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Chapter 1: Social-based Routing Protocols in Opportunistic Networks

Routing in Opportunistic Networks. Chapter 1: Social-based Routing Protocols in Opportunistic Networks. Ying Zhu and Yu Wang University of North Carolina at Charlotte. Outline. Introduction Social Properties Social-based Routing Conclusion. Routing in Opportunistic Networks.

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Chapter 1: Social-based Routing Protocols in Opportunistic Networks

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  1. Routing in Opportunistic Networks Chapter 1: Social-based Routing Protocols in Opportunistic Networks Ying Zhu and Yu Wang University of North Carolina at Charlotte

  2. Outline • Introduction • Social Properties • Social-based Routing • Conclusion

  3. Routing in Opportunistic Networks • Intermittent Connectivity in OppNets • “Store and Forward“ No connection available？ Store & carry the data Make forwarding decision based on certain routing strategy

  4. Routing in Opportunistic Networks • OppNet Routing Strategies： • Based on mobility pattern • Unpredictable mobility • High overhead • Based on social characteristics • Long term • Less volatile • Low overhead • This chapter focuses on social-based routing

  5. Outline • Introduction • Social Properties • Social-based Routing • Conclusion

  6. Social Graph • Social Graph： • A global mapping of everybody and how they are related • Vertices: people • Edges: social ties • Different social relationships, i.e. friends, co-workers • Intuitive source for many social metrics • Sometime is hard to directly obtain

  7. Contact Graph • Contact Graph： • Recording contacts seen in the past • Vertices: Mobile nodes • which are carried by people • Edges: One or more past meetings • Indicate node’s relationships in OppNets • People with close relationships tend to meet more often, more regular and with longer duration

  8. Social Properties: Community • Community： • A group of interacting users • Devices within same community have higher chances encounter each other

  9. Social Properties: Community • Community Detection Methods： • Minimum-cut method • Hierarchical clustering • Girvan-Newman algorithm • Modularity maximization • The Louvain method • Clique based method

  10. Social Properties: Centrality • Centrality： • Social importance of its represented node in a social network • Degree centrality • The number of links upon a given node • Betweenness centrality • The number of shortest paths passing via given node • Closeness centrality • An inverse of node’s average shortest distance to all other nodes

  11. Social Properties: Centrality • Degree centrality a->3, b->4, others->1 • Betweenness centrality a->18, b->24. others->0 • Closeness centrality a->2/3, b->3/4, c/d/e->6/13,f/g->3/7

  12. Social Properties: Similarity • Similarity： • A measurement on degree of separation • A simple way to define: Number of common neighbors between nodes in social/contact graph Similarity between a and c is 1 c and e is 3

  13. Social Properties: Friendship • Friendship： • Close personal/contact relationships • In OppNets, friends may have: • Long-lasting contacts • Regular contacts • Common interests • Similar actions • Different ways to define

  14. Outline • Introduction • Social Properties • Social-based Routing • Conclusion

  15. Label Routing • Label Routing[Hui& Crowcroft, 2007] • Small label for each node (its social group) • Only forward messages to nodes which has same label with destination or directly to destination • Requires little information • Easy to implement • Long delay

  16. SimBet Routing • SimBet Routing [Daly& Haahr, 2007] • SimBet utility, a weighted combination of betweenness centrality and similarity • Forward message to node with larger SimBet utility with destination

  17. SimBet Routing • SimBet uses local centrality & betweenness to reduce overhead • may lead to inaccurate “bridge” identification Node u will not pass message to node a considers local SimBet utility

  18. Bubble Rap Forwarding • Bubble Rap Forwarding [Hui, Crowcroft, Yonek, 2008] global centrality: across whole network local centrality: within local community

  19. Bubble Rap Forwarding • Bubble-up on global centrality • Forward message to the node with higher global centrality • Until it reaches a node belongs to the same local community as destination • Bubble-up on local centrality • Use nodes within destination’s community as relays • Choose the ones with higher local centrality • When destination only belongs to communities whose members are all with low global centrality, BubbleRap may fail.

  20. Social-Based Multicasting • Social Based Multicasting [Gao, et al. 2009] • Cumulative contact probability of node i: • N is the total number of nodes in network • T is the total time period • λi,j is average contact rate of Possion process for node pair (i,j)

  21. Social-Based Multicasting • Single-data multicast • Destinations are uniformly distributed • All nodes need to be contacted within T • Select minimal number of relay nodes • Using cumulative contact probabilities • Considered as unified knapsack problem • Multi-data multicast • Relay and destination in different communities: Forwarding via gateways (G1, G2) • Relay and destination in same community：Same as single-data multicast

  22. Homophily Based Data Diffusion • Homophily Based Data Diffusion [Zhang & Zhao, 2009] • When contact time too short or buffer is limited,need consider data propagation orders • Friends usually share more common interests than strangers (Friendship is user defined) • Diffuses the most similar data of their common interests to friend first • Diffusing start from the data most different from their common interests to strangers

  23. Friendship Based Routing • Friendship Based Routing [Bulut & Szymanski, 2010] • Social pressures metric(SPM) between i and j: • f(t) denotes the remaining time to the first encounter of node i and j after time t • T denotes the total time period • Describes the average forwarding delay

  24. Friendship Based Routing • Link quality: An inverse of SPM • Bigger link quality represents closer friendship • Construct friendship community based on link quality • Forward message to node in the same friendship community with destination • Forward message to node with stronger friendship to destination than current node

  25. Social-aware and Stateless Routing • Social-aware and Stateless Routing (Sane) [Mei et al., 2011] • People with similar interests tend to meet more often • Interest profile for node u: K-dimensional vector Iu • Cosine similarity: • If cosine similarity betwween encounted node and destination is larger than a threshold, forward message • Stateless & Scalable

  26. User-Centric Data Disseination • User-Centric Data Disseination [Gao & Cao, 2012] • Interest profile of node i: • Pij : prob. of user i interested in jth keyword • A data item is described by • the importance of ki • Probability of node i interested in data D:

  27. User-Centric Data Disseination • Centrality value of node i for data dk at t≤Tk: • Tk: Time constraint of data dk • Ni: Set of nodes whose information is maintained by i • Cij(Tk-t): Prob. of node i can forward dk to j within Tk-t • Ci(k)(t): Expected number of interesters i can encounter during Tk-t

  28. User-Centric Data Disseination • Node i is selected as relay for data dk only if: • NRk(t): The number of selected relays for dK at time t • NIk(t): The number of interesters will receive dk by Tk, estimated at time t

  29. Sociability-Based Routing • Sociability Based Routing[Fabbri and Verdone, 2011] • Sociability indicator: • Evaluate node’s forwarding ability • The node’s number of encounters with all other nodes in the network over a period T • Nodes which frequently encounter many different nodes have high degree of sociability • Good forwarder: Nodes with high sociability • Forward packet to the most sociable node

  30. Summary Social-based routing uses one or multiple social properties to make forwarding decision

  31. Outline • Introduction • Social Properties • Social-based Routing • Conclusion

  32. Conclusion • Social-based approaches are promising for OppNets • None of these approaches guarantee perfect routing performance • Performance of routing protocol in OppNetsdepends heavily on mobility model, environment, node density, social structure, and many other facts • Universalrouting solution for all Oppnet application scenarios is extremely hard • For particular Oppnet applications, specific routing protocols and mobility/social models are needed

  33. Future Directions • Are there new social characteristics better than existing ones? • How to combine multiple social properties efficiently? • How to model and extract accurate social characteristics in dynamic OppNets? • How to combine social-based approaches with other type of routing stratigies? • ...

  34. Thanks for your attention!

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