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Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs

This workshop paper explores the use of social network analysis techniques for routing in disconnected delay-tolerant mobile ad hoc networks. It discusses various centrality metrics, similarity measures, and the SimBet routing algorithm. The paper concludes that these metrics can effectively capture network social structure and achieve comparable delivery performance with lower overhead compared to epidemic routing.

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Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs

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

  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 • Milgram’s ‘Small world’ • Hsu and Helmy’s 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 s2 s4 w6 w8 w7 w4 w9 w2 w3 i w5 w1 s1 i3 • Analysis of a node’s local neighbourhood Betweenness Centrality Degree Centrality Closeness Centrality

  7. Egocentric Betweenness Correlation w6 w8 w7 s4 w9 s2 i3 w4 w2 w3 i1 w5 w1 s1 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 A B Add node encounters Update betweenness Update similarity Add node encounters Update betweenness Update similarity Deliver msgs Exchange encounters HELLO Exchange messages Exchange Summary Vector Compare SimBet Utility

  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 0 1 1 1 1 * * * * * 1 0 1 1 0 * * * * 3 1 1 0 1 1 * * * * * * * * * * 1 1 1 0 1 1 0 1 1 0 * * * * * w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w82[1-w8] = w8 = [Everett and Borgatti 2005]

  11. Similarity Utility Calculation 0 1 1 1 1 0 1 1 1 1 w5 w5 1 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w8 w6 w7 w9 s4 w8 w8 = = • Indirect Node contacts learnt during a node encounter is represented in and additional matrix • Node similarity is a simple count of common neighbours

  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 Pearson’s 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…

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