validation and analysis of mobility models
DESCRIPTION
Presentation of my Master's Thesis.TRANSCRIPT
Validation and analysis of mobility models
Università degli studi “La Sapienza” di RomaMaster’s Thesis in Computer Science
Supervisor:Prof. Luca Becchetti
Candidate:Umberto GriffoMatr. 799201
Assistant Supervisor:Prof. Leonardo Querzoni
2
GoalsValidation of mobility models in social
contextsRandom Waypoint Truncated Lévy Walk
Software development for efficient simulation of algorithms on Evolving Dynamic Network
3
Mobility Models
Truncated Lévy Walk Random Waypoint
Mobile nodes follow random directions with speed chosen randomly. The destination, speed and direction changes when waiting time is ended.
The human walks are approximated with the Lévy walks.
Validation Framework
Real-world social contacts•SocialDIS•MACRO
Real contact traces
Contact Graph•Aggregated•Dynamic
Statistical analysis
Mobility models• RWP• TLW
Models Execution
Synthetic contact traces
Contact Graph•Aggregated
•Dynamic
Statistical analysis
Validation
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Social Experiments with RFID Platform
SocialDIS# partecipants: 116# duration: 4 days
NeonMACRO# partecipants: 114# duration: 3h
Software architecture - new Gephi modules
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ContributionsGathering and processing of user traces gathered by
social experiment NeonMACRODefinition 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 importerimplementation of an efficient dinamicity simulator
(FastUtils)implementation of Mobility Models (RWP and TLW)implementation of algorithms to compute metrics and
statistical indicesExtensive experimental analysis of mobility models
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Experimental analysisOn aggregated Contact Graph
Weighted Clustering CoefficientStrengthDensityModularity
On Evolving NetworkInter-Intra contact timesFlooding timeDistance from stationaritySpatial/Time correlations
Main findings (1/9)Dataset # Edges Average
degree
Average
strength
Graph
density
MACRO 132 2,316 0,004 0,02
TLW 5394 94,63 1 0,83
RWP 6120 107,368 1 0,95
Dataset Average
Clustering
Coefficient
Average
Weighted
Clustering
Coefficient
MACRO 0,378 0,237
TLW 0,848 0,853
RWP 0,951 0,951
Social experiments: contacts mostly with “friends” seldom with “strangers”
Mobility models: all-to-all like contacts
Dataset Average
Intra-
contact
Time
(seconds)
Average
Inter-
contact
Time
(seconds)
#
Conta
ct
#
Interv
al
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
Main findings (2/9)The models:
don’t capture the friendly ties
Main findings (3/9)The models:
don’t capture the friendly ties
overestimate the speed of flooding
Main findings (4/9)The mobility models
overestimate temporal correlations
The existence probability of a contact results to be approximately stationary
Main findings (5/9)The mobility models
overestimate temporal correlations
The existence probability of a contact results to be approximately stationary
Main findings (6/9)
The nodes moving by mobilty models present spatial correlations that do not agree with experimental observation
RWPMACRO
Main findings (7/9)
The nodes moving by mobilty models present spatial correlations that do not agree with experimental observation
TLWMACRO
Main findings (8/9)
The nodes moving by mobilty models present spatial correlations that do not agree with experimental observation
RWPMACRO
Main findings (9/9)
The nodes moving by mobilty models present spatial correlations that do not agree with experimental observation
TLW
MACRO
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Conclusions and future developmentsRWP and TLW mobility models fail to model
key properties collected to SocialDIS and MACRO experiments
Future work:Outdoor scenariosLarger scenarioAdapted Mobility Models