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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 2 Outline  Introduction  Social Properties  Social-based Routing  Conclusion

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 5 Outline  Introduction  Social Properties  Social-based Routing  Conclusion

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 8 Social Properties: Community  Community ：  A group of interacting users  Devices within same community have higher chances encounter each other

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 9 Social Properties: Community  Community Detection Methods ：  Minimum-cut method  Hierarchical clustering  Girvan-Newman algorithm  Modularity maximization  The Louvain method  Clique based method

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 14 Outline  Introduction  Social Properties  Social-based Routing  Conclusion

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 18 Bubble Rap Forwarding  Bubble Rap Forwarding [Hui, Crowcroft, Yonek, 2008] global centrality: across whole network local centrality: within local community

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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.

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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  λ i,j is average contact rate of Possion process for node pair (i,j)

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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 I u  Cosine similarity:  If cosine similarity betwween encounted node and destination is larger than a threshold, forward message  Stateless & Scalable

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 26 User-Centric Data Disseination  User-Centric Data Disseination [Gao & Cao, 2012]  Interest profile of node i:  P ij : prob. of user i interested in jth keyword  A data item is described by  the importance of k i  Probability of node i interested in data D:

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 27 User-Centric Data Disseination  Centrality value of node i for data d k at t≤T k :  T k : Time constraint of data d k  N i : Set of nodes whose information is maintained by i  C ij (T k -t): Prob. of node i can forward d k to j within T k -t  C i (k) (t): Expected number of interesters i can encounter during T k -t

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 28 User-Centric Data Disseination  Node i is selected as relay for data d k only if:  N R k (t): The number of selected relays for d K at time t  N I k (t): The number of interesters will receive d k by T k, estimated at time t

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 30 Summary Social-based routing uses one or multiple social properties to make forwarding decision

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 31 Outline  Introduction  Social Properties  Social-based Routing  Conclusion

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 32 Conclusion  Social-based approaches are promising for OppNets  None of these approaches guarantee perfect routing performance  Performance of routing protocol in OppNets depends heavily on mobility model, environment, node density, social structure, and many other facts  Universal routing solution for all Oppnet application scenarios is extremely hard  For particular Oppnet applications, specific routing protocols and mobility/social models are needed

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 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? ...

© Y. Zhu and Y. University of North Carolina at Charlotte, USA 34 Thanks for your attention!