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
0
2000
4000
6000
8000
10000
12000
14000
16000
0.0
5
0.2
0.3
5
0.5
0.6
5
0.8
0.9
5
1.1
1.2
5
Flow Rate (vehicles/s)
Dis
tan
ce f
or
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
10
20
30
40
50
60
70
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
0
20
40
60
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%
40%
60%
80%
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%
40%
60%
80%
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
100
200
300
400
500
0.1 0.2 0.3 0.4 0.5
Penetration Ratio
Del
ay (
s)
Avg DelayMax DelayMin Delay
Penetration Ratio
Delivery Ratio
0
0.2
0.4
0.6
0.8
1
1.2
0.1 0.2 0.3 0.4 0.5
Penetration Ratio
Del
ive
ry R
atio
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
N
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
o
NorthboundSouthbound
Pea
chtr
ee B
attle
Exi
t 2
54
Tre
esTre
es
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
-1400m
-2270m
-700m
0
Exit 252B
Exit 252A
Exit 250
600m
2500m3000m
3370m4250m
4570m
4700m
1000m
Trees
N
S
V2V Performance (Southbound)
North Bridge South
0%
20%
40%
60%
80%
100%
-3000 -2000 -1000 0 1000 2000 3000 4000 5000
Sender Location (m)
Su
cces
s R
atio
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
s R
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
N
S
Multi-hop 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
N
S
Performance Comparison
0%
20%
40%
60%
80%
100%
-1200 -700 -200 300 800 1300 1800Distance (m)
Su
cces
s R
atio
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
0
5000
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15000
20000
25000
30000
35000
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
0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
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
0
0.5
1
1.5
2
2.5
3
3.5
0 0.2 0.4 0.6 0.8 1
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.