Communication and Content sharing in the Urban Vehicle Grid

Qualnet WorldOct 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

• 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,…

These roles demand efficient communications

The urban wireless options

• Cellular telephony – 2G (GSM, CDMA), 2.5G, 3G

• Wireless LAN (IEEE 802.11) access – WiFI, Mesh Nets, WIMAX

• Ad hoc wireless nets (manly based on 802.11)– 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

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 toWired networkVia Multihop

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: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Alert Status: None

Alert Status: Passing Vehicle on left

Alert Status: Inattentive Driver on Right

Alert Status: None

Alert Status: Slowing vehicle aheadAlert Status: Passing vehicle on left

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

Forward radar

Computing platform

Event data recorder (EDR)

Positioning system

Rear radar

Communication facility

Display

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 VegasYou 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

Download a piece

Internet

Transferring Piece of File from Gateway

Outside Range of Gateway

Co-operative Download: Car Torrent

Vehicle-Vehicle Communication

Internet

Exchanging Pieces of File Later

BitTorrent: Internet P2P file downloading

Uploader/downloader

Uploader/downloader

Uploader/downloader

Uploader/downloader

TrackerUploader/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

Receiverrecoversoriginal

by matrix

inversion

random mixing

buffer

Intermediate nodes

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

CodeTorrent: Basic Idea

Internet

Downloading Coded Blocks from AP

Outside Range of AP

Buffer

BufferBuffer

Re-Encoding: Random Linear Comb.of Encoded Blocks in the Buffer

Exchange Re-Encoded Blocks

Meeting Other Vehicles with Coded Blocks

• Single-hop pulling (instead of CarTorrent multihop)

“coded” block

B1

File

: k

blo

cks

B2B3

Bk

+

*a1

*a2*a3

*ak

Random Linear Combination

Simulation Results

• Histogram of Number of completions per slot (Slot = 20sec)

200 nodes40% popularity

Time (seconds)

Vehicular Sensor Network (VSN)IEEE Wiress Communications 2006

Uichin Lee, Eugenio Magistretti (UCLA)

VSN-enabled vehicle

Inter -vehiclecommunications

Vehicle -to-roadsidecommunications

Roadside base station

Vid e o Ch e m.

Sensors

S to ra g e

Systems

P ro c.

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

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

CRASH

- Sensing - P rocessing

Crash Summary Reporting

Summary Harvesting

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

1) “periodically” Relay (Broadcast) its Event to Neighbors 2) Listen and store other’s relayed events into one’s storage

Keep “relaying” its meta-data to neighbors

Epidemic Diffusion - Idea: Mobility-Assist Meta-Data Harvesting

Meta-Data Req

1. Agent (Police) harvestsMeta-Data from its neighbors

2. Nodes return all the meta-datathey have collected so far

Meta-Data Rep

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

Time (seconds)

Nu

mbe

r of

Har

vest

ed S

um

mar

ies

Private cars

Police

Harvesting Results with “Real Track”

• Restricted mobility results in larger delay

Time (seconds)

Nu

mbe

r of

Har

vest

ed S

um

mar

ies Police

Private cars

UU--VVee TTUcla - Vehicular TestbedUcla - 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– Remote access to U-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 .

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, PaoloBellavista, Antonio Corradi. MobEyes: Smart Mobs for UrbanMonitoring with a Vehicular Sensor Network. IEEEWireless 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. PerformanceEvaluation 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