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Vehicular Urban Sensing: efficiency and privacy issues NEC visit Mukaigawara, Japan, Dec 9, 2008

Vehicular Urban Sensing: efficiency and privacy issues NEC visit Mukaigawara, Japan, Dec 9, 2008. Mario Gerla Computer Science Dept, UCLA www.cs.ucla.edu. Outline. Vehicular communications V2V applications Content distribution Urban sensing, Mobeyes Bio inspired “harvesting”

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Vehicular Urban Sensing: efficiency and privacy issues NEC visit Mukaigawara, Japan, Dec 9, 2008

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  1. Vehicular Urban Sensing: efficiency and privacy issuesNEC visitMukaigawara, Japan, Dec 9, 2008 Mario Gerla Computer Science Dept, UCLA www.cs.ucla.edu

  2. Outline • Vehicular communications • V2V applications • Content distribution • Urban sensing, Mobeyes • Bio inspired “harvesting” • Security implications • The UCLA CAMPUS Testbed

  3. The Enabling Standard: DSRC / IEEE 802.11p Digital Short Range Communications • Car-Car communications at 5.9Ghz • Derived from 802.11a • three types of channels: Vehicle-Vehicle service, a Vehicle-Gateway service and a control broadcast channel . • Ad hoc mode and infrastructure mode • 802.11p: IEEE Task Group for Car-Car communications

  4. V2V Applications • Safe Navigation • Efficient Navigation/Commuting (ITS) • Urban Sensing • Location Relevant Content Distr. • Advertising • Commerce • Entertainment/Games

  5. V2V Applications • Safe navigation: • Forward Collision Warning, • Intersection Collision Warning……. • Advisories to other vehicles about road perils • “Ice on bridge”, “Congestion ahead”,….

  6. Car to Car communications 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

  7. V2V Applications (cont) • Efficient Navigation • GPS Based Navigators • Dash Express (just came to market):

  8. Intelligent Transport Systemsintelligent lane reservations

  9. V2V Applications (cont) • Environment sensing/monitoring: • Traffic monitoring • Pollution probing • Pavement conditions (eg, potholes) • Urban surveillance (eg, disturbance) • Witnessing of accidents/crimes

  10. V2V Applications (cont) • Location related content delivery/sharing: • Traffic information • Local attractions • Tourist information, etc

  11. CarTorrent : cooperative download of location relevant multimedia files

  12. You are driving to VegasYou hear of this new show on the radioVideo preview on the web (10MB)

  13. One option: Highway Infostation download Internet file

  14. Incentive for opportunistic “ad hoc networking” Problems: Stopping at gas station for full download is a nuisance Downloading from GPRS/3G too slow and quite expensive Observation: many other drivers are interested in download sharing (like in the Internet) Solution: Co-operative P2P Downloading via Car-Torrent

  15. CarTorrent: Basic Idea Internet Download a piece Outside Range of Gateway Transferring Piece of File from Gateway

  16. Co-operative Download: Car Torrent Internet Vehicle-Vehicle Communication Exchanging Pieces of File Later

  17. CarTorrent with Network Coding • Limitations of Car Torrent • Piece selection critical • Frequent failures due to loss, path breaks • New Approach – network coding • “Mix and encode” the packet contents at intermediate nodes • Random mixing (with arbitrary weights) will do the job!

  18. Network Coding e = [e1e2e3 e4] encoding vector tells how packet was mixed (e.g. coded packet p = ∑eixiwhere xiis original packet) buffer Receiver recovers original by matrix inversion random mixing Intermediate nodes

  19. Simulation Results • Completion time density 200 nodes40% popularity Time (seconds)

  20. Vehicular Sensor Network

  21. Vehicular Sensor Applications • Environment • Traffic density/congestion monitoring • Urban pollution monitoring • Pavement, visibility conditions • Civic and Homeland security • Bomb threat • Forensic accident or crime site investigations • Terrorist alerts

  22. Accident Scenario: storage and retrieval • Public/Private Cars (eg, busses, taxicabs, police, commuters, etc): • Continuously collect images on the street (store data locally) • Process the data and detectan event • Classify the event asMeta-data(Type, Option, Loc, time,Vehicle ID) • Distribute Metadata to neighbors probabilistically (ie, “gossip”) • Police retrieve data from public/private cars Meta-data : Image,”loud noise”,(10,10), 3:15PM, V1710

  23. Mobility-assisted Meta-data Diffusion/Harvesting • Mobeyes exploit “mobility” to disseminate meta-data! • Mobile nodes periodically broadcast meta-data to their neighbors • Only “originator” advertises meta-data to neighbors • Neighbors store advertisements in their local memory • Drop stale data • A mobile agent (the police) harvests meta-data from mobile nodes by actively querying them (with Bloom filter)

  24. HREP HREQ Mobility-assist Meta-data Diffusion/Harvesting Agent harvests a set of missing meta-data from neighbors Periodical meta-data broadcasting + Broadcasting meta-data to neighbors + Listen/store received meta-data

  25. Simulation Experiment • Simulation Setup • NS-2 simulator • 802.11: 11Mbps, 250m tx range • Average speed: 10 m/s • Mobility Models • Random waypoint (RWP) • Real-track model (RT) : • Group mobility model • merge and split at intersections • Westwood map

  26. Higher mobility decreases harvesting delay Number of Harvested Summaries Time (seconds) Meta-data harvesting delay with RWP V=25m/s V=5m/s

  27. Number of Harvested Summaries Time (seconds) Harvesting Results with “Real Track” • Restricted mobility results in larger delay V=25m/s V=5m/s

  28. Vehicular Security requirements Sender authentication Verification of data consistency Availability (ie, DoS prevention) Non-repudiation Privacy Situation Aware Trust Real-time constraints

  29. Privacy Attack : Tracking

  30. Situation Aware Trust (SAT) Situation? • Attribute based Trust • Situation elements are encoded into • attributes • Static attributes (affiliation) • Dynamic attributes (time and place) time place affiliation Attributes bootstrapped by social networks Dynamic attributes can be predicted • Social Trust • Bootstrap initial trust • Transitive trust relations • Proactive Trust • predict dyn attributes based on mobility and location service • establish trust in advance An attribute based situation example: Yellow Cab ANDTaxi AND Washington Street AND10-11pm 8/22/08

  31. Security based on attribute and policy group A driver wants to alert all taxicabsof company A on Washington Streetbetween 10-11pmthatconvention attendees need rides Central Key Master Extension of Attribute based Encryption (ABE) scheme [IEEE S&P 07] to incorporate dynamic access tree Attribute (companyA AND taxi AND Washington St. AND 10-11am) Ciphertext Receivers who satisfy those encoded attributes (have the corresponding private key) can decrypt the message Extended ABE Module plaintext Signature

  32. C-VeTCampus - Vehicular Testbed E. Giordano, A. Ghosh, G. Marfia, S. Ho, J.S. Park, PhD System Design: Giovanni Pau, PhD Advisor: Mario Gerla, PhD

  33. The Plan • We plan to install our node equipment in: • 30 Campus operated vehicles (including shuttles and facility management trucks). • Exploit “on a schedule” and “random” campus fleet mobility patterns • 30 Commuting Vans: Measure urban pollution, traffic congestion etc • 12 Private Vehicles: controlled motion experiments • Cross campus connectivity using 10 node Mesh (Poli Milano).

  34. Campus Initial Coverage Using MobiMesh

  35. C-VeT Goals Provide: • A platform for car-to-car experiments in various mobility patterns • A shared virtualized environment to test new protocols and applications • Full Virtualization • MadWiFi Virtualization (with on demand exclusive use) • Multiple OS support (Linux, Windows). • Large Scale Experiments • Qualnet simulator and Emulator Allow: • Collection of mobility traces and network statistics • Experiments on a real vehicular network • Provide a platform for Urban Sensing • Deployment of innovative V2V/V2I applications

  36. On Board Radio

  37. “Instrumenting” the vehicle

  38. Preliminary Experiments • Equipment: • 6 Cars roaming the UCLA Campus • 802.11g radios • Routing protocol: OLSR • 1 EVDO interface in the Lead Car • 1 Remote Monitor connected to the Lead Car through EVDO and Internet • Experiments: • Connectivity map computed by OLSR • Azureus P2P application

  39. Campus Demo: connectivity via OLSR

  40. Conclusions New VANET research opportunities: • Physical and MAC layers: • Radio virtualization; cognitive radios • Efficient, low latency safety message broadcast • Routing: • Geo routing, Delay tolerant routing, Network Coding, • New Applications: • Content, mobile sensing, harvesting • Urban surveillance; pollution monitoring • Application dependence of motion model/pattern • Security: • Privacy protection • Situation Aware Trust

  41. The Future • Still, lots of exciting research ahead • And, need a testbed to validate it! • Realistic assessment of radio, mobility characteristics • Account for user behavior • Interaction with (and support of ) the Infrastructure • Scalability to thousands of vehicles using hybrid simulation • We are building one at UCLA - come and share!

  42. Thank You!

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