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

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MobiHoc 2011

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  • Putting Contacts into Context Mobility Modeling beyond Inter-Contact Times Theus Hossmann ETH Zrich, Switzerland Thrasyvoulos Spyropoulos EURECOM, France Franck Legendre ETH Zrich, Switzerland
  • Mobility Modeling
    • Mobile Ad Hoc Network (incl. DTN) research largely based on simulation
    • Unrealistic mobility models can lead to wrong conclusions about protocol performance! [Bai et al Infocom `03]
    • Many (many, many) good existing models
      • Simple vs. Complex
      • Location based vs. Social network based
    [email_address] RPGM SIMPS SLAW TVCM CMM HCMM SWIM GHOST
  • 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)]
  • Unexplored Mobility Properties
    • What about correlations of more than two nodes?
      • Community structure
      • Hubs
    • Social (Contact) Graph
    • Quantify structure
    • Protocols
      • Simbet [Daly et al MobiHoc `07]
      • BubbleRap [Hui et al MobiHoc `08]
    [email_address] ??? Structural Properties Community Structure [Hui et al MobiHoc `08] Community Connections ?? Do existing models correctly reflect structural properties ??
  • Methodology [email_address] Mobility Model ?? Synthetic Trace Contact Graph Contact Trace Contact Graph Community Structure? Modularity Community Connections? Bridges Structural Properties?
  • Mobility Traces [email_address] Self-reported check-ins (like Foursquare) ~ 440000 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
  • 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]
  • The Contact Graph
    • Represent contacts as Weighted Graph G(V,W)
    • How to assess the tie strength?
      • Contact frequency (many contacts -> short delay)
      • Contact duration (long contacts -> high bandwidth)
    [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
  • The Contact Graph [email_address]
  • Community Structure
    • Louvain Community Detection Algorithm [Blondel et al `08]
    • Heuristic to maximize modularity [Newman PNAS `06]
    MASTERED ? ? ? ? ? ? ? [email_address] Structural Properties Community Structure Modularity, heterogenous community sizes, etc. Community Connections
  • Community Connections
    • Distribution of community connection among links and nodes
    • Implications for networking? (Routing, Energy, Resilience)
    • Different mobility processes?
    [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
  • Node Spread / Edge Spread
    • Example
    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 ??
  • Context of Contacts
    • Difference in mobility processes (speculation)
      • Mobility Models: Nodes visit other communities
      • Reality/Traces: Nodes of different communities meet outside the context and location of their communities
    • Infer context of contacts in traces
      • GOW: From spot category
      • DART: From AP locations
    [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%
  • Location of Contacts
    • Def: Location profile : Smallest set of locations which contains 90% of intra-community contacts
    [email_address] Confirmed DART Outside Home Locations At home Speculation: Small spread edges happen outside community context and location
  • Synchronization of Contacts
    • Do nodes visit the same social location synchronously?
      • Overlap (Jaccard Index) of time spent at social locations
      • Null model: Independent visits (same number, same durations)
      • Result: many synchronized visits
    • Do only pairs visit social locations or larger cliques?
      • Detecting cliques of synchronized nodes
    [email_address] DART Geometric Distribution
  • Social Overlay
    • Hypergraph G(N, E)
      • Arbitrary number of nodes per Hyperedge
      • Represent group behavior
    • Calibration from measurements
      • # Nodes per edge
      • # Edges per node
    • Adapted configuration model
    • Drive different mobility models
      • TVCM:SO
      • HCMM:SO
    [email_address]
  • TVCM:SO [email_address]
  • Evaluation
    • Edge spread
    • Original propreties
    [email_address] MODELS TRACES Small Spread
  • Conclusion
    • Traces: Bridging links (narrow community connections)
    • Models: Bridging nodes (broad community connections)
    • Trace analysis shows
      • Inter-community contacts happening outside the locations of communities cause bridging links
      • Synchronized social meetings of two or more nodes
    • Social Overlay
      • TVCM:SO, HCMM:SO
      • Create bridging links
      • Maintain original model properties
    [email_address]
  • Thank you! [email_address]