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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 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 • 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? • 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
Vehicular Grid as Emergency Net Power Blackout Power Blackout
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 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 Internet Vehicle-Vehicle Communication Exchanging Pieces of File en route
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 Diffusion
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
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 • 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