Presentation of my Master's Thesis.

1. Università degli studi “La Sapienza” di Roma
Master’s Thesis in Computer Science
Supervisor: Candidate:
Prof. Luca Becchetti Umberto Griffo
Matr. 799201
Assistant Supervisor:
Prof. Leonardo Querzoni

2. Goals
 Validation of mobility models in social contexts
 Random Waypoint
 Truncated Lévy Walk
 Software development for efficient simulation of
algorithms on Evolving Dynamic Network
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3. Mobility Models
Truncated Lévy Walk Random Waypoint
The human walks are Mobile nodes follow random
approximated with the Lévy directions with speed chosen
walks. randomly. The destination, speed
and direction changes when waiting
time is ended. 3

4. Validation Framework
Mobility Contact
models Synthetic Graph
Models Statistical
contact
• RWP Execution • Aggregated analysis
traces
• TLW • Dynamic
Validation
Real-world Contact
social Real Graph
contacts
Statistical
contact
traces • Aggregated analysis
• SocialDIS
• Dynamic
• MACRO
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5. Social Experiments with RFID Platform
SocialDIS NeonMACRO
# partecipants: 116 # partecipants: 114
# duration: 4 days # duration: 3h
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6. Software architecture - new Gephi modules
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7. Contributions
 Gathering and processing of user traces gathered by social
experiment NeonMACRO
 Definition of new efficient format to represent Dynamic
Contact network named DNF (Dynamic Network Format)
 Development of new modules on Gephi simulation
Platform:
 implementation of a Contact Graph importer
 implementation of an efficient dinamicity simulator
(FastUtils)
 implementation of Mobility Models (RWP and TLW)
 implementation of algorithms to compute metrics and
statistical indices
 Extensive experimental analysis of mobility models
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8. Experimental analysis
 On aggregated Contact Graph
 Weighted Clustering Coefficient
 Strength
 Density
 Modularity
 On Evolving Network
 Inter-Intra contact times
 Flooding time
 Distance from stationarity
 Spatial/Time correlations
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9. Main findings (1/9)
Dataset # Edges Average Average Graph
 Social experiments: degree strength density
MACRO 132 2,316 0,004 0,02
contacts mostly with TLW 5394 94,63 1 0,83
“friends” seldom with RWP 6120 107,368 1 0,95
“strangers” Dataset Average Clustering Average Weighted
 Mobility models: all-to- Coefficient Clustering
Coefficient
all like contacts MACRO 0,378 0,237
TLW 0,848 0,853
RWP 0,951 0,951
Dataset Average Intra- Average Inter- # #
contact Time contact Time Contact Interval
(seconds) (seconds)
MACRO 1,7 51,2 1.325 966
TLW 20,7 645,8 28.187 325
RWP 32,7 1.619,3 19.117 246
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10. Main findings (2/9)
 The models:
 don’t capture the
friendly ties
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11. Main findings (3/9)
 The models:
 don’t capture the
friendly ties
 overestimate the speed
of flooding
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12. Main findings (4/9)
 The mobility models
overestimate temporal
correlations
 The existence
probability of a contact
results to be
approximately
stationary
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13. Main findings (5/9)
 The mobility models
overestimate temporal
correlations
 The existence
probability of a contact
results to be
approximately
stationary
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14. Main findings (6/9)
MACRO RWP
 The nodes moving by mobilty models present spatial
correlations that do not agree with experimental
observation 14

15. Main findings (7/9)
MACRO TLW
 The nodes moving by mobilty models present spatial
correlations that do not agree with experimental
observation 15

16. Main findings (8/9)
MACRO RWP
 The nodes moving by mobilty models present spatial
correlations that do not agree with experimental
observation 16

17. Main findings (9/9)
TLW
MACRO
 The nodes moving by mobilty models present spatial
correlations that do not agree with experimental
observation 17

18. Conclusions and future developments
 RWP and TLW mobility models fail to model key
properties collected to SocialDIS and MACRO
experiments
 Future work:
 Outdoor scenarios
 Larger scenario
 Adapted Mobility Models
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