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
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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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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