1. Vicinity-based DTN Characterization
Tiphaine Phe-Neau , Marcelo Dias de Amorim , Vania Conan‡
CNRS/LIP6, UPMC Sorbonne Universit´s
e
Thales Communications‡
ACM International Workshop on Mobile Opportunistic Networks (MobiOpp’12)
March 15, 2012
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2. People are the network...
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3. People are the network...
...and often travel in packs.
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4. Motivation
nodes in contact
with A
F
B G
nodes in
“binary intercontact”
A based on the traditional
definition
C D
E
Binary intercontact definition:
Intercontact = Contact
Are all the grey nodes
the same to node A?
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5. The 1-vicinity
i
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6. The 2-vicinity
i
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7. Patterns in the vicinity?
j
i i
2
j
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8. What to expect from my
presentation?
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9. Datasets
Real-life measurements:
Infocom05 is a 12-hour slot w/ 41 iMotes during a conference.
Rollernet has 62 iMotes during a 3-hour Rollerblade tour in Paris.
Unimi measured 48 university members for 19 days.
Synthetical datasets:
RandomTrip simulated 20 nodes during 9 hours [PalC05].
Community emulated 50 nodes during 9 hours [Muso07].
[PalC05] S. Pal Chaudhuri, J.-Y. Le Boudec, and M. Vojnovic. “Perfect Simulations for Random
Trip Mobility Models”. In IEEE INFOCOM, 2005.
[Muso07] M. Musolesi and C. Mascolo. “Designing Mobility Models
based on Social Network Theory”. SIGMOBILE Mob. Comput.
Commun. Rev., 11:59-70, July 2007.
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10. κ-intercontact
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11. 1-intercontact distributions
Unimi
1
P[ 1-intercontact>t ]
0.1
0.01
0.001
1 10 100 1000 10000 1000001e+06
Time t (seconds)
Power law distribution up to a knee point and then
exponential decay (results confirmation of [Karag07]).
[Karag07] T. Karagiannis, J.-Y. Le Boudec, and M. Vojnov´ “Power
ıc.
Law and Exponential Decay of Inter Contact Times between Mobile
Devices” in ACM MOBICOM, 2007.
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12. κ-intercontact distributions
Unimi
1
Interc.
P[ κ-intercontact>t ]
2-interc.
0.1
3-interc.
4-interc.
0.01
5-interc.
6+-interc.
Char. Time
0.001
1 10 100 1000 10000 100000
Time t (seconds)
Similar to 1-intercontact → power law + exponential decay.
Same knee point for every κ-intercontact distribution.
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13. κ-contact
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14. κ-contact - dense distributions
Rollernet
1
6-contact
7+-contact
P[ κ-contact > t ]
Contact
0.1 2-contact
3-contact
0.01
4-contact
5-contact
0.001
1 10 100 1000
Time t (seconds)
Dense distributions = more large κ-contact
intervals.
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15. κ-contact - sparse distributions
Unimi
1
Contact
P[ κ-contact > t ] 0.1
2-contact
3-contact
0.01
4+-contact
0.001
1 10 100 1000 10000 100000
Time t (seconds)
Light distributions = more short κ-contact intervals.
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16. The potential influence of density
dense
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17. The potential influence of density
dense
sparse
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18. How could we use the
κ-vicinity?
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19. κ-vicinity management
Intuitions:
A node is popular → more neighbors,
more neighbors → more κ-contacts possibilities,
⇒ higher κ-contact and lower κ-intercontact durations.
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20. κ-vicinity management
Intuitions:
A node is popular → more neighbors,
more neighbors → more κ-contacts possibilities,
⇒ higher κ-contact and lower κ-intercontact durations.
Definition:
Node i’s κ-vicinity
Node i’s κ-vicinity
density i
i card(Vκ )
Dκ =
τ
Experiment duration
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21. i
Average Dκ in a node’s κ-vicinity
How far should we probe the κ-vicinity?
Community
Infocom05
7 Rollernet
Unimi
6
5
Average Dκ
i
4
3
2
1
1 2 3 4 5 6 7 8+
κ
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22. i
Average Dκ in a node’s κ-vicinity
How far should we probe the κ-vicinity?
Community
Infocom05
7 Rollernet
Unimi
6
5
Average Dκ
i
4
3
2
1
1 2 3 4 5 6 7 8+
κ
Strong increase for κ ≤ 4.
Above κ ≥ 4, limited or null growth.
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23. Implications
Opportunistic protocols design
Density, hubs and, hot places should be leveraged.
κ-contact and κ-intercontact knowledge and patterns
integration.
κ = 4 is enough to benefit from the κ-vicinity.
Mobility models
Some are socially aware [Muso07, Bold10].
They care for colocation, contact patterns which is great.
But they should not leave incidental parameters like κ-vicinity
astray.
[Bold10] C. Boldrini and A. Passarella. “HCMM: Modelling spatial and temporal properties of
human mobility driven by users social relationships”. Computer Communications, 33(9):1056 -
1074, 2010.
[Muso07] M. Musolesi and C. Mascolo. “Designing Mobility Models
based on Social Network Theory”. SIGMOBILE Mob. Comput.
Commun. Rev., 11:59-70, July 2007.
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24. Next steps
κ-vicinity pairwise distribution analyses.
Stochastic modeling of nodes pairwise
movements.
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25. Conclusion
People may maintain a constant vicinity:
→ κ-vicinity, κ-contact and κ-intercontact.
κ-contact distributions have 2 patterns: dense
and sparse.
Sensing a node’s 4-vicinity may be enough to
benefit from the vicinity patterns.
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26. Interrogations? Questions? Fragen? ρωτ ηση?
Perguntas? Preguntas? Domande?
Contact: tiphaine.phe-neau@lip6.fr
Website: www.phe-neau.com
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