Content sharing and surveillance in the urban vehicle grid by ANejman

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									Content sharing and surveillance in the urban
                vehicle grid


                 MSN 2006
           Hong Kong, Dec 15, 2006


               Mario Gerla
       Computer Science Dept, UCLA
            www.cs.ucla.edu
                       Outline



•   Why vehicle communications
•   Emerging Standards
•   Content sharing: Car Torrent
•   Sensor platforms: MobEyes
•   Vehicular Safety: alerts, evacuation
•   The C-VeT testbed at UCLA
       Why Vehicle Communications?

• Safe navigation:
   – Vehicle & Vehicle, Vehicle & Roadway
     communications
   – Forward Collision Warning, Blind Spot Warning,
     Intersection Collision Warning…….
   – In-Vehicle Advisories
       • “Ice on bridge”, “Congestion ahead”,….
       Car to Car communications for Safe Driving

                                                                 Vehicle type: Cadillac XLR
                                                                 Curb weight: 3,547 lbs
                                                                 Speed: 65 mph
Vehicle type: Cadillac XLR                                       Acceleration: - 5m/sec^2
Curb weight: 3,547 lbs                                           Coefficient of friction: .65
Speed: 75 mph                                                    Driver Attention: Yes
Acceleration: + 20m/sec^2                                        Etc.
Coefficient of friction: .65     Alert Status: None
Driver Attention: Yes
Etc.
                                                                                          Alert Status: None




    Alert Status: Inattentive Driver on Right
        Alert Status: Slowing vehicle ahead
        Alert Status: Passing vehicle on left


                                                                                                Vehicle type: Cadillac XLR
                                                                                                Curb weight: 3,547 lbs
                                                                                                Speed: 45 mph
Vehicle type: Cadillac XLR                                                                      Acceleration: - 20m/sec^2
Curb weight: 3,547 lbs                                                                          Coefficient of friction: .65
Speed: 75 mph                                                                                   Driver Attention: No
Acceleration: + 10m/sec^2                       Alert Status: Passing Vehicle on left
                                                                                                Etc.
Coefficient of friction: .65
Driver Attention: Yes
Etc.
             Vehicle Comms(cont)

• Content/entertainment delivery/sharing:
   – Music, news, video etc
   – Location relevant multimedia files
   – Local ads, tourist information, etc
   – Passenger to passenger internet games
   – Peer to peer “data muling”
   – etc
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
            Vehicle Comms (cont)


• Environment sensing/monitoring:
  – Pavement conditions (eg, potholes)
  – Traffic monitoring
  – Pollution probing
  – Pervasive urban surveillance
  – “Unconscious” witnessing of
    accidents/crimes
        Convergence to a Standard:
      Government, Industry, Academia
• Federal Communications Commission created
  DSRC
   – … allocation of spectrum for DSRC based ITS
     applications to increase traveler safety, reduce
     fuel consumption and pollution, and continue to
     advance the nations economy.
       • FCC Report and Order, October 22, 1999, FCC
         99-305
       • Amendment with licensing rules in December
         2003
• DSRC Standard:
   – IEEE 802.11p
   – http://grouper.ieee.org/groups/scc32/dsrc/
      Convergence to a Standard (cont)

• USDOT has created Cooperative Intersection
  Collision Avoidance (CICAS) Consortium
   – http://www.its.dot.gov/cicas/cicas_workshop.htm
• Automotive companies created Vehicle Safety
  Communications Consortium (VSCC)
• Academia and Industry have sponsored several
  Special Issues, Workshops on the subject:
   – VANET, V2VCom, Autonet, etc
   USDOT VII: Vehicle Infrastructure Integration
                    Initiative

• http://www.itsa.org/vii.html
   – The VII Initiative is a cooperative effort between
     Federal and state departments of transportation
     (DOTs) and vehicle manufacturers to evaluate the
     technical, economic, and social/political feasibility
     of deploying a communications system to be used
     primarily for improving the safety and efficiency of
     the nation's road transportation system.
         The Standard: DSRC / IEEE 802.11p

• Car-Car communications at
  5.9Ghz                                          E v en t d a ta reco rd er (E D R )
                                                                                        P o sitio n in g sy ste m
• Derived from 802.11a        F orw ard rad ar



• three types of channels:                                                                                 C o m m u n ic a tio n
                                                                                                                fa c ility

  Vehicle-Vehicle service,
  a Vehicle-Gateway
  service and a control                                                                                 R ear radar


  broadcast channel .
                                         D isp la y                      C o m p u tin g p la tfo rm




• Ad hoc mode; and
  infrastructure mode
• 802.11p: IEEE Task Group for
  Car-Car communications
             The rest of my talk


A. Content Sharing and Sensor Applications
  Content sharing: Car Torrent
  Sensor platforms: MobEyes

B. Safety Related Applications
  Alert propagation
  Urban evacuation

C. The C-VeT testbed at UCLA
CarTorrent : Opportunistic Ad Hoc
  networking to download large
        multimedia files

      Alok Nandan, Shirshanka Das
       Giovanni Pau, Mario Gerla
              WONS 2005
      You are driving to Vegas
You hear of this new show on the radio
  Video preview on the web (10MB)
One option: Highway Infostation download



  Internet




   file
     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
                 CarTorrent: Basic Idea



Internet




           Download a piece




                                          Outside Range of Gateway


                              Transferring Piece of File from Gateway
  Co-operative Download: Car Torrent



Internet




                       Vehicle-Vehicle Communication




                     Exchanging Pieces of File Later
Car Torrent inspired by BitTorrent:
  Internet P2P file downloading
                              Uploader/downloader
  Uploader/downloader




                                     Uploader/downloader
                        Tracker



  Uploader/downloader

                                  Uploader/downloader
  CarTorrent: Gossip to discover peers




A Gossip message containing Torrent ID, Chunk list
and Timestamp is “propagated” by each peer

Problem: how to select the peer for downloading?
Selection Strategy Critical
    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!
            Network Coding




                        e = [e1 e2 e3 e4] encoding
                        vector tells how packet was
                        mixed (e.g. coded packet p =
                        ∑eixi where xi is original packet)




                             buffer

Receiver
recovers
 original                              random
    by                                  mixing
  matrix
inversion               Intermediate nodes
                                           CodeTorrent: Basic Idea
•                    Single-hop pulling (instead of CarTorrent multihop)



                                                                           Buffer
                                  Internet

                                                                               Buffer
                                                          Buffer
    File: k blocks




                       B1   *a1
                       B2   *a2
                       B3   *a3
                                  +                                 Re-Encoding: Random Linear Comb.
                            *ak       “coded” block                   of Encoded Blocks inof AP
                                                                           Outside Range the Buffer
                       Bk
                                                                   Exchange Re-Encoded Blocks
                     Random Linear Combination

                                                          Downloading Coded Blocks from AP
                                                      Meeting Other Vehicles with Coded Blocks
                Simulation Results

• Completion time density




                                        200 nodes
                                      40% popularity




                            Time (seconds)
Vehicular Sensor Network (VSN)
    IEEE Wiress Communications 2006
     Uichin Lee, Eugenio Magistretti (UCLA)




                Roadside base station




              Vehicle-to-roadside                 Inter-vehicle
               communications                   communications



                VSN-enabled vehicle
                 Sensors            Systems



                Video   Chem.   Storage Proc.
        Vehicular Sensor Applications


• Environment
  – Traffic congestion monitoring
  – Urban pollution monitoring
• Civic and Homeland security
  – Forensic accident or crime site investigations
  – Terrorist alerts
      Accident Scenario: storage and retrieval


•   Designated Cars:
    – Continuously collect images on the street (store data locally)
    – Process the data and detect an event
    – Classify the event as Meta-data (Type, Option, Location, Vehicle ID)
    – Post it on distributed index
•   Police retrieve data from designated cars
                                                     Summary
                                                     Harvesting
                           - Sensing
                           - Processing




                   CRASH




                                                    Crash Summary
                                                       Reporting




                           Meta-data : Img, -. (10,10), V10
           How to retrieve the data?

Two options:
• Upload to nearest AP (Cartel project, MIT)
• “Epidemic diffusion” (our proposed approach) :
   – Mobile nodes periodically broadcast meta-data of
     events to their neighbors
   – A mobile agent (the police) queries nodes and
     harvests events
   – Data dropped when stale and/or geographically
     irrelevant
         Epidemic Diffusion
- Idea: Mobility-Assist Meta-Data Diffusion
         Epidemic Diffusion
- Idea: Mobility-Assist Meta-Data Diffusion




                                            Keep “relaying”
                                            its meta-data to
                                            neighbors


                   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

            1. Agent (Police) harvests
               Meta-Data from its neighbors
            2. Nodes return all the meta-data
               they have collected so far
                         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
   Meta-data harvesting delay with RWP

• Higher mobility decreases harvesting delay


                                    V=25m/s
    Number of Harvested Summaries




                                        V=5m/s




                                              Time (seconds)
               Harvesting Results with “Real Track”

• Restricted mobility results in larger delay


                                   V=25m/s
   Number of Harvested Summaries




                                       V=5m/s




                                       Time (seconds)
Protecting vehicles against road perils
                 Evacuation from a Tunnel after a Fire:
                     Emergency Video Streaming
         • Multimedia type message propagation helps road
           safety
                – Precise situation awareness via video
                – Drivers can make better informed decisions
                                                         Real-time Video Streaming




                                            Fire inside the Tunnel



Source: http://www.landroverclub.net/Club/HTML/MontBlanc.htm
           Emergency Video Streaming

• Problems
   – Potential volume of multimedia traffic
   – Unreliable wireless channel
• Multimedia data delivery service that is reliable
  and efficient and real time
• Our Approach: Random network coding
                Emergency Video Streaming


• Highway Data Mule: Data is store-carry-and-forwarded via
  platoons in opposite direction
   – Random network coding for delayed data delivery



        405
                                                     Ramp




                                Pf -1            Pf -2




              Pr -1                       Pr-2
           Ramp                                             Ramp
Simulation Results (Delivery Ratio)



                         1.01
                           1
 Packet Delivery Ratio




                         0.99
                         0.98
                         0.97
                         0.96
                         0.95
                         0.94       Network Coding
                         0.93       Conventional Multicast
                         0.92
                                0     10          20         30   40
                                        Max Node Speed (m/sec)
The vehicle grid as an emergency
             network
                               Hot Spot




                   Hot Spot




Vehicular Grid as Opportunistic Ad Hoc Net
   STOP
    Power
   Blackout
                           Hot Spot




              Hot Spot




The Infrastructure Fails
   STOP
    Power
   Blackout




Vehicular Grid as Emergency Net
                      Evacuation Scenario




•   A highly dense area of a town needs to be evacuated because of a bomb threat,
    a chemical threat or an actual explosion
•   Evacuation plans that are in place today are static, do not adapt to a highly
    dynamic scenario
•   Must be able to dynamically re-evaluate and readjust the strategy
•   The infrastructure may have failed - must rely on Car to Car only
          Evacuation Scenario – Car to Car
                 communications




• Manage the evacuation of a town through the use of vehicular
  networks
   – Cars can sense and report local information (eg, radiation from a DIRTY Bomb
     explosion)
   – The information propagated by the cars can be used for safe evacuation
• Related project: RESCUE (Calit2) http://rescue.calit2.net
        C-VeT
Campus - Vehicular Testbed

      E. Giordano, A. Ghosh,
  G. Marfia, S. Ho, J.S. Park, PhD
 System Design: Giovanni Pau, PhD
     Advisor: Mario Gerla, PhD
                          Project Goals


• Provide:
   – A platform to support car-to-car experiments in various traffic
     conditions and mobility patterns
   – A shared virtualized environment to test new protocols and
     applications
   – 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
                              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 Commuting Vans
      • Measure freeway motion patterns (only tracking equipment installed
         in this fleet).
   – Hybrid cross campus connectivity using 10 WLAN Access Points .
                           The C-Box Node:

• Mature system deployment:
   –   Industrial PC (Linux OS)
   –   2 x WLAN Interfaces
   –   1 Software Defined Radio (FPGA based) Interface
   –   1 Control Channel
   –   1 GPS
• Current proof of concept:
   –   1 Dell Latitude Laptop (Windows)
   –   1 IEEE 802.11 Interface
   –   1 GPS
   –   OLSR Used for the Demo
             Preliminary Demo (Aug 06)

• Equipment:
  –   6 Cars roaming the UCLA Campus
  –   802.11g radios
  –   Clocks are in synch with GPS
  –   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
  –   Rough loss channel analysis through ping.
  –   Azureus P2P application
  –   On/Off traffic using Iperf
The C-VeT testbed
Campus Demo: connectivity via OLSR
          P2P Application: AZUREUS

We ran AZUREUS: a bit-torrent client that allows to use
 distributed trackers.

Intrinsically delay tolerant: a node automatically
   restarts download (after reconnect) without need of
   central tracker.

Each car downloads 5 different files from other cars

Average download rate per node = 200Kb/s
                       Conclusions

• V2V communications effective for
  content/entertainment:
   – Car torrent, Code torrent, Ad Torrent
   – Car to Car Internet games
• V2V are critical for urban surveillance:
   – Pervasive, mobile sensing: MobEyes
   – Emergency Networking
   – Evacuation
                          Future Work

• Still, lots of work ahead :
   –   Routing models: geo-routing, landmark routing, hybrid routing
   –   Transport models: epidemic, P2P
   –   Searching massive mobile storage
   –   Security, privacy, incentives
• The need for a testbed:
   – Realistic assessment of radio, mobility characteristics
   – Inclusion of user behavior
   – Interaction with (and support of ) the Infrastructure
 The End

Thank You

								
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