putting contacts into context: mobility modeling beyond inter-contact times
DESCRIPTION
MobiHoc 2011TRANSCRIPT
Putting Contacts into Context
Mobility Modeling beyond Inter-Contact Times
Theus HossmannETH Zürich, Switzerland
Thrasyvoulos Spyropoulos
EURECOM, France
Franck Legendre ETH Zürich, 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
RPGMSIMPSSLAWTVCM
CMM
HCMMSWIMGHOST
Known Mobility Properties
3
Individual PropertiesDiurnal & weekly periodicity[Henderson et al MobiCom `04]
Location preference[Tuduce et al Infocom `05]
Power law trip length[Lee et al Infocom `09]
Pairwise PropertiesHeavy 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)]
MASTERED
MASTERED
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]
Structural PropertiesCommunity Structure[Hui et al MobiHoc `08]
Community Connections???
?? Do existing models correctly reflect structural properties ??
Methodology
Mobility Model
??
Synthetic Trace
Contact Graph
Contact Trace
Contact Graph
Community Structure?ModularityCommunity Connections?Bridges
Structural Properties?
Mobility Traces
6
Self-reported “check-ins” (like Foursquare)~ 440’000 users (October 2010)~ 16.7 Mio check-ins to ~ 1.6 Mio spots473 “power users” who check-in at least 5 out of 7 days
Mobility Models
7
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)
time
w12
w13 w35 w67
d
fw(i,j)
wij
Frequency fDuration d
wij (scalar weight)PCA
Community Structure
• Louvain Community Detection Algorithm [Blondel et al `08]
• Heuristic to maximize modularity [Newman PNAS `06]
10
Structural PropertiesCommunity StructureModularity, heterogenous community sizes, etc.
Community Connections
MASTERED
? ? ? ?
? ? ?
Community Connections
• Distribution of community connection among links and nodes
• Implications for networking? (Routing, Energy, Resilience)
• Different mobility processes?
Def: Bridging node u of community Ci: Strong weights to many nodes of community CjDef: Bridging link between u of Ci and v of Cj: Strong weight but neither u nor v is bridging node
Node Spread / Edge Spread
• Example
12
2/5
3/5
TRACES MODELS
Low spread(Bridging Links)
High spread(Bridging Nodes)
Structural PropertiesCommunity StructureCommunity ConnectionsBridging nodes, bridging links
MASTERED
? ? ? ?
? ? ?
FAILED!
3/5
?? 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
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
DART
OutsideHomeLocations
“At home”
Speculation: Small spread edges happen outside community context and locationConfirmed
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
15
DART
GeometricDistribution
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
Evaluation
• Edge spread
• Original propreties
18
Small Spread
✔✔✔✔
✔
✔✔✔✔
✔✔✔
✔✔✔
MODELS TRACES
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
