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V2V applications: End to end or broadcast-based? Panel VANET 2007, Sept 10, 2007. Mario Gerla Computer Science Dept, UCLA www.cs.ucla.edu. End to end vs broadcast based. VANET E2E networking (without infrastructure) extremely challenging: An urban VANET may have over 100,000 nodes

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v2v applications end to end or broadcast based panel vanet 2007 sept 10 2007

V2V applications:End to end or broadcast-based?Panel VANET 2007, Sept 10, 2007

Mario Gerla

Computer Science Dept, UCLA

www.cs.ucla.edu

end to end vs broadcast based
End to end vs broadcast based
  • VANET E2E networking (without infrastructure) extremely challenging:
    • An urban VANET may have over 100,000 nodes
    • Nodes move in unpredictable ways
    • End to end routing is HARD
      • AODV and OLSR do not scale
      • Geo-routing can get trapped in “dead ends”
      • Geo Location Service not very scalable
    • TCP over several hops “in motion” is a nightmare!
    • Intermittent connectivity in most cases
  • So, end to end applications (a la mesh network) hard to deploy
  • However….
where are the e2e applications
Where are the E2E applications?
  • Very few urban scenarios/applicatios require “true” E2E networking:
    • Emergency, disaster recovery (eg, earthquake, hostile attack)
    • Urban warfare
  • Generally, these are situations where the Infrastructure has failed
slide4

Vehicular Grid as Emergency Net

Power

Blackout

Power

Blackout

broadcast based applications
Broadcast based applications
  • The most popular VANET applications are “broadcast” based
    • Safe navigation - neighborhood broadcast
    • Content sharing - P2P proximity routing
    • Distributed urban sensing - Epidemic dissemination
car to car broadcast for safe driving
Car to Car broadcast for Safe Driving

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 65 mphAcceleration: - 5m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Alert Status: None

Alert Status: None

Alert Status: Inattentive Driver on Right

Alert Status: Slowing vehicle ahead

Alert Status: Passing vehicle on left

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Alert Status: Passing Vehicle on left

co operative download car torrent
Co-operative Download: Car Torrent

Internet

Vehicle-Vehicle Communication

Exchanging Pieces of File en route

accident scenario storage and retrieval
Accident Scenario: storage and retrieval
  • Designated Cars (eg, busses, taxicabs, UPS, police agents, etc):
    • Continuously collect images on the street (store data locally)
    • Process the data and detectan event
    • Classify the event asMeta-data(Type, Option, Location, Vehicle ID)
    • Epidemically disseminate -> distributed index
  • Police retrieve data from designated cars

Meta-data : Img, -. (10,10), V10

epidemic diffusion idea mobility assist meta data diffusion10

Keep “relaying” its meta-data to neighbors

Epidemic Diffusion- Idea: Mobility-Assist Meta-Data Diffusion

1) “periodically” Relay (Broadcast) its Event to Neighbors

2) Listen and store other’s relayed events into one’s storage

epidemic diffusion idea mobility assist meta data harvesting

Meta-Data Rep

Meta-Data Req

Epidemic Diffusion- Idea: Mobility-Assist Meta-Data Harvesting
  • Agent (Police) harvestsMeta-Data from its neighbors
  • Nodes return all the meta-datathey have collected so far
the future
The Future
  • Future VANET applications will be broadcast, proximity routing based
  • However, proximity and broadcast only removes the E2E complexity
  • Enormous challenges still ahead:
  • Navigation safety
    • “liability” stigma
    • strict delay constraints
  • Location aware content, Infotainment
    • Driver distraction -> more accidents?
    • Virus scare
  • Urban Sensing
    • Business model not clear (who makes money?)
    • Privacy issues
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