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Predictive Mobile IP for a Rapid Mobility Environment Dr. Edwin A. Hernandez,  Dr. Sumi Helal Harris Lab University of Florida November 16 th , 2004
Motivation ,[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Performance of Mobile-IP for TCP transmissions (FTP) ns- 2 simulation.  (a) Average throughput (b) Percentage of usable time (not in handoff)
RAMON : A network emulation approach  ,[object Object],[object Object],[object Object]
Implementation of RAMON antennas Agents Access Points
Average throughput at different speeds and attenuation patterns.  ,[object Object],[object Object],[object Object]
Ghost Mobile-IP ,[object Object],[object Object],[object Object]
The “ghost” entities ,[object Object],[object Object]
Ghost Mobile-IP  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Mobility and Extensions to Mobile-IP ,[object Object],[object Object]
Ghost Mobile Node ,[object Object],[object Object],[object Object],0  1  2  3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Type  |S|B|D|M|G|V|rsv|  Lifetime  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Home Address  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Home Agent  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Care-of Address  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  | +  Identification  + |  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Extensions ... +-+-+-+-+-+-+-+-
Ghost Mobile Node ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0  1  2  3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Type  |S|B|D|M|G|V|rsv|  Lifetime  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Home Address  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Home Agent  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  Care-of Address  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |  | +  Identification  + |  | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Extensions ... +-+-+-+-+-+-+-+- distance(FA, MN) refers to the Predictive distance
Ghost Foreign Agent ,[object Object],[object Object]
Ghost Foreign Agent ,[object Object],[object Object],[object Object],[object Object],[object Object]
Location tracking with Kalman Filter ,[object Object],[object Object]
Equations  State vector  : Measurement vector: In our case the state vector indicates, speed in <x,y> and the <x,y> Coordinates of the MH. The Measurement vector are the values of <x,y> measured from a GPS system or a location tracking device. System equations:
Equations :  Matrices representing our system: Kalman Filter Time-Update equations: Q =  E{ w k  w k T   }.
Filter Performance (MATLAB) 2-20 m Error  Per Prediction T  = 5 sec
Results: Average Throughput of M-IP and Predictive M-IP
TCP sequence-time plots at 40m/s Ghost-MobileIP Dynamics MIP
Time-sequence plots at 80 m/s Ghost MobileIP Dynamics MIP
Conclusion ,[object Object],[object Object],[object Object],[object Object]
Summary and Future Work ,[object Object],[object Object],[object Object]
Questions / Comments? ,[object Object]

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Edwin Hernandez Presentation for Local Computer Networks n 2004

  • 1. Predictive Mobile IP for a Rapid Mobility Environment Dr. Edwin A. Hernandez, Dr. Sumi Helal Harris Lab University of Florida November 16 th , 2004
  • 2.
  • 3.
  • 4. Performance of Mobile-IP for TCP transmissions (FTP) ns- 2 simulation. (a) Average throughput (b) Percentage of usable time (not in handoff)
  • 5.
  • 6. Implementation of RAMON antennas Agents Access Points
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. Equations State vector : Measurement vector: In our case the state vector indicates, speed in <x,y> and the <x,y> Coordinates of the MH. The Measurement vector are the values of <x,y> measured from a GPS system or a location tracking device. System equations:
  • 18. Equations : Matrices representing our system: Kalman Filter Time-Update equations: Q = E{ w k w k T }.
  • 19. Filter Performance (MATLAB) 2-20 m Error Per Prediction T = 5 sec
  • 20. Results: Average Throughput of M-IP and Predictive M-IP
  • 21. TCP sequence-time plots at 40m/s Ghost-MobileIP Dynamics MIP
  • 22. Time-sequence plots at 80 m/s Ghost MobileIP Dynamics MIP
  • 23.
  • 24.
  • 25.