1. Comp. Lab. Univ. Cambridge
Bubble Rap: Forwarding in Small World DTNs in Ever Decreasing Circles Pan Hui (Ben) Jon Crowcroft Eiko Yoneki
Comp. Lab. Univ. Cambridge

2. Outline Pocket Switched Network Multiple levels human heterogeneity
6/11/2018
Pocket Switched Network
Multiple levels human heterogeneity
Power law inter-contact time distribution
Local community structures
Diversity of centrality in different scales
Four categories of human relationship
Heterogeneous forwarding algorithms
Design space
BUBBLE RAP (centrality meets community)
Approximation and predictability
Decentralized approximation of centrality
Human predictability
Distributed community detection 2

3. Pocket Switched Network
Thank you but you are in the opposite direction!
I can also carry for you!
I have 100M bytes of data, who can carry for me?
Give it to me, I have 1G bytes phone flash.
Reach an access point.
There is one in my pocket…
Internet
Search La Bonheme.mp3 for me
Search La Bonheme.mp3 for me
Search La Bonheme.mp3 for me
Pocket Switched Network 3

4. People Are the Network
(PAN)… 4

5. Understanding multiple levels of heterogeneity
The first goal of this research is to move to a new generation of human mobility models, understanding heterogeneity at multiple levels of detail. 5

6. Experimental Setup iMotes ARM processor Bluetooth radio
64k flash memory
Bluetooth Inquiries
5 seconds every 2 minutes
Log {MAC address, start time, end time} tuple of each contact

7. Experimental devices

8. DataSets

9. PAIR WISE RELATIONSHIP
Duration of the experiment
a contact time
an inter-contact t

10. EXAMPLE: A TYPICAL PAIR
6/11/2018 α
cutoff

11. OTHER EXAMPLES

12. Social Structures Vs Network Structures
6/11/2018
Community structures
Social communities, i.e. affiliations
Topological cohesive groups or modules
Centralities
Social hubs, celebrities and postman
Betweenness, closeness, inference power centrality 12

13. K-clique Community Definition
Union of k-cliques reachable through a series of adjacent k-cliques [Palla et al]
Adjacent k-cliques share k-1 nodes
Members in a community reachable through well- connected well subsets
Examples
2-clique (connected components)
3-clique (overlapping triangles)
Overlapping feature 13

14. K-clique Communities in Cambridge Dataset
6/11/2018 14

15. K-clique Communities in Infocom06 Dataset
6/11/2018
Barcelona Group
Paris Group A
Paris Group B
Lausanne Group
Paris Groups
K=3

16. K-clique Communities in Infocom06 Dataset
Barcelona Group
Paris Group A
Paris Group B
Lausanne Group
Paris Groups
6/11/2018
K=4

17. K-clique Communities in Infocom06 Dataset
6/11/2018
Barcelona Group (Spanish)
Paris Group A (French)
Paris Group B (French)
Italian
K=5

18. Other Community Detection Methods
6/11/2018
Betweenness [Newman04]
Modularity [Newman06]
Information theory[Rosvall06]
Statistical mechanics[Reichardt]
Weighted Network Analysis[Newman05]
Survey Papers[Danon05][Newman04] 18

19. Centrality in Temporal Network
6/11/2018
Large number of unlimited flooding
Uniform sourced and temporal traffic distribution
Number of times on shortest delay deliveries
Analogue to Freeman centrality [Freeman] 19

20. Homogenous Centrality
Reality
Cambridge
Infocom06 HK 20

21. Within Group Centrality Cambridge Dataset
6/11/2018
Group B
Group A 21

22. Within Group Centrality Reality Dataset
6/11/2018
Group A
Group B
Group D
Group C 22

23. Model Node Centrality
6/11/2018
Node centrality should be modelled in different levels of heterogeneity 23

24. Regularity and Familiarity
6/11/2018 I
Correlation Coefficient = II
III IV
Familiarity
I: Community II. Familiar Strangers III. Strangers IV. Friends 24

25. c: 0.6604 c: 0.5817 Infocom05 Infocom06 c: 0.8325 c: 0.7927 Reality HK

26. Heterogeneous Forwarding
6/11/2018
The second goal of this research is to devise efficient forwarding algorithms for PSNs which take advantage of both a priori and learned knowledge of the structure of human mobility. 26

27. Interaction and Forwarding
Third generation human interaction model
Categories of human contact patterns
Clique and community
Popularity/Centrality
Dual natures of mobile network
Social network
Physical network
Benchmark Forwarding strategies
Flooding, Wait, and Multiple-copy-multiple-hop (MCP), PROPHET 27

28. Design Space Human Dimension Network Plane Explicit Social Structure
Bubble
Label
Human Dimension
Structure in Cohesive Group
Clique Label
Network Plane
Rank, Degree
Structure in Degree 28

29. Centrality meets Community
6/11/2018
Population divided into communities
Node has a global and local ranking
Global popular node like a postman, or politician in a city
Local popular node like Prof. David Johnson in SIGMOBILE
BUBBLE 29

30. Global Community Sub community Sub community Destination Ranking
6/11/2018
Destination
Ranking
Subsub community
Sub community
Sub community
Source
Global Community 30

31. Centrality meets Community
6/11/2018
Bubble Reality single group 31

32. Centrality meets Community

33. Making Centrality Practical
6/11/2018
How can each node know its own centrality in decentralised way? How well does past centrality predict the future? 33

34. Approximating Centrality
Total degree, per-6-hour degree
Correlation coefficients, and 34

35. Approximating Centrality
6/11/2018
DEGREE
S-Window
A-Window (Exponential Smoothing) 35

36. Approximating Centrality

37. Predictability of Human Mobility
6/11/2018
Three sessions of Reality dataset
Two sessions using the ranking calculated from the first session
Almost same performance 37

38. Distributed Community Detection
6/11/2018
SIMPLE, K-CLIQUE, MODULARITY
Terminology : Familiar Set (F), Local Community (C)
Update and exchange local information during encounter
Build up Familiar Set and Local Community
CommunityAccept( ), MergeCommunities( ) 38

39. SIMPLE Vi Vi λ
CommunityAccept ( vi)

40. SIMPLE γ
MergeCommunities ( Co, Ci)

41. Results and Evaluations
Data Set
SIMPLE
K-CLIQUE
MODULARITY
Reality
0.79/0.76
0.87
0.82
UCSD
0.47/0.56
0.55
0.40
Cambridge
0.85/0.85
0.85
Complexity
O(n)
O(n2)
O(n4)/O(n2k2)
Newman weighted analysis
Palla et al, k-Clique

42. Distributed BUBBLE RAP (DiBuBB)
Distributed Community Detection
Data Out
Data In
Forwarding Module
Approximating
Centrality
(e.g. C-Window)

43. Conclusion and Future Woks
6/11/2018
Improve forwarding efficient
Data collection of million participants
Community detection of giga-scale (e.g. mobile phone users, social networks)
Characterise human social network
Model human mobility in multi-level details
Mobile social applications
Forwarding in metropolitan scale 43