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Mario Gerla cs.ucla/NRL

Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Washington, DC. Mario Gerla www.cs.ucla.edu/NRL. Outline. New vehicle roles in urban environments Opportunistic “Ad Hoc” Wireless Networks V2V applications Car Torrent MobEyes

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Mario Gerla cs.ucla/NRL

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  1. Communication and Content sharing in the Urban Vehicle GridQualnet WorldOct 27, 2006Washington, DC Mario Gerla www.cs.ucla.edu/NRL

  2. Outline • New vehicle roles in urban environments • Opportunistic “Ad Hoc” Wireless Networks • V2V applications • Car Torrent • MobEyes • Network layer optimization • Network Coding • Modeling and simulation challenges • Conclusions

  3. New Roles for Vehicles on the road • Vehicle as a producer of geo-referenced data about its environment • Pavement condition • Weather data • Physiological condition of passengers, …. • Vehicle as Information Gateway • Internet access, infotainment, P2P content sharing, …… • Vehicle collaborates with other Vehicles and with Roadway • Forward Collision Warning, Intersection Collision Warning……. • Ice on bridge,… Need efficient wireless communications

  4. The urban wireless options • Cellular telephony • 2G (GSM, CDMA), 2.5G, 3G • Wireless LAN (IEEE 802.11) access • WiFI, Mesh Nets, WIMAX • Satellites, UAVs (Unattended Air Vehicles) • Expensive when used for Internet access • Mostly military, disaster recovery • Ad hoc wireless nets • Set up in an area with no infrastructure; to respond to a specific, time limited need

  5. Wireless Infrastructure vs Ad Hoc Infrastructure Network (WiFI or 3G) Ad Hoc, Multihop wireless Network

  6. Ad Hoc Network Characteristics • Instantly deployable, re-configurable (No fixed infrastructure) • Created to satisfy a “temporary” need • Portable (eg sensors), mobile (eg, cars)

  7. Traditional Ad Hoc Network Applications Military • Automated battlefield Civilian • Disaster Recovery (flood, fire, earthquakes etc) • Law enforcement (crowd control) • Homeland defense • Search and rescue in remote areas • Environment monitoring (sensors) • Space/planet exploration

  8. SURVEILLANCE MISSION AIR-TO-AIR MISSION STRIKE MISSION RESUPPLY MISSION FRIENDLY GROUND CONTROL (MOBILE) SATELLITE COMMS SURVEILLANCE MISSION UAV-UAV NETWORK COMM/TASKING COMM/TASKING Unmanned UAV-UGV NETWORK Control Platform COMM/TASKING Manned Control Platform AINS: Autonomous Intelligent Network System

  9. New Trend: “Opportunistic” ad hoc nets • Driven by “commercial” application needs • Indoor W-LAN extended coverage • Group of friends sharing 3G via Bluetooth • Peer 2 peer networking in the vehicle grid • Access to Internet: • available, but; it can be “opportunistically” replaced by the “ad hoc” network (if too costly or inadequate)

  10. Urban “opportunistic” ad hoc networking From Wireless to Wired network Via Multihop

  11. 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

  12. Opportunistic piggy rides in the urban mesh • Pedestrian transmits a large file block by block to passing cars, busses • The carriers deliver the blocks to the hot spot

  13. The Standard: DSRC / IEEE 802.11p • 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

  14. DSRC Channel Characteristics

  15. CarTorrent : Opportunistic Ad Hoc networking to download large multimedia files Alok Nandan, Shirshanka Das Giovanni Pau, Mario Gerla WONS 2005

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

  17. One option: Highway Infostation download Internet file

  18. 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

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

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

  21. BitTorrent: Internet P2P file downloading Uploader/downloader Uploader/downloader Uploader/downloader Tracker Uploader/downloader Uploader/downloader

  22. CarTorrent: Gossip protocol A Gossip message containing Torrent ID, Chunk list and Timestamp is “propagated” by each peer Problem: how to select the peer for downloading

  23. Selection Strategy Critical

  24. 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!

  25. “Random Linear” 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

  26. Single-hop pulling (instead of CarTorrent multihop) Buffer Buffer Buffer B1 *a1 B2 *a2 *a3 File: k blocks B3 + “coded” block *ak Bk Random Linear Combination CodeTorrent: Basic Idea Internet Re-Encoding: Random Linear Comb.of Encoded Blocks in the Buffer Outside Range of AP Exchange Re-Encoded Blocks Downloading Coded Blocks from AP Meeting Other Vehicles with Coded Blocks

  27. Simulation Experiment • Qualnet simulator • 802.11: 2Mbps, 250m tx range • Average speed: 10-30 m/s • 2.4 X 2.4 Km • Real-track motion model (RT) : • merge and split at intersection • Westwoodmap • Three AP’s have full 1MB file • 250 pieces, 4KB (= 4pkts) each • UDP transfers AP AP AP

  28. Simulation Results • Histogram of Number of completions per slot (Slot = 20sec) 200 nodes40% popularity Time (seconds)

  29. Popularity Impact popularity

  30. Vehicular Sensor Network (VSN)“Mobeyes”Uichin Lee, Eugenio Magistretti (UCLA)

  31. Vehicular Sensor Applications • Environment • Traffic congestion monitoring • Urban pollution monitoring • Civic and Homeland security • Forensic accident or crime site investigations • Terrorist alerts

  32. Accident Scenario: storage and retrieval • Designated Cars: • Continuously collect images on the street (store data locally) • Process the data and detectan event • Classify event asMeta-data (Type, Option, Location, Time,Vehicle ID) • Post it on distributed index • Police retrieve data from designated cars Meta-data : Img, -. Time, (10,10), V10

  33. How to retrieve the data? • “Epidemic diffusion” : • Mobile nodes periodically broadcast meta-data of events to their neighbors • A mobileagent(the police) queries nodes and harvests events • Data dropped when stale and/or geographically irrelevant

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

  35. 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

  36. 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

  37. 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 • Westwoodmap

  38. Higher speed -> lower harvesting delay Number of Harvested Summaries Time (seconds) Meta-data harvesting delay with RWP

  39. Number of Harvested Summaries Time (seconds) Harvesting Results with “Real Track” • Restricted mobility results in larger delay

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

  41. Project Goals • Provide: • A platform to support car-to-car experiments in various traffic conditions and mobility patterns • Remote access to C -VeT through web interface • Extendible to 1000’s of vehicles through WHYNET emulator • potential integration in the GENI infrastructure • Allow: • Collection of mobility traces and network statistics • Experiments on a real vehicular network

  42. Big Picture • We plan to install our node equipment in: • 50 Campus operated vehicles (including shuttles and facility management trucks). • Exploit “on a schedule” and “random” campus fleet mobility patterns • 50 Communing Vans • Measure freeway motion patterns (only tracking equipment installed in this fleet). • Hybrid cross campus connectivity using 10 WLAN Access Points .

  43. U-veT - 50 vehicle Campus testbed

  44. Car 2 Car connectivity via OLSR

  45. Modeling and simulation challenges • Example 1: Urban evacuation model following a chemical/nuclear disaster • Need accurate vehicle layout pattern • Need to model the ENTIRE grid - millions of nodes (subset will not do!) • Need accurate GPS reception and urban propagation models

  46. Modeling and simulation (cont) • Example 2 - Content Dissemination/search (Mobeyes) • Large population model required to study epidemic dissemination dynamics • Realistic motion pattern essential

  47. Motion Pattern Modeling • Random way point (RWP): • Too pessimistic for network connectivity, path breaks • Too optimistic for epidemic diffusion • No correlated motion • Random Trip model (Le Boudec, EPFL): • Concatenation of random “trips” • Track model: • Inspired to Markov Chain models • Can incorporate correlated motion • Traces • Experimentally collected (from GPS sensors on cars, say), or; • Artificially calculated from Census data • Enormous complexity in the simulation

  48. Track Based Group Mobility Model

  49. Group Motion Pattern (cont) • Can coexist with RWP and TRACK models • Group leader moves with RWP

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