Content sharing and surveillance in the urban vehicle grid MSN 2006 Hong Kong, Dec 15, 2006

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Content sharing and surveillance in the urban
vehicle grid MSN 2006Hong Kong, Dec 15, 2006

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

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Outline

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

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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,.

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Car to Car communications for Safe Driving
Vehicle type Cadillac XLRCurb weight 3,547
lbsSpeed 65 mphAcceleration -
5m/sec2Coefficient of friction .65Driver
Attention YesEtc.
Vehicle type Cadillac XLRCurb weight 3,547
lbsSpeed 75 mphAcceleration
20m/sec2Coefficient 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/sec2Coefficient of friction .65Driver
Attention NoEtc.
Vehicle type Cadillac XLRCurb weight 3,547
lbsSpeed 75 mphAcceleration
10m/sec2Coefficient of friction .65Driver
Attention YesEtc.
Alert Status Passing Vehicle on left
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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

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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
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Vehicle Comms (cont)

• Environment sensing/monitoring
• Pavement conditions (eg, potholes)
• Traffic monitoring
• Pollution probing
• Pervasive urban surveillance
• Unconscious witnessing of accidents/crimes

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Convergence to a StandardGovernment, 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/

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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

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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.

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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

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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

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CarTorrent Opportunistic Ad Hoc networking to
download large multimedia files

• Alok Nandan, Shirshanka Das
• Giovanni Pau, Mario Gerla
• WONS 2005

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

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One option Highway Infostation download
Internet
file
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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

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CarTorrent Basic Idea
Internet
Download a piece
Outside Range of Gateway
Transferring Piece of File from Gateway
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Co-operative Download Car Torrent
Internet
Vehicle-Vehicle Communication
Exchanging Pieces of File Later
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Car Torrent inspired by BitTorrent Internet
P2P file downloading
Uploader/downloader
Uploader/downloader
Uploader/downloader
Tracker
Uploader/downloader
Uploader/downloader
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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?
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Selection Strategy Critical
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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!

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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 by matrix inversion
random mixing
Intermediate nodes
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CodeTorrent Basic Idea

• Single-hop pulling (instead of CarTorrent
  multihop)

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
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Simulation Results

• Completion time density

200 nodes40 popularity
Time (seconds)
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Simulation Results

• Impact of mobility
• Speed helps disseminate from APs and C2C
• Speed hurts multihop routing (CarT)
• Car densitymultihop promotes congestion (CarT)

40 popularity
Avg. Download Time (s)
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Vehicular Sensor Network (VSN)IEEE Wiress
Communications 2006Uichin Lee, Eugenio
Magistretti (UCLA)
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Vehicular Sensor Applications

• Environment
• Traffic congestion monitoring
• Urban pollution monitoring
• Civic and Homeland security
• Forensic accident or crime site investigations
• Terrorist alerts

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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

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

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Epidemic Diffusion - Idea Mobility-Assist
Meta-Data Diffusion
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Epidemic Diffusion - Idea Mobility-Assist
Meta-Data Diffusion
1) periodically Relay (Broadcast) its
Event to Neighbors 2) Listen and store
others relayed events into ones storage
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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

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VSN Mobility-Assist Meta-Data Harvesting (cont)

• Assumption
• N disseminating nodes each node ni advertises
  event ei
• k-hop relaying (relay an event to k-hop
  neighbors)
• v average speed, R communication range
• ? network density of disseminating nodes
• Discrete time analysis (time step ?t)
• Metrics
• Average event percolation delay
• Average delay until all relevant data is harvested

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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

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Meta-data harvesting delay with RWP

• Higher mobility decreases harvesting delay

V25m/s
V5m/s
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Harvesting Results with Real Track

• Restricted mobility results in larger delay

V25m/s
V5m/s
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Protecting vehicles against road perils
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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/Mon
tBlanc.htm
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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

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Emergency Video Streaming

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

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Simulation Results (Delivery Ratio)
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Simulation Results (Overhead)
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The vehicle grid as an emergency network
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Hot Spot
Hot Spot
Vehicular Grid as Opportunistic Ad Hoc Net
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Hot Spot
Hot Spot
The Infrastructure Fails
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Vehicular Grid as Emergency Net
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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

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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

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C-Ve TCampus - Vehicular Testbed

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

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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 1000s 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

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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 .

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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

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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

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The C-VeT testbed
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Campus Demo connectivity via OLSR
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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

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Related Car to Car Projects

• UMassDiesel (UMass)
• A Bus-based Disruption Tolerant Network (DTN)
• http//signl.cs.umass.edu/diesel
• VEDAS (UMBC)
• A Mobile and Distributed Data Stream Mining
  System for Real-Time Vehicle Monitoring and
  diagnostics
• http//www.cs.umbc.edu/hillol/vedas.html
• CarTel (MIT)
• Vehicular Sensor Network for traffic conditions
  and car performance
• http//cartel.csail.mit.edu
• RecognizingCars (UCSD)
• License Plate, Make, and Model Recognition
• Video based car surveillance
• http//vision.ucsd.edu/car_rec.html

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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

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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

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The End

• Thank You