Properties and Impact of Vicinity in Mobile Opportunistic Networks - PhD Defense Slides.

1. Tiphaine Phe-Neau
Properties and Impact of Vicinity in
Mobile Opportunistic Networks
André-Luc BEYLOT Reviewer Professeur, ENSEEIHT
Emmanuel LOCHIN Reviewer Professeur, ISAE
Aline CARNEIROVIANA Examiner Chargée de Recherche HDR, INRIA
Vania CONAN Examiner Directeur de recherche,Thales
Anne FLADENMULLER Examiner Maître de Conférence, UPMC
Vincent GAUTHIER Examiner Maître de Conférence,Télécom SudParis
Marcelo DIAS DE AMORIM Advisor Directeur de recherche, CNRS & UPMC

2. 2
People = Network

3. Disruption-tolerant (DTN) or
opportunistic networks
3

4. A DTN scenario
4
t = t1 t = t2 t = t3
A A
A
B
B
B
C
C
C
D
D
D

5. A motivating example
5
A
B
C D
E
F
G
nodes in contact
with A
Intercontact = Contact

6. A motivating example
6
The binary intercontact vision
is NOT enough.
A
B
C D
E
F
G
nodes in contact
with A
A
B
C D
E
F
G
nodes in contact
unreachable
nodes
nodes reachable
by an end-to-end path

7. Positionning
 MANET-DTN mixed approach
– HYMAD from Whitbeck et al.
– Mixed approach by Ott et al.
– Island-hopping in VANET by Sarafijanovic-Djukic et
al.
 Similar vision in satellite networks
– « A DTN routing scheme for LEO satellites topology »
by Diana et al.
7

8. Motivation
Proximity knowledge is helpful
A
B
C D
E
F
G
A
B
C
D
E
F
G
t0 t1
8

9. Contributions
I. Uncovering Vicinity Properties of
Intercontacts in DTNs
II. Digging into the Vicinity Dynamics of
Mobile Opportunistic Networks
III. Predicting Vicinity Dynamics
9

10. Datasets
10
Infocom05
Rollernet
Unimi
Random Trip
Community
Sigcomm09

11. Uncovering Vicinity Properties of
Intercontacts in DTNs
1st contribution
Tiphaine Phe-Neau, Marcelo Dias de Amorim, and Vania Conan,
“Fine-Grained Intercontact Characterization in Disruption-Tolerant Networks,”
IEEE ISCC, Kerkyra, Greece, June 2011.
Tiphaine Phe-Neau, Marcelo Dias de Amorim, and Vania Conan,
“Vicinity-based DTN Characterization,”
ACM MobiOpp, Zurich, Switzerland, March 2012.
Tiphaine Phe-Neau, Marcelo Dias de Amorim, and Vania Conan,
“The Strength of Vicinity Annexation in Opportunistic Networking,”
IEEE NetSciCom, Torino, Italy, April 2013.

12. An interesting observation with RT
12
0
0.2
0.4
0.6
0.8
1
contact intercontact
Fractionoftime

13. 0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7 8 9 ∞
Fractionoftime
Number of hops
An interesting observation with RT
13
The binary assertion limits the
information from the network

14. Sociostructure: Infocom05
14
Most of end-to-end transmission possibilities
come from 2-hop paths
0
100
200
300
400
500
75000 85000 95000 105000 115000
Numberofconnectedpairsover1640
Time t (seconds)
4+
3
2
contact

15. The notion of k-vicinity
 κ-vicinity :
The κ-vicinity of node i is the set of all nodes
with shortest paths of length at most κ hops from i.
15
i
1-vicinity

16. The notion of k-vicinity
 κ-vicinity :
The κ-vicinity of node i is the set of all nodes
with shortest paths of length at most κ hops from i.
16
2-vicinity
i

17. k-contact and k-intercontact
 κ-contact : Two nodes are in κ-contact when they dwell
within each other’s κ-vicinity, with κ ∈ N∗.
 κ-intercontact : Two nodes are in κ-intercontact while
they do not belong to each other’s κ-vicinity.
17
i
j
j
j leaves i’s κ-vicinity
(κ-intercontact starts)
κ-vicinity (for κ = 2) j returns to i’s κ-vicinity
(end of κ-intercontact time)
i

18. k-intercontact characterization:
Infocom05
18
2+-intercontact follow the same properties as
binary intercontact. Same knee point.
0.001
0.01
0.1
1
1 10 100 1000 10000
P[κ-intercontact>t]
Time t (seconds)
Contact
2-interc.
3-interc.
4-interc.
5-interc.
6+-interc.
Interc.

19. k-contact characterization: Infocom05
19
Logical: larger k values,
longer average k-contact intervals.
0.001
0.01
0.1
1
1 10 100 1000 10000
P[κ-contact>t]
Time t (seconds)
Contact
Contact
2-contact
2-contact
3-contact
4-contact
5+-contact
i

20. k-contact characterization: Infocom05
20
Logical: larger k values,
longer average k-contact intervals.
0.001
0.01
0.1
1
1 10 100 1000 10000
P[κ-contact>t]
Time t (seconds)
Contact
Contact
2-contact
2-contact
3-contact
4-contact
5+-contact
ii

21. k-contact characterization: Unimi
21
Unexpected: larger k values,
shorter average k-contact intervals
Why?

22. Density-related behavior
22
 Two different behaviors
– Dense: more longer k-contact intervals (Infocom05)
– Light: more shorter k-contact intervals (Unimi)
dense light

23. The power of vicinity annexation
 The WAIT protocol
– Delays transmission until direct contact
 Metric
– Waiting delay: the time between message creation and
destination detection
 Performances
– Generation of 10 messages at random times
23

24. WAIT protocol
24
i!

25. WAIT protocol + 2-vicinity
25
i!

26. The power of vicinity annexation
26
Considering only 2-contact lowers waiting delays
up to 80%. Beyond k=4 less gains.
0
100
200
300
400
500
RT Community Infocom05 Rollernet
contact
κ = 2
κ = 3
κ = 4
κ = 5
κ = 6
κ = 7
1200
1300
1400
1500
1600
1700
1800
Sigcomm09-d1
2500
3000
3500
4000
4500
5000
5500
6000
Shopping
27500
28000
28500
29000
29500
30000
Unimi
57%
80%
40%
40%
16%
48%
5%
*

27. 1st contribution wrap up
 The binary assertion
covers reality
 k-vicinity and k-contact
show hidden transmission
possibilities
 k-contact have similar
properties to contact
 Using a k={3,4} is enough
Take this
away
27

28. Digging into the Vicinity Dynamics
of Mobile Opportunistic Networks
2nd Contribution
T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, andV. Conan,
“Padrões de Mobilidade deVizinhança em Redes de Contato Intermitente,”
In SBRC, Brasilia, DF, Brazil, May 2013 - Best paper candidate!
T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, andV. Conan,
“ExaminingVicinity Dynamics in Opportunistic Networks,”
In ACM MSWiM, Barcelona, Spain, November 2013.
T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, andV. Conan,
“Padrões de Mobilidade deVizinhança em Redes de Contato Intermitente (extended),”
In RB-RESD, accepted.

29. Vicinity Motion
Pairwise Markov chain process
– States: k+1 states representing pairwise distances (∞,
1...k)
– Transitional probabilities: probability of 2 nodes being
at distance m during step s knowing that they were at
distance n before, m ≠ n
– Asynchronous method
0.10 0.44 0.38 0.24
0.16 0.30 0.311 2 3 4∞
0.35 0.300.25
0.32
0.35 0.42 0.57
29

30. Vicinity patterns
 Birth
– appearance in the κ-vicinity after a period of k-intercontact
 Death
– nodes departure from the κ-vicinity
 Sequential
– the process of drifting closer or further from each other,
when nodes at a distance m move to distance m-1 or m+1
 Erratic
30
0.10 0.44 0.38 0.24
0.16 0.30 0.311 2 3 4∞
0.35 0.300.25
0.32
0.35 0.42 0.57

31. Vicinity patterns: birth
31
Most births happen at states {3,4} not contact
0
20
40
60
80
100
1 2 3 4 5 6 7
%oftotal
Birth State
Rollernet
Unimi
RT
Infocom05
Sigcomm09-d1

32. Vicinity patterns: death
32
Death have quite constant rates
0
20
40
60
80
100
1 2 3 4 5 6 7
%oftotal
Current State
Rollernet
Unimi
Sigcomm09-d1
RT
Infocom05

33. Vicinity patterns: sequential
33
Sequential movements have similar patterns
0
20
40
60
80
100
1 2 3 4 5 6 7
%oftotal
Current State
Rollernet
Unimi
RTInfocom05
Sigcomm09-d1

34. Vicinity patterns: repartition
Most observed movements come from Death
and Sequential patterns
0
20
40
60
80
100
1 2 3 4 5 6 7
%oftotal
Distance
Death
Sequential
Erratic
34

35. Vicinity patterns: take-aways
 Birth
– Monitor k-vicinity up to k = {3,4} hops
– Increases arrival detection to 46% to 72%
 Death
– Rates remain quite constant with larger k values
 Sequential
– A tendency to come closer
– With death, represents up to 90% of observed
movements
35

36. TiGeR: timeline generator
36
 Generator of vicinity behaviors: timelines
 Why? To bootstrap vicinity knowledge
 Mode I (duration friendly), Mode 2 (transition friendly)
1
2
3
4
5
6
7
50 51 52 53 54
Numberofhops
Time (×103 seconds)

37. 0
0.2
0.4
0.6
0.8
1
∞
-1∞
-2∞
-3∞
-4∞
-5∞
-6∞
-71-∞
1-2
1-3
1-4
1-5
1-6
1-7
(From - To) States
Average VM
Mode I
Mode II
TiGeR: evaluation (1/2)
Average transitions do match
37

38. TiGeR: evaluation (1/2)
Average transitions do match
38
0
0.2
0.4
0.6
0.8
1
2-∞2-12-32-42-52-62-73-∞3-13-23-43-53-63-74-∞4-14-24-34-54-64-7
(From - To) States
Average VM
Mode I
Mode II

39. 0
0.2
0.4
0.6
0.8
1
5-∞5-15-25-35-45-65-76-∞6-16-26-36-46-56-77-∞7-17-27-37-47-57-6
(From - To) States
Average VM
Mode I
Mode II
TiGeR: evaluation (1/2)
Average transitions do match
39

40. 0
0.2
0.4
0.6
0.8
1
∞
-1∞
-2∞
-3∞
-4∞
-5∞
-6∞
-71-∞1-21-3
2-∞2-12-32-4
3-∞3-13-23-43-5
4-∞4-14-24-34-5
Transitions
(From - To) States
(30,36)-transitions (Infocom05)
Original
Mode I
Mode II
TiGeR: evaluation (2/2)
40
Pairwise transitions fit as well

41. TiGeR: evaluation (2/2)
41
Pairwise transitions fit as well
0
0.2
0.4
0.6
0.8
1
5-∞
5-4
5-6
6-∞
6-3
6-5
7-∞
7-6
Transitions
(From - To) States
(30,36)-transitions (Infocom05)
Original
Mode I
Mode II

42. 2nd contribution wrap up
 Vicinity Motion models
vicinity behavior under a
chain process
 3 main patterns: birth,
death, and sequential
 The TiGeR generator
provides synthetic
vicinity behaviors
Take this
away
42

43. Predicting Vicinity Dynamics
3rd contribution
A. Tatar, T. Phe-Neau, M. Dias de Amorim, V. Conan, and S. Fdida,
“Beyond Contact Predictions in Mobile Opportunistic Networks,”
submitted to IFIP/IEEE WONS.

44. Synchronous Vicinity Motion (SVM)
Pairwise Markov chain process
– States: k+1 states representing pairwise distances (∞,
1...k)
– Transitional probabilities: probability of 2 nodes being
at distance m during step s knowing that they were at
distance n before
– Synchronous method every τ seconds
0.006 0.22 0.18 0.09
0.15 0.27 0.231 2 3 4∞
0.01 0.010.02
0.13
0.19 0.28 0.36
0.96
0.13
44

45. Vicinity Motion-based
Markovian heuristic
45
Presence vector!
(Transition matrix)n !
Result vector!
for the !
nth step!

46. Vicinity Motion-based
Markovian heuristic
46
[0 1 0]
0,5 0,1 0,4
0,7 0,2 0,1
0,6 0,3 0,1
x!
1
= [0,7 0,2 0,1]
Sf!
Ss!
 Sf = ∞ : first highest probability guess
 Ss = 1 : second highest probability guess
Nodes in state 1!

47. Evaluation
 Is it right?
– nth future steps : n = {1...10}
– Exact Distance (ED): if Sf or Ss = observed value  OK!
– Upper bound Distance (UbD): if Sf or Ss ≥ observed
value  OK!
 Full vs partial knowledge
– SVM over full experiment
– SVM over half experiment, performance test on the
second half
47

48. Complete knowledge heuristic:
Sigcomm09
It is highly efficient because DTN scenarios are
mainly disconnected
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1 2 3 4 5 6 7 8 9 10
Proportionofcorrectguesses
nth
forward step
SVM-full
ED
UbD
48

49. Partial knowledge heuristic:
Sigcomm09
Light decrease prediction accuracy.
Shows how behavior is repetitive.
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1 2 3 4 5 6 7 8 9 10
Proportionofcorrectguesses
nth
forward step
SVM-half
ED
UbD
49

50. 3rd contribution wrap up
 Vicinity Motion embeds
a prediction scheme
 It is highly efficient in
DTN because of their
disconnected behavior
 Useable in the close
futureTake this
away
50

51. The Vicinity Package
for DTN
Transversal contribution

52. Vicinity package details
 The package embeds:
– Vicinity Motion, TiGeR, heuristics, and various tools
 Python, NumPy, and NetworkX
 http://vicinity.lip6.fr
52

53. 53
Take this away

54. 54
Take this away
TheVicinitypackage
http://vicinity.lip6.fr
Vicinity is important in DTN.
k-vicinity up to {3,4} hops
is enough.
Vicinity Motion.
Birth, death, and sequential.
TiGeR generator.
Predictions with theVicinity
Motion-based Heuristic.
Simple with high efficiency.

55. Research directions
 Vicinity usage in routing protocols and
vehicular networks
 Opportunistic networks interoperability
 Predictions in DTNs
55

56. Publications (1/2)
 T. Phe-Neau, M. Dias de Amorim and V. Conan,
“Caractérisation en diptyque de l’intercontact pour les réseaux à connectivité intermittente,”
In Algotel, La Grande Motte, France, May 2012.
 T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, and V. Conan,
“Padrões de Mobilidade de Vizinhança em Redes de Contato Intermitente,”
In SBRC, Brasilia, DF, Brazil, May 2013.
Best paper candidate!
 T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, and V. Conan,
“Padrões de Mobilidade de Vizinhança em Redes de Contato Intermitente,”
In RB-RESD, accepted.
56

57. Publications (2/2)
 T. Phe-Neau, M. Dias de Amorim and V. Conan,
“Fine-Grained Intercontact Characterization in Disruption-Tolerant Networks,”
In IEEE ISCC, Kerkyra, Greece, June 2011.
 T. Phe-Neau, M. Dias de Amorim and V. Conan,
“Vicinity-based DTN Characterization,”
In ACM MobiOpp, Zurich, Switzerland, March 2012.
 T. Phe-Neau, M. Dias de Amorim and V. Conan,
“The Strength of Vicinity Annexation in Opportunistic Networking,”
In IEEE NetSciCom, Torino, Italy, April 2013.
 T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, and V. Conan,
“Examining Vicinity Dynamics in Opportunistic Networks (poster),”
In ACM MSWiM, Barcelona, Spain, November 2013.
57

58. Under review
 T. Phe-Neau, M. E. M. Campista, M. Dias de Amorim, and V. Conan,
“Analyzing and Generating Vicinity Traces,”
submitted to Elsevier Ad Hoc Networks.
 T. Phe-Neau, M. Dias de Amorim, and V. Conan,
“Uncovering Vicinity Properties in Disruption-Tolerant Networks,”
submitted to Elsevier Computer Networks.
 A. Tatar, T. Phe-Neau, M. Dias de Amorim, V. Conan, and S. Fdida,
“Beyond Contact Predictions in Mobile Opportunistic Networks,”
submitted to IFIP/IEEE WONS.
58

59. Questions