Transcript QoS Guarantee in Wirless Network

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
• Network layer optimization
– Network Coding
• Modeling and simulation challenges
• Conclusions
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
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
Wireless Infrastructure vs Ad Hoc
Infrastructure Network (WiFI or 3G)
Ad Hoc, Multihop wireless Network
Ad Hoc Network Characteristics
• Instantly deployable, re-configurable (No fixed
infrastructure)
• Created to satisfy a “temporary” need
• Portable (eg sensors), mobile (eg, cars)
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
SATELLITE
COMMS
SURVEILLANCE
MISSION
SURVEILLANCE
MISSION
UAV-UAV NETWORK
AIR-TO-AIR
MISSION
STRIKE
MISSION
COMM/TASKING
Unmanned
Control Platform
COMM/TASKING
COMM/TASKING
RESUPPLY
MISSION
UAV-UGV NETWORK
FRIENDLY
GROUND CONTROL
(MOBILE)
Manned
Control Platform
AINS: Autonomous Intelligent Network System
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)
Urban “opportunistic” ad hoc networking
From Wireless to
Wired network
Via Multihop
Car to Car communications for Safe Driving
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 75 mph
Acceleration: + 20m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 65 mph
Acceleration: - 5m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
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 XLR
Curb weight: 3,547 lbs
Speed: 75 mph
Acceleration: + 10m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
Alert Status: Passing Vehicle on left
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 45 mph
Acceleration: - 20m/sec^2
Coefficient of friction: .65
Driver Attention: No
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
The Standard: DSRC / IEEE 802.11p
• Car-Car communications at
5.9Ghz
• Derived from 802.11a
Event data recorder (EDR)
Forward radar
• 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
Positioning system
Communication
facility
Rear radar
Display
Computing platform
DSRC Channel Characteristics
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
BitTorrent: Internet P2P file downloading
Uploader/downloader
Uploader/downloader
Tracker
Uploader/downloader
Uploader/downloader
Uploader/downloader
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
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!
“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
CodeTorrent: Basic Idea
•
Single-hop pulling (instead of CarTorrent multihop)
Buffer
Internet
File: k blocks
Buffer
B1
B2
B3
*a1
*a2
*a3
*ak
+
“coded” block
Bk
Random Linear Combination
Buffer
Re-Encoding: Random Linear Comb.
OutsideBlocks
Rangeinofthe
APBuffer
of Encoded
Exchange Re-Encoded Blocks
Downloading Coded Blocks from AP
Meeting Other Vehicles with Coded Blocks
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 AP’s have full 1MB file
– 250 pieces, 4KB (= 4pkts) each
UDP transfers
•
AP
AP
AP
Simulation Results
• Histogram of Number of completions per slot (Slot = 20sec)
200 nodes
40% popularity
Time (seconds)
Popularity Impact
popularity
Vehicular Sensor Network (VSN)
“Mobeyes”
Uichin Lee, Eugenio Magistretti (UCLA)
Roadside base station
Inter-vehicle
communications
Vehicle-to-roadside
communications
VSN-enabled vehicle
Sensors
Video
Chem.
Systems
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 event as Meta-data (Type, Option, Location, Time,Vehicle ID)
– Post it on distributed index
Police retrieve data from designated cars
- Sensing
- Processing
Summary
Harvesting
CRASH
Crash Summary
Reporting
Meta-data : Img, -. Time, (10,10), V10
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
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
Number of Harvested Summaries
• Higher speed -> lower harvesting delay
Time (seconds)
Harvesting Results with “Real Track”
Number of Harvested Summaries
• Restricted mobility results in larger delay
Time (seconds)
C -VeT
Campus - Vehicle 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
– 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 Communing Vans
• Measure freeway motion patterns (only tracking
equipment installed in this fleet).
– Hybrid cross campus connectivity using 10 WLAN
Access Points .
U-veT - 50 vehicle Campus testbed
Car 2 Car connectivity via OLSR
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
Modeling and simulation (cont)
• Example 2 - Content Dissemination/search
(Mobeyes)
– Large population model required to study epidemic dissemination
dynamics
– Realistic motion pattern essential
- Sensing
- Processing
Summary
Harvesting
CRASH
Crash Summary
Reporting
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
Track Based Group Mobility Model
Group Motion Pattern (cont)
• Can coexist with RWP and TRACK models
• Group leader moves with RWP
Landmark
Logical Group
Hybrid Simulation
• Simulation: applications not realistic enough
• Testbed experiments: will never reach meaningful
population size
• Enter - hybrid emulation
Hybrid Simulation in Whynet
- Sensing
- Processing
Summary
Harvesting
Simulated large-scale network
CRASH
Crash Summary
Reporting
Access Nodes & Hybrid Simulation Server Cluster
Small-scale Real Testbed
Internet
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
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.
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
The End
Thank You