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VANET:On mobility Scenarios and Urban Infrastructure. & Realistic Simulation of Network Protocols in VANET Scenarios

VANET:On mobility Scenarios and Urban Infrastructure. & Realistic Simulation of Network Protocols in VANET Scenarios. Advisor: Kai-Wei Ke Speaker: Chia-Ho Chao Date: 22/04/2008. VANET:On mobility Scenarios and Urban Infrastructure. Outline. Overview of TRANSIMS Overview of CORSIM

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VANET:On mobility Scenarios and Urban Infrastructure. & Realistic Simulation of Network Protocols in VANET Scenarios

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  1. VANET:On mobility Scenarios and Urban Infrastructure.&Realistic Simulation of Network Protocols in VANET Scenarios Advisor: Kai-Wei Ke Speaker: Chia-Ho Chao Date: 22/04/2008

  2. VANET:On mobility Scenarios and Urban Infrastructure.

  3. Outline • Overview of TRANSIMS • Overview of CORSIM • Random waypoint (RWP) • Mobility Models Comparison • Flat Network • Opportunistic Infrastructure • Inter-contact Times • Afternoon Trend • Conclusion

  4. TRANSIMS Traces • TRANSIMS : Transportation Analysis Simulation System • Asses the performance of a large scale urban sensor network. • Creating car movement patterns based on activity flows by large scale, vehicular traffic and parallel simulator.

  5. CORSIM Traces • CORSIM : Microscopic Traffic Simulation Model • high level of precision in vehicular traffic simulation. • difference from TRANSIMS: • Single CPU • Lack of activity flow information

  6. RWP

  7. Mobility Models Comparison:Simulation setting

  8. Simulation setting • VANET simulations are run for 200 seconds on a 1*2 km rectangle on the map. • Highest AP density

  9. Mobility Models Comparison:FLAT NETWORK

  10. Using TRANSIMS mobility traces 7am no APs 8am no APs

  11. Using RWP model 7am no APs 8am no APs

  12. Using CORSIM mobility traces 7am no APs 8am no APs

  13. Mobility Models Comparison:OPPORTUNISTIC INFRASTRUCTURE

  14. Simulation Setting

  15. Simulation Setting

  16. Using TRANSIMS mobility traces 7am with APs 8am with APs

  17. Using RWP model 7am with APs 8am with APs

  18. Using CORSIM mobility traces 7am with APs 8am with APs

  19. Mobility Models Comparison:Inter-contact TimesandAfternoon trend

  20. TRANSIMS mobility traces at 7AM

  21. TRANSIMS mobility traces at 8AM

  22. Afternoon trend

  23. Afternoon trend

  24. Conclusion • The results confirm that open APs can be effectively exploited to dramatically improve performance. • A correct model of traffic flows is important. • In long timeframes during the day (i.e. rush hours), network becomes static.

  25. Realistic Simulation of Network Protocols in VANET Scenarios

  26. Outline • Traffic Simulation • Network Simulation • Coupling Traffic Microsimulation and Network Simulation • Simulation Result • Conclusion

  27. Traffic Simulation • Macroscopic models • METACOR • Mesoscopic models • CONTRAM • Microscopic models • Cellular Automaton model (CA) • SK model • IDM/MOBIL model

  28. Intelligent-Driver Model(IDM) • Car-following model Desired gap Minimum gap(jam) Additional gap(driving) Time headway Difference in speed a:comfortable acceleration b:comfortable deceleration Acceleration exponent acceleration Desired velocity The gap to a vehicle in front

  29. MOBIL • MOBIL: Minimizing Overall Braking decelerations Induced by Lane change • Have to be fulfilled two criteria: • The lane change has to be safe. politeness factor Lane change threshold Desired gap b) Acceleration Desired gap Maximum safe deceleration Bias to the right lane

  30. Road Traffic Microsimulation Parameters δ

  31. OMNeT++ • A discrete event simulation environment. • primary application area is the simulation of communication networks • GUI support • INET Framework

  32. DYMO Routing Protocol • DYMO: Dynamic MANET On-Demand • Reactive A AB A ABC A A B C D AODV DYMO DCB D DC D D

  33. DYMO and support modules in the protocol stack App1 App2 DYMO Transport Layer queue hook Network Layer Data Link Layer

  34. Simulated VANET Scenario

  35. Coupling Traffic Microsimulation and Network Simulation Excerpt from the traffic simulation’s output stream

  36. Simulation Result 15%

  37. Simulation Result

  38. Simulation Result

  39. Conclusion • Integrated the traffic model in network simulation in order to improve the quality of network simulations. • Simulation setups using simple mobility models often produce skewed results compared to the application of traffic models.

  40. Reference • G. Marfia, G. Pau, E. De Sena, E. Giordano, M. Gerla, “Evaluating Vehicle Network Strategies for Downtown Portland: opportunistic infrastructure and the importance of realistic mobility models,” • http://transims.tsasa.lanl.gov/. • http://mctrans.ce.ufl.edu/featured/TSIS/Version5/corsim.htm. • http://www.omnetpp.org/ • I. Dietrich, C. Sommer, and F. Dressler, "Simulating DYMO in OMNeT++," University of Erlangen, Dept. of Computer Science 7, Technical Report 01/07, April 2007.

  41. Thanks for your attention!

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