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Putting Contacts into Context: Mobility Modeling beyond Inter-Contact Times

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Putting Contacts into Context: Mobility Modeling beyond Inter-Contact Times

  1. 1. Putting Contacts into Context Mobility Modeling beyond Inter-Contact Times Theus Hossmann ETH Zürich, Switzerland Thrasyvoulos Spyropoulos EURECOM, France Franck Legendre ETH Zürich, Switzerland
  2. 2. Mobility Modeling <ul><li>Mobile Ad Hoc Network (incl. DTN) research largely based on simulation </li></ul><ul><li>Unrealistic mobility models can lead to wrong conclusions about protocol performance! [Bai et al Infocom `03] </li></ul><ul><li>Many (many, many) good existing models </li></ul><ul><ul><li>Simple vs. Complex </li></ul></ul><ul><ul><li>Location based vs. Social network based </li></ul></ul>[email_address] RPGM SIMPS SLAW TVCM CMM HCMM SWIM GHOST
  3. 3. Known Mobility Properties MASTERED MASTERED [email_address] Individual Properties Diurnal & weekly periodicity [Henderson et al MobiCom `04] Location preference [Tuduce et al Infocom `05] Power law trip length [Lee et al Infocom `09] Pairwise Properties Heavy tailed aggregate inter-contact times (exponential cut -off) [Chaintreau et al Infocom `06] [Karagiannis et al MobiCom `07] [Cai et al MobiCom `07] Individual pairs with various distributions [Leguay et al Autonomics `07)]
  4. 4. Unexplored Mobility Properties <ul><li>What about correlations of more than two nodes? </li></ul><ul><ul><li>Community structure </li></ul></ul><ul><ul><li>Hubs </li></ul></ul><ul><li>Social (Contact) Graph </li></ul><ul><li>Quantify structure </li></ul><ul><li>Protocols </li></ul><ul><ul><li>Simbet [Daly et al MobiHoc `07] </li></ul></ul><ul><ul><li>BubbleRap [Hui et al MobiHoc `08] </li></ul></ul>[email_address] ??? Structural Properties Community Structure [Hui et al MobiHoc `08] Community Connections ?? Do existing models correctly reflect structural properties ??
  5. 5. Methodology [email_address] Mobility Model ?? Synthetic Trace Contact Graph Contact Trace Contact Graph Community Structure? Modularity Community Connections? Bridges Structural Properties?
  6. 6. Mobility Traces [email_address] Self-reported “check-ins” (like Foursquare) ~ 440’000 users (October 2010) ~ 16.7 Mio check-ins to ~ 1.6 Mio spots 473 “power users” who check-in at least 5 out of 7 days
  7. 7. Mobility Models [email_address] TVCM (location based) [Spyropoulos et al ToN `09] HCMM (social network based) [Boldrini et al Comp. Comm. `10] SLAW (location based) [Lee et al Infocom `09]
  8. 8. The Contact Graph <ul><li>Represent contacts as Weighted Graph G(V,W) </li></ul><ul><li>How to assess the tie strength? </li></ul><ul><ul><li>Contact frequency (many contacts -> short delay) </li></ul></ul><ul><ul><li>Contact duration (long contacts -> high bandwidth) </li></ul></ul>[email_address] time w 12 w 13 w 35 w 67 d f w (i,j) w ij Frequency f Duration d w ij (scalar weight) PCA
  9. 9. The Contact Graph [email_address]
  10. 10. Community Structure <ul><li>Louvain Community Detection Algorithm [Blondel et al `08] </li></ul><ul><li>Heuristic to maximize modularity [Newman PNAS `06] </li></ul>MASTERED ? ? ? ? ? ? ? [email_address] Structural Properties Community Structure Modularity, heterogenous community sizes, etc. Community Connections
  11. 11. Community Connections <ul><li>Distribution of community connection among links and nodes </li></ul><ul><li>Implications for networking? (Routing, Energy, Resilience) </li></ul><ul><li>Different mobility processes? </li></ul>[email_address] Def: Bridging node u of community C i : Strong weights to many nodes of community C j Def: Bridging link between u of C i and v of C j : Strong weight but neither u nor v is bridging node
  12. 12. Node Spread / Edge Spread <ul><li>Example </li></ul>2/5 3/5 MASTERED ? ? ? ? ? ? ? FAILED! [email_address] 3/5 TRACES MODELS Low spread (Bridging Links) High spread (Bridging Nodes) Structural Properties Community Structure Community Connections Bridging nodes, bridging links ?? Why ??
  13. 13. Context of Contacts <ul><li>Difference in mobility processes (speculation) </li></ul><ul><ul><li>Mobility Models: Nodes visit other communities </li></ul></ul><ul><ul><li>Reality/Traces: Nodes of different communities meet outside the context and location of their communities </li></ul></ul><ul><li>Infer context of contacts in traces </li></ul><ul><ul><li>GOW: From spot category </li></ul></ul><ul><ul><li>DART: From AP locations </li></ul></ul>[email_address] Context INTRA- Community INTER- Community Academic 4.9% 32% Administration 1.4% 1.2% Library 0.12% 11% Residential 90% 45% Social 0.5% 3.5% Athletic 2.7% 6.5%
  14. 14. Location of Contacts <ul><li>Def: Location profile : Smallest set of locations which contains 90% of intra-community contacts </li></ul>[email_address] Confirmed DART Outside Home Locations “ At home” Speculation: Small spread edges happen outside community context and location
  15. 15. Synchronization of Contacts <ul><li>Do nodes visit the same “social” location synchronously? </li></ul><ul><ul><li>Overlap (Jaccard Index) of time spent at social locations </li></ul></ul><ul><ul><li>Null model: Independent visits (same number, same durations) </li></ul></ul><ul><ul><li>Result: many synchronized visits </li></ul></ul><ul><li>Do only pairs visit social locations or larger cliques? </li></ul><ul><ul><li>Detecting cliques of synchronized nodes </li></ul></ul>[email_address] DART Geometric Distribution
  16. 16. Social Overlay <ul><li>Hypergraph G(N, E) </li></ul><ul><ul><li>Arbitrary number of nodes per Hyperedge </li></ul></ul><ul><ul><li>Represent group behavior </li></ul></ul><ul><li>Calibration from measurements </li></ul><ul><ul><li># Nodes per edge </li></ul></ul><ul><ul><li># Edges per node </li></ul></ul><ul><li>Adapted configuration model </li></ul><ul><li>Drive different mobility models </li></ul><ul><ul><li>TVCM:SO </li></ul></ul><ul><ul><li>HCMM:SO </li></ul></ul>[email_address]
  17. 17. TVCM:SO [email_address]
  18. 18. Evaluation <ul><li>Edge spread </li></ul><ul><li>Original propreties </li></ul>[email_address] ✔✔✔✔✔ ✔✔✔✔✔ ✔✔✔✔✔ MODELS TRACES Small Spread
  19. 19. Conclusion <ul><li>Traces: Bridging links („narrow“ community connections) </li></ul><ul><li>Models: Bridging nodes („broad“ community connections) </li></ul><ul><li>Trace analysis shows </li></ul><ul><ul><li>Inter-community contacts happening outside the locations of communities cause bridging links </li></ul></ul><ul><ul><li>Synchronized “social” meetings of two or more nodes </li></ul></ul><ul><li>Social Overlay </li></ul><ul><ul><li>TVCM:SO, HCMM:SO </li></ul></ul><ul><ul><li>Create bridging links </li></ul></ul><ul><ul><li>Maintain original model properties </li></ul></ul>[email_address]
  20. 20. Thank you! [email_address]

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