An Architecture Study of Ad-Hoc Vehicle Networks

Richard Fujimoto

Hao Wu

Computational Science & Engineering

College of Computing

Georgia Institute of Technology

Randall Guensler

Michael Hunter

Civil and Environmental Engineering

College of Engineering

The Costs of Mobility

Safety: 6 Million crashes, 41,000 fatalities in U.S. per year ($150 Billion)

Congestion: 3.5 B hours delay, 5.7 B gal. wasted fuel per year in U.S. ($65 Billion)

Pollution: > 50% hazardous air pollutants in U.S., up to 90% of the carbon monoxide in urban air

< 0.5 Million

0.5 - 1M

1M - 3M

> 3M

Source: 2005 Annual Urban Mobility Report (http://mobility.tamu.edu)Texas Natural Resource Conservation Commission (http://www.tnrcc.state.tx.us/air)

Disproportionate increase in car ownership relative to population growth in China, India

Intelligent Transportation Systems

www.georgia-navigator.com

ITS deployments: Traffic Management Centers (TMC)– Roadside cameras, sensors, communicate to TMC via private network– Disseminate information (web, road signs), dispatch emergency vehicles

Infrastructure heavy– Expensive to deploy and maintain; limited coverage area– Limited traveler information– Limited ability to customize services for individual travelers

Current Trends

Smart Vehicles On-board GPS, digital maps Vehicle, environment sensors Significant computation, storage,

communication capability Not power constrained

U.S. DOT Vehicle Infrastructure Integration (VII) Initiative

Public/private partnership “Establishment of vehicle-to-vehicle and

vehicle-to-roadside communication capability nationwide”

Improve safety, reduce congestion

Dedicated Short Range Communications (DSRC)

5.850-5.925 GHz V2V, V2R communication 802.11p protocol 7 channels, dedicated safety channel 6- 27 Mbps Up to 1000 m range

Applications Collision

warning/avoidance– Unseen vehicles– Approaching

congestions/hazards Traffic/road monitoring Emergency vehicle

warning, signal warning

Internet Access Traveler & Tourist

Assistance Entertainment

Mobile Distributed Computing Systems on the Road

RoadsideBase-Station

Roadside-to-vehiclecommunication

Vehicle-to-vehiclecommunication

Objectives

Challenges• Create realistic models for mobility by developing,

populating, and calibrate simulations specific to data for the Atlanta metropolitan area

• Develop simulation modeling tools for traffic, vehicle-to-vehicle, and vehicle-to-roadside communications to support the development and evaluation of future generation intelligent transportation systems

• Evaluate the performance limits of multi-hop vehicle-to-vehicle communication for realistic test conditions

Motivating question: Can networks composed of smart vehicles be used to collect and disseminate information in urban / rural transportation systems?

• Augment or replace infrastructure deployments

Spatial Propagation Problem

Spatial Propagation Problem: How fast can information propagate with vehicle forwarding?

Focus on V2V ad hoc networks (802.11) in order to understand the limitations of message forwarding

Observations One dimensional partitioned network Vehicle movement helps propagate information

Message

Vehicle Ad Hoc Networks

Dis

tanc

e

Time

Catch-up Mode

Forward Mode

Time-space Trajectory

Cyclic Process Partitioned Network Forward mode

Message forwarding within a partition

Catch-up mode Vehicle movement allows

message propagation between partitions

Time-space Trajectory

Time t0

Time t1

Time t2

Informed Vehicle

H

Uninformed Vehicle

Partition

Time t3

Analytic Models

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Flow Rate (vehicles/s)

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100s (

m) Sparse Netowork ModelDense Network ModelSimulation

Sparse network model -- Small partition size– Information propagation principally relies on vehicle movement.– Message propagation speed approaches maximum vehicle speed.

Dense network model -- Large partition size– Independent cycles– Renewal reward process

Reward: message propagation distance during each cycle

H. Wu, R. M. Fujimoto, G. Riley, “Analytical Models for Information Propagation in Vehicle-to-Vehicle Networks,” IEEE Vehicular Technology Conference, September 2004.

A single road with one way traffic Vehicle movement follows undisturbed traffic model

Run Time Infrastructure SoftwareFederation

managementPub/Sub

CommunicationSynchronization

(Time Management)

CORSIM QualNet

Traffic Simulator Comm. Simulator

Microscopic traffic simulation

Vehicle-to-vehicle and vehicle-to-infrastructure wireless communication

Distributed simulation over LANs and WANs

LAN/Internet

Integrated Distributed Simulations

Traffic Simulation Model(Guensler, Hunter, et al.)

• One-foot resolution United States Geological Survey (USGS) orthoimagery aerial photos used to code lanes, turn bay configurations, and turn bay lengths for each intersection

• Traffic volumes, signal control plans, geometric data, speed limits, etc., obtained from local transportation agencies

Traffic Corridor Study Area

I-75 and surrounding arterials in NW Atlanta

189 nodes (117 arterial, 72 freeway)

45 signalized nodes 365 one-way links (295

arterial, 70 freeway) 101.4 arterial miles 16.3 freeway miles (13.6

mainline, 2.7 ramp)

Model Calibration & Validation

Anomalous (simulated) delays observed at some locations– Field surveys completed at six intersections to calibrate model

Validation using instrumented vehicle fleet collecting second-by-second speed and acceleration data

– GPS data from 7 AM to 8 AM peak used– 591 weekday highway trips (Feb.-May 2003)– 601 weekday highway trips (July-Sept. 2003)

Comparison of Measured and Simulated Vehicle Speeds

0

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Arterials

Spee

d(M

PH

)

Measured Simulated

H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L. Guensler, J. Ko, “Simulated Vehicle-to-Vehicle Message Propagation Efficiency on Atlanta’s I-75 Corridor,” Journal of the Transportation Research Board, 2005.

Mobility-Centric Data Dissemination for Vehicle Networks (MDDV)

No end-to-end connection assumption– Opportunistic forwarding [Fall, SIGCOMM 2003]– Trajectory-based forwarding [Niculescu & Nath, Mobicom’03]– Geographic forwarding [Mauve, IEEE Networks 15 (6)]

Compute trajectory to destination region Group forwarding: Set of vehicles holding message “closest” to

destination region actively forward message toward destination Group membership

– Vehicle stores last known location/time of message head candidate; forwards information with message

– Join group if (1) moving toward destination along trajectory and (2) reach estimated head location (or closer) less than Tl time units after head

– Leave group if (1) leave trajectory or (2) receives same message indicating head is closer to the destination

H. Wu, R. M. Fujimoto, R. Guensler, M. Hunter, “MDDV: Mobility-Centric Data Dissemination Algorithm for Vehicular Networks,” ACM Workshop on Vehicular Ad Hoc Networks (VANET), October 2004.

Propagation Delay (simulation)

Delay to propagate message 6 miles southbound on I-75 Relatively heavy traffic conditions Penetration ratio: fraction of instrumented vehicles

End-to-End Propagation Delay

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80

100

120

0.05 0.1 0.15 0.2 0.25Penetration Ratio

Dela

y (

s)

EVENING PEAK

Penetration Ratio

H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L. Guensler, J. Ko, “Simulated Vehicle-to-Vehicle Message Propagation Efficiency on Atlanta’s I-75 Corridor,” Journal of the Transportation Research Board, 2005.

End-to-End Delay Distribution

Delay to propagate message 6 miles along I-75 (southbound)

Heavy (evening peak) and light (nighttime) traffic

Penetration ratio: fraction of instrumented vehicles

Significant fraction of messages experience a large delay

Evening Peak End-to-End Delay Distribution

0%

20%

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100%

< 1 s 1 s - 3 s 3 s - 5 s 5 s - 8 s 8 s - 15 s > 15 sE2E Delay

Penetration = 0.1 Penetration = 0.2

Nighttime End-to-End Delay Distribution

0%

20%

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100%

< 1 s 1 s - 3 s 3 s - 5 s 5 s - 8 s 8 s - 15 s > 15 sE2E Delay

Penetration = 0.5 Penetration = 1

Mobility-centric Data Dissemintation for Vehicle Networks (MDDV)

MDDV: opportunistic forwarding algorithm

Morning rush hour traffic

Propagate information to destination 4 miles away

Delivery ratio: fraction delivered before expiration time (480 seconds)

Large variation in delay observed0

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Penetration Ratio

Del

ay (

s)

Avg DelayMax DelayMin Delay

Penetration Ratio

Delivery Ratio

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ive

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Field Experiments: Goals

Characterize communication performance in a realistic vehicular environment

Identify factors affecting communication Lay the groundwork of realizing

communication services Demonstrate and assess the benefits of

multi-hop forwarding

When the Rubber Meets the Road

In-vehicle systems– Laptop– GPS receiver– 802.11b wireless card– External antenna

Roadside station using the same equipment

Software– Iperf w/ GPS readings– Data forwarding module

Location– I-75 in northwest Atlanta,

between exits 250 and 255 Un-congested traffic Clear weather

H. Wu, M. Palekar, R. M. Fujimoto, R. Guensler, M. Hunter, J. Lee, J. Ko, “An Empirical Study of Short Range Communications for Vehicles,” ACM Workshop on Vehicular Ad Hoc Networks (VANET), September 2005.

Vehicle-to-Roadside (V2R) Communication

-2440m

Peachtree Battle Bridge

Exit 254

Exit 255

-1400m

-2270m

-700m

0

Exit 252B

Exit 252A

Exit 250

600m

2500m3000m

3370m4250m

4570m

4700m

1000m

Trees

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S

North Bridge South

0%10%20%30%40%50%60%70%80%90%

100%

-1500 -1000 -500 0 500 1000 1500 2000Distance (m)

Su

ccess R

ati

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NorthboundSouthbound

Pea

chtr

ee B

attle

Exi

t 2

54

Tre

esTre

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V2R Performance

Success Ratio - Percentage of packet transmissions received by the receiver

View facing south

View facing north

north

Vehicle-to-Vehicle (V2V) Communication

-2440m

Peachtree Battle Bridge

Exit 254

Exit 255

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Exit 252A

Exit 250

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V2V Performance (Southbound)

North Bridge South

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-3000 -2000 -1000 0 1000 2000 3000 4000 5000

Sender Location (m)

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v2v Distance = 150mv2v Distance = 300m

North Bridge South

0%10%20%30%40%50%60%70%80%90%

100%

-3000 -2000 -1000 0 1000 2000 3000 4000 5000Sender Location (m)

Su

cces

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atio

n

v2v Distance = 400mv2v Distance = 700m

-2440m

Peachtree Battle Bridge

Exit 254

Exit 255

-1400m

-2270m

-700m

0

Exit 252B

Exit 252A

Exit 250

600m

2500m3000m

3370m4250m

4570m

4700m

1000m

Trees

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Multi-hop Communication

-2440m

Peachtree Battle Bridge

Exit 254

Exit 255

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Exit 252A

Exit 250

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Performance Comparison

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-1200 -700 -200 300 800 1300 1800Distance (m)

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Single hop, northboundSingle hop, southboundTw o hops, southbound, v2v distance = 340mTw o hops, southbound, v2v distance = 180mTw o hops, northbound, v2v distance = 320mTw o hops, northbound, v2v distance = 460m

Summary

Mobile distributed computing systems on the road are coming– Safety likely to be the initial primary application– System monitoring also early application– Enable wide variety of commercial applications

Simulation methodology is essential to design vehicle networks, e.g., to determine a necessary penetration ratio for effective communication

– Realistic evaluation of vehicular networks requires careful consideration of mobility

– Federating simulation models can play a key role Vehicle-to-vehicle communication can be used to propagate

information for applications that can tolerate some data loss and/or unpredictable delays

– V2V communication provides a means to supplement infrastructure-based communications

– Must weigh benefits against implementation complexity

Future Directions

Architectures of the future will likely include a mix of technologies– WWAN, WLAN (e.g., DSRC), V2V– Roadside computing stations, Internet gateways

Transition from data draught to data flood will create new technical challenges– Management of bandwidth– Management of computing resources; vehicle grids– Data challenges: cleaning, aggregating, mining

Wireless Infrastructure Technologies

Wireless Technologies (in order of decreasing coverage)– Wireless Wide Area Networks (WWAN)

Cellular networks (2nd Generation, 2.5G, 3G, 4G) High coverage (up to 20 km) Low bandwidth: Verizon BroadbandAccess provides up to 2 Mbps

upstream, the Cingular Edge provides up to 170 Kbps upstream

– Wireless Metro Area Networks (WMAN) Fixed broadband wireless link (WiMAX -- IEEE 802.16)

– Wireless Local Area Networks (WLAN) IEEE802.11x (T-mobile hop spots) High bandwidth: 802.11b provides 11 Mbps, 802.11 a/g offers 54

Mbps Low coverage (250m)

– Wireless Personal Area Networks (WPAN) Bluetooth

Larger coverage -> Increased cost, low bandwidth

Network Architecture Options

WWAN BS

BackboneBackbone

WWAN last hop: broad coverage, limited capacity

WLAN AP WLAN AP

WLAN access points to improve capacity– In addition to, or rather than WWAN– Many access points vs. coverage tradeoff

Add WLAN multihop (vehicle-to-vehicle) communication– Extend WLAN coverage, reduce number of access points– Requires presence of vehicles

Required WWAN Capacity

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0 0.2 0.4 0.6 0.8 1Penetration Ratio

(Kbps)

WWAN last-hopWWAN last-hop + WLAN last-hopWWAN last-hop + 2-hop WLANWWAN last-hop + 3-hop WLAN

Required WWAN Capacity

5.6 Mbps

vehicle data rate = 16Kbps (a modest value)

7 WLAN access points (for hybrid architectures)

WWAN does not scale well. A hybrid architecture can increase the system capacity

and reduce the WWAN data traffic load.

28.8 Mbps

Not Linear

Required WLAN Access Points to Provide Sufficient Capacity

# of Access Points

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Penetration Ratio

WLAN last-hopWWAN last-hop + WLAN last-hopWWAN last-hop + 2-hop WLANWWAN last-hop + 3-hop WLAN

vehicle data rate = 16 Kbps, road length = 11,000 m, number of instrumented vehicles = 1800 * penetration ratio, aggregated WWAN data rate = 6 Mbps

Fixed number required for WLAN last-hop architecture Hybrid architecture can greatly reduce the number Multi-hop forwarding can reduce number further

Coverage range: expected length of road segment within which vehicles can access a WLAN access point using at most m hops

Normalized WLAN Coverage

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Penetration Ratio

hops = 1analysis, hop limit = 2analysis, hop limit = 3simulation, hop limit = 2simulation, hop limit = 3

WLAN Coverage Range

0.025

Instrumented vehicles will likely be sufficiently dense Further coverage increase minor when instrumented vehicle

density reaches a saturation value (penetration ratio 0.3 above)

Design Implication

Vehicular network design requires:– Careful assessment of cost / performance tradeoffs– Addressing changing vehicle traffic conditions

Multi-hop forwarding– Pro: extend coverage -> reduce number of access points -

> reduce cost– Con: reduced channel capacity, additional system

complexity (routing, billing & security)– Questionable except in places with cost or other

constrains– Voluntary cooperation is beneficial in improving

communication performance

Design Implication (Cont.)

Continuous connectivity– WWAN: does not scale well.– WLAN last-hop: simple, easy deployment, provide high

throughput, require a large number of access points– WWAN + WLAN: increase system capacity

Intermittent connectivity– WLAN-based solution– Whether to allow multi-hop forwarding is governed by a

tradeoff between cost and system complexity.– Connectivity probability in every location can be estimated

using our models. Deal with vehicle traffic dynamics

– Overprovision (for hard-to-predict variations)– Adaptation (for predictable variations)

References

H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L. Guensler, J. Ko, “Simulated Vehicle-to-Vehicle Message Propagation Efficiency on Atlanta’s I-75 Corridor,” Journal of the Transportation Research Board, 2005.

H. Wu, R. M. Fujimoto, R. Guensler, M. Hunter, “An Architecture Study of Infrastructure-Based Vehicular Networks,” Eighth ACM/IEEE International Symposium on Modeling, Analysis, and Simulation of Wireless and Mobile Systems,” October 2005.

R. M. Fujimoto, H. Wu, R. Guensler, M. Hunter, “Evaluating Vehicular Networks: Analysis, Simulation, and Field Experiments,” Cooperative Research in Science and Technology (COST) Symposium on Modeling and Simulation in Telecommunications, September 2005.

H. Wu, M. Palekar, R. M. Fujimoto, R. Guensler, M. Hunter, J. Lee, J. Ko, “An Empirical Study of Short Range Communications for Vehicles,” ACM Workshop on Vehicular Ad Hoc Networks (VANET), September 2005.

Lee, J., M. Hunter, J. Ko, R. Guensler, and H.K. Kim, "Large-Scale Microscopic Simulation Model Development Utilizing Macroscopic Travel Demand Model Data", Proceedings of the 6th Annual Conference of the Canadian Society of Civil Engineers, Toronto, Ontario, Canada, June 2005.

H. Wu, M. Palekar, R. M. Fujimoto, J. Lee, J. Ko, R. Guensler, M. Hunter, “Vehicular Networks in Urban Transportation Systems,” National Conference on Digital Government Research, May 2005

H. Wu, R. M. Fujimoto, R. Guensler, M. Hunter, “MDDV: Mobility-Centric Data Dissemination Algorithm for Vehicular Networks,” ACM Workshop on Vehicular Ad Hoc Networks (VANET), October 2004.

H. Wu, R. M. Fujimoto, G. Riley, “Analytical Models for Information Propagation in Vehicle-to-Vehicle Networks,” IEEE Vehicular Technology Conference, September 2004.

B. Fitzgibbons, R. M. Fujimoto, R. Guensler, M. Hunter, A. Park, H. Wu, “Simulation-Based Operations Planning for Regional Transportation Systems,” National Conference on Digital Government Research, pp. 175-176, May 2004.

B. Fitzgibbons, R. M. Fujimoto, R. Guensler, M. Hunter, A. Park, H. Wu, “Distributed Simulation Testbed for Intelligent Transportation System Design and Analysis,” National Conference on Digital Government Research, pp. 308-309, May 2004.