Dynamic Consensus for Secured Vehicular Ad hoc Networks

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Jonathan Petit

Talk given in IEEE Conference WiMob'2011 in Shanghai, China.

Tecnología Entretenimiento y humor

Dynamic Consensus for Secured Vehicular Ad hoc Networks

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Notas del editor

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VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
VANET definition - Application (LDW) - Trust\nMain problem in distributed systems - Decision method\n
naive not efficient computationally\nmajority most used in distributed systems\n
naive not efficient computationally\nmajority most used in distributed systems\n
naive not efficient computationally\nmajority most used in distributed systems\n
naive not efficient computationally\nmajority most used in distributed systems\n
naive not efficient computationally\nmajority most used in distributed systems\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Decision delay = consensus delay\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Explain event i, packet color. Remind the example of accidents.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Let's have a look on how is the criticalness computed.\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
Ca peut s'arr&#xEA;ter avant et aussi continuer apres la collision\n%attaquant est un challenge + apprentissage, analogie appel de phares\n
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T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
T(safety) = time to travel the safety distance\nExplain the criticalness (weight)\n
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To solve these issues we propose...\n
To solve these issues we propose...\n
To solve these issues we propose...\n
To solve these issues we propose...\n
To solve these issues we propose...\n
To solve these issues we propose...\n
To solve these issues we propose...\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
Bleu: calcul\nVert: r&#xE9;seau (d&#xE9;terministe)\nOrange: r&#xE9;seau (probabiliste)\n
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Standard compliant, mobility, window to reduce simulation complexity\n
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Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? Because we respect the real-time constraint of the application!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
Why not waiting more? We keep a gap before the braking time to warn the driver!\n
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Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
Problem trust - Define parameters - Generic model to visualize\n3 Contexts for FLEXIBILITY (computation + best-fitted)\nOpen issues: Threshold + Dynamic decision method change => Dynamic consensus for secured VANET\n
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