived from the ULOOP
trust management framework proposal

13. Mobility Estimation
The Relevance of Mobility Estimation
In Network Modeling and Network Validation
Most of the models we use in simulators and testbeds are not truly
accurate
A more recent wave (social models [9]) seem to be closer to user
interaction, but lack of validation prevents from globally adopting
these models, and from adding them in real network environments
In Machine-to-Machine communication
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In Machine-to-Machine communication
Adequate mobility estimation assists in improving proximity algorithms
In Distributed and Intelligent Information Systems (e.g. Traffic)
Assists in adequately providing cooperative behavior

14. References
[1] Familiar Stranger project, http://www.urban-
atmospheres.net/Jabberwocky/info.htm
[2] Social Net
[3] Mtracker, SITI,Labs, Universidade Lusofona, siti.ulusofona.pt
[4] Dynamics of Person-to-Person Interactions from Distributed RFID Sensor
Networks, Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza,
Jean-François Pinton, Alessandro Vespignani
[5] Crawdad, http://crawdad.cs.dartmouth.edu/
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[5] Crawdad, http://crawdad.cs.dartmouth.edu/
[6] ULOOP, http://uloop.eu
[7] Trust management in ULOOP, http://www.uloop.eu/wp-
content/uploads/2012/10/ULOOP_WP07_Trustmanagement.pdf
[8] Chun et. al. IMC 2008 /Interaction graphs
[9] Andréa Ribeiro and Rute C. Sofia, A Survey on Mobility Models for Wireless
Networks, SITI, University Lusófona, number SITI-TR-11-01, 2011 ,
http://siti.ulusofona.pt/aigaion

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