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  1. SOCIAL-BASED FORWARDING SCHEMES Rance Fredericksen CMPE 257 Wireless Networks

  2. CONCEPT: SOCIAL BASED FORWARDING Follows patterns of social interactions, generally taking after actual human interactions, to understand trends in mobility and communications Forward based on social “popularity” of connected nodes—the more popular the recipient, the more likely the message will get do its destination while maintaining low overhead. Forward based on community involvement—if the next hop is in the same social community as the destination, it is likely to be closer to the destination than other nodes. Store, Forward, Store—if message is forwarded to another node in the community, a local copy is stored in the case the destination contacts the source.

  3. SOCIAL METRICS Centrality/Popularity Betweenness – This metric measures the extent to which a node lies on the shortest path linking other nodes (how often it lies “between” multiple nodes creating a shortest path. This can be measured dynamically by counting the number of unique contacts a node has over time intervals. Group Membership This metric is simply a comparison of nodes and group affiliations. In human interaction, this is analogous to hobbies, clubs, etc, where like-minded people are likely to meet and socialize at length. This is implemented by having each node contain a social profile (Facebook?)

  4. ALGORITHMS PROPOSED WORK Social-Greedy I Social-Greedy II Social-Greedy III Maximum Expected Social Welfare LABEL BUBBLE RAP

  5. PERFORMANCE METRICS Delivery Ratio This is the ratio of number of nodes that have received deliveries to the total number of nodes. This metric is only valuable if sources and destinations are chosen randomly. Average Actually Delay The average delay for all the delivered destinations to receive their data. Average Cost The average number of relays (hops) used during the deliveries of all sources to all destinations.

  6. Social-Greedy I. If node u has a message for the destination v and encounters node w (who is “socially closer” to v than u), u hands off the message to w, but does not remove the local copy. II. When node u (with message destined for v) encounters node w at time t0, u hands off message to w ( if w is “socially closer” to v than u). At any time after t0, u can only pass the message to an encountered node if the encountered node is “socially closer” to v than w. III. Same as I, only removes the message from storage.

  7. BUBBLE Rap Assumptions: Each node is a member of at least one group Each node contains a global and local ranking (local to their community/group) Foreach ( encounteredNode ) If ( this.label == destination.label ) If ( encounteredNode.label == destination.label && encounteredNode.localrank > this.localrank ) encounteredNode.takeMessage() else If ( encounteredNode.label == destination.label || encounteredNode.globalrank > this.globalrank ) encounteredNode.takeMessage()

  8. Simulation in NS-2 Social-Greedy (I,II,III) Node Mobility Social Profile Parameters (socialRank,socialNetwork) Node Mobility Based on Social Profile Parameters (Not random) BUBBLE RAP Node Mobility Varying Communities/Memberships Random Node Social Rankings Dynamically changing Communities/Memberships

  9. Simulation in NS2 Challenges Social-Greedy (I,II,III) Node Mobility Social Profile Parameters (socialRank,socialNetwork) Node Mobility Based on Social Profile Parameters (Not random) BUBBLE RAP Node Mobility Varying Communities/Memberships Random Node Social Rankings Dynamically changing Communities/Memberships

  10. Simulation in NS-2 Position, Social Rank and Network Affiliation set randomly Set up a routing agent in tcl/lib/ns-lib.tcl Periodic beacons traverse the network to create/update route tables and social information (socialRank and socialNetwork) For { set I } { I < $val(nn) } { incr I} { $ns at 0.00 "[$mnode_(0) set ragent_] sink" } rt_lookup() function in SGRP Agent consults the route table and chooses the most applicable route to which to forward based on community/social data

  11. ANTICIPATED ERROR Mobility Nodes are moving at random-the purpose of the social-based approach is to assume non-random mobility, in terms of communities and grouping/mobility based on social likenesses Popularity Popularity in this model is equal to the degree of the node. In the paper on BUBBLE Rap, the authors suggest this is not the best good measure of popularity, and an average over a time interval is a better approach of simulating a popularity metric

  12. EXPECTED CONCLUSIONS The Social-Greedy (I/II) algorithm should improve delivery ratio via the store, forward, store approach, possibly showing dropped stored packets at the intermediate nodes (unless we presume infinite storage) In theory, the social-greedy (III) algorithm of forwarding messages based on belonging to the same socialNetwork group will have no positive effect (because the group memberships were chosen at random) unless I set a static map and group nodes by community organization. More to follow hopefully...

  13. EXPECTED CONCLUSIONS • The BUBBLE Rap algorithm, that forwards based on between-ness averaged over time, however, will have a positive effect on delivery ratio, at the expense of possible congestion at the “popular” nodes. • If node mobility can be tested (in the timeframe), I would like to see how these two algorithms stand up to each other for delivery ratio and average cost

  14. REFERENCES Jahanbakhsh, Kazem. Shoja, Gholamali C. King, Valerie. Social-Greedy: A Socially-Based Greedy Routing Algorithm for Delay Tolerant Networks. MobiOpp '10 February 2010. Lu, Mingming. Wu, Jie. Social Welfare Based Routing in Ad Hoc Networks. NSF grant ANI 0073736 Wei Gao, Qinghua Li. Bo, Zhao. Cao, Guohong. Multicasting in Delay Tolerant Networks: A Social Network Perspective. MobiHoc '09 May 2009. Hui, Pan. Crowcroft, Jon. Eiko, Yoneki. BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks. MobiHoc '08 May 2008.