1 Workshop on Online Social Networks Microsoft Research Cambridge Elizabeth Daly and Mads Haahr Distributed Systems Group, Computer Science Department Trinity College, Dublin Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs

2 Introduction and Motivation Routing in a disconnected network graph –Traditional MANET Routing protocols fail –Store-carry-forward model used –Global view of network unavailable and volatile Social Networks –Milgrams Small world –Hsu and Helmys analysis of wireless network

3 Related Work Deterministic –Assumes node movements are deterministic DataMULEs or Message Ferries –Assumes given nodes travel around the network Epidemic –Expensive in terms of resources History or Prediction –Captures direct and indirect social relationships –Problem: What if destination node is unknown to neighbouring nodes

4 Solution Exploit Social Network Analysis Techniques in order to: –Identify bridging ties Centrality –Identify clusters Similarity

5 Centrality Metrics [Freeman 1977,1979] Degree centrality –popular nodes in the network Closeness centrality –the distance of a given node to each node in the network Betweenness centrality –the extent to which a node can facilitate communication to other nodes in the network

6 Ego Network Centrality Measures Analysis of a nodes local neighbourhood s4 w6 w8w7 w9 s2i3 w4 w2 w3iw5 w1s1 Degree Centrality Closeness Centrality Betweenness Centrality

7 Egocentric Betweenness Correlation NodeSociocentric Betweenness Egocentric Betweenness w w20.25 w w w5304 w600 w w80.33 w90.33 s s200 s400 i100 i200 w6 w8w7 s4 w9s2i3 w4 w2 w3i1w5 w1s1 Marsden 2002

8 Similarity Social networks exhibit clustering Increased common neighbours increases probability of a relationship [Newman 2001] Similarity metric may be used to predict future interactions [Liben-Nowell,Kleinberg 2003] Represents similarity of social circles

9 SimBet Routing AB HELLO Deliver msgs Exchange encounters Add node encounters Update betweenness Update similarity Compare SimBet UtilityExchange Summary Vector Add node encounters Update betweenness Update similarity Exchange messages

10 Betweenness Utility Calculation Node contacts represented in symmetric adjacency matrix if there is a contact between i and j otherwise Ego betweenness is given as the sum of the reciprocals of w8 w6 w7 w9 s4 w8 w6 w7 w9 s =w8 * * * * * * * * * 3 * * * * * w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 =w8 2 [1-w8] [Everett and Borgatti 2005]

11 Similarity Utility Calculation Indirect Node contacts learnt during a node encounter is represented in and additional matrix Node similarity is a simple count of common neighbours w8 w6 w7 w9 s4 w8 w6 w7 w9 s =w w5 w8 w6 w7 w9 s4 w8 w6 w7 w9 s =w w5

12 SimBet Utility Calculation Goal: to select node that represents the best trade off across both attributes Combined: where

13 Simulation Setup Trace based simulation using MIT Reality Mining project data set –100 users carrying Nokia 6660 for 9 months –Bluetooth sightings used as opportunity for data exchange Comparison –Epidemic Routing [Vahdat and Becker 2000] –PRoPHET [Lindgren, Doria and Schelén 2004] Scenario 1: Each node generates a single message for all other nodes Scenario 2: Message exchange between least connected nodes

14 MIT Data set Egocentric Betweenness

15 Egocentric Betweenness Correlation Pearsons Correlation

16 Egocentric Betweenness Friendship network Eagle and Pentland Egocentric Betweenness

17 Delivery Performance

18 Average End-To-End Delay

19 Average Number of Hops

20 Total Number of Forwards

21 Delivery Performance between least connected nodes

22 Conclusion Simple metrics for capturing network social structure suitable for disconnected delay-tolerant MANETs –Egocentric Betweenness –CentralitySimilarity Achieves comparable delivery performance compared to Epidemic Routing –But with lower delivery overhead Achieves delivery performance between least connected nodes

23 Questions…