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Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Washington, DC

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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 ... – PowerPoint PPT presentation

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Title: Communication and Content sharing in the Urban Vehicle Grid Qualnet World Oct 27, 2006 Washington, DC


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


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

18
One option Highway Infostation download
Internet
file
19
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

20
CarTorrent Basic Idea
Internet
Download a piece
Outside Range of Gateway
Transferring Piece of File from Gateway
21
Co-operative Download Car Torrent
Internet
Vehicle-Vehicle Communication
Exchanging Pieces of File Later
22
BitTorrent Internet P2P file downloading
Uploader/downloader
Uploader/downloader
Uploader/downloader
Tracker
Uploader/downloader
Uploader/downloader
23
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
24
Selection Strategy Critical
25
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!

26
Random Linear 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
27
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
28
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
  • Westwood map
  • Three APs have full 1MB file
  • 250 pieces, 4KB ( 4pkts) each
  • UDP transfers

AP
AP
AP
29
Simulation Results
  • Histogram of Number of completions per slot (Slot
    20sec)

200 nodes40 popularity
Time (seconds)
30
Popularity Impact
popularity
31
Vehicular Sensor Network (VSN)MobeyesUichin
Lee, Eugenio Magistretti (UCLA)
32
Vehicular Sensor Applications
  • Environment
  • Traffic congestion monitoring
  • Urban pollution monitoring
  • Civic and Homeland security
  • Forensic accident or crime site investigations
  • Terrorist alerts

33
Accident Scenario storage and retrieval
  • Designated Cars
  • Continuously collect images on the street (store
    data locally)
  • Process the data and detect an event
  • Classify event as Meta-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
34
How to retrieve the data?
  • Epidemic diffusion
  • 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

35
Epidemic Diffusion - Idea Mobility-Assist
Meta-Data Diffusion
36
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
37
Epidemic Diffusion - Idea Mobility-Assist
Meta-Data Harvesting
  1. Agent (Police) harvestsMeta-Data from its
    neighbors
  2. Nodes return all the meta-datathey have
    collected so far

38
VSN Mobility-Assist Meta-Data Harvesting (cont)
  • Model 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

39
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

40
Meta-data harvesting delay with RWP
  • Higher speed -gt lower harvesting delay

41
Harvesting Results with Real Track
  • Restricted mobility results in larger delay

42
C -Ve TCampus - Vehicle Testbed
  • E. Giordano, A. Ghosh,
  • G. Marfia, S. Ho, J.S. Park, PhD
  • System Design Giovanni Pau, PhD
  • Advisor Mario Gerla, PhD

43
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 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

44
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 .

45
(No Transcript)
46
U-veT - 50 vehicle Campus testbed
47
Car 2 Car connectivity via OLSR
48
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

49
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

50
Modeling and simulation (cont)
  • Example 2 - Content Dissemination/search
    (Mobeyes)
  • Large population model required to study epidemic
    dissemination dynamics
  • Realistic motion pattern essential

51
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

52
Track Based Group Mobility Model
53
Group Motion Pattern (cont)
  • Can coexist with RWP and TRACK models
  • Group leader moves with RWP

54
Hybrid Simulation
  • Simulation applications not realistic enough
  • Testbed experiments will never reach meaningful
    population size
  • Enter - hybrid emulation

55
Hybrid Simulation in Whynet
Simulated large-scale network
Access Nodes Hybrid Simulation Server Cluster
Small-scale Real Testbed
Internet
56
Conclusions
  • Vehicular Communications offer opportunities
    beyond safe navigation
  • Dynamic content sharing/delivery Car Torrent
  • Pervasive, mobile sensing MobEyes
  • Massive Network games
  • Research Challenges
  • New routing/transport models epidemic
    dissemination, P2P, Congestion Control, Network
    Coding
  • Searching massive mobile storage
  • Security, privacy, incentives

57
Future Work
  • Extending the P2P sharing concepts to pedestrians
  • Health Networking
  • Security, privacy in vehicular content sharing
  • Network Coding
  • Implement CodeCast congestion control and ETE
    recovery above UDP
  • If loss used as feedback, key problem is
    discrimination between random error and
    congestion
  • Network Coding solutions for intermittent
    connectivity
  • Models that include mobility
  • Vehicular tesbed experiments

58
Publications
  • Uichin Lee, Eugenio Magistretti, Biao Zhou, Mario
    Gerla, Paolo
  • Bellavista, Antonio Corradi. MobEyes Smart Mobs
    for Urban
  • Monitoring with a Vehicular Sensor Network. IEEE
  • Wireless Communications, Sept 2006.
  • J.-S. Park, D. Lun, Y. Yi, M. Gerla, M. Medard.
    CodeCast
  • A Network Coding based Ad hoc Multicast Protocol.
  • IEEE Wireless Communications, Oct 2006.
  • J.-S. Park, D. Lun, M. Gerla, M. Medard.
    Performance
  • Evaluation of Network Coding in multicast MANET.
  • Proc. IEEE MILCOM 2006.
  • U. Lee, J.-S. Park, J. Yeh, G. Pau, M. Gerla.
    CodeTorrent
  • Content Distribution using Network Coding in
    VANET.
  • Proc. of MobiShare, Los Angeles, Sept 2006.

59
Support
  • This work was supported by
  • ARMY MURI Project DAWN (PI JJ Garcia)
    2005-2008 UCLA CoPI Rajive Bagrodia
  • ARMY Grant under the IBM - TITAN Project (PI,
    Dinesh Verma, IBM) 2006-2011 UCLA CoPIs
    Deborah Estrin, Mani Srivastava
  • NSF NeTS Grant - Emergency Ad Hoc Networking
    Using Programmable Radios and Intelligent Swarms
    2005-2009 PI Gerla, UCLA CoPIs - Soatto, Fitz,
    Pau

60
The End
  • Thank You
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