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Validation and analysis of mobility models

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Presentation of my Master's Thesis.

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Validation and analysis of mobility models

  1. 1. Università degli studi “La Sapienza” di Roma Master’s Thesis in Computer ScienceSupervisor: Candidate:Prof. Luca Becchetti Umberto Griffo Matr. 799201Assistant Supervisor:Prof. Leonardo Querzoni
  2. 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 2
  3. 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. 4. Validation FrameworkMobility Contactmodels 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 4
  5. 5. Social Experiments with RFID Platform SocialDIS NeonMACRO # partecipants: 116 # partecipants: 114 # duration: 4 days # duration: 3h 5
  6. 6. Software architecture - new Gephi modules 6
  7. 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 7
  8. 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 8
  9. 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 9
  10. 10. Main findings (2/9) The models:  don’t capture the friendly ties 10
  11. 11. Main findings (3/9) The models:  don’t capture the friendly ties  overestimate the speed of flooding 11
  12. 12. Main findings (4/9) The mobility models overestimate temporal correlations The existence probability of a contact results to be approximately stationary 12
  13. 13. Main findings (5/9) The mobility models overestimate temporal correlations The existence probability of a contact results to be approximately stationary 13
  14. 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. 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. 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. 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. 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 18

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