An Architecture Study of Ad-Hoc Vehicle Networks

1
An Architecture Study of Ad-Hoc Vehicle Networks
Richard Fujimoto Hao Wu Computational Science
Engineering College of Computing
Randall Guensler Michael Hunter Civil and
Environmental Engineering College of Engineering
Georgia Institute of Technology
2
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 gt 50 hazardous air pollutants in
  U.S., up to 90 of the carbon monoxide in urban
  air

3
Intelligent Transportation Systems

• 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

4
Current Trends

• Smart Vehicles
• On-board GPS, digital maps
• Vehicle, environment sensors
• Significant computation, storage, communication
  capability
• Not power constrained

5
Mobile Distributed Computing Systems on the Road
Roadside Base- Station
Vehicle-to-vehicle communication

• Applications
• Collision warning/avoidance
• Unseen vehicles
• Approaching congestions/hazards
• Traffic/road monitoring
• Emergency vehicle warning, signal warning
• Internet Access
• Traveler Tourist Assistance
• Entertainment

Roadside-to-vehicle communication
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Objectives

• 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

• 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

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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

8
Vehicle Ad Hoc Networks
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-space Trajectory
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Analytic Models
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

• 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

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Integrated Distributed Simulations
CORSIM
QualNet

• Microscopic traffic simulation
• Vehicle-to-vehicle and vehicle-to-infrastructure
  wireless communication
• Distributed simulation over LANs and WANs

Traffic Simulator
Comm. Simulator
LAN/Internet
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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

12
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)

13
Model Calibration Validation
H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L.
Guensler, J. Ko, Simulated Vehicle-to-Vehicle
Message Propagation Efficiency on Atlantas I-75
Corridor, Journal of the Transportation Research
Board, 2005.

• 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)

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Mobility-Centric Data Dissemination for Vehicle
Networks (MDDV)
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.

• No end-to-end connection assumption
• Opportunistic forwarding Fall, SIGCOMM 2003
• Trajectory-based forwarding Niculescu Nath,
  Mobicom03
• 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

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Propagation Delay (simulation)
H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L.
Guensler, J. Ko, Simulated Vehicle-to-Vehicle
Message Propagation Efficiency on Atlantas I-75
Corridor, Journal of the Transportation Research
Board, 2005.

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

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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

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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 observed

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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

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When the Rubber Meets the Road
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.

• 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

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Vehicle-to-Roadside (V2R) Communication
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V2R Performance
Success Ratio - Percentage of packet
transmissions received by the receiver
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Vehicle-to-Vehicle (V2V) Communication
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V2V Performance (Southbound)
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Multi-hop Communication
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Performance Comparison
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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

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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

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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 -gt Increased cost, low bandwidth

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Network Architecture Options
Backbone
WWAN BS

• WWAN last hop broad coverage, limited capacity

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Required WWAN Capacity
vehicle data rate 16Kbps (a modest value) 7
WLAN access points (for hybrid architectures)
28.8 Mbps
5.6 Mbps
Not Linear

• WWAN does not scale well.
• A hybrid architecture can increase the system
  capacity and reduce the WWAN data traffic load.

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Required WLAN Access Points to Provide Sufficient
Capacity
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

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

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Design Implication

• Vehicular network design requires
• Careful assessment of cost / performance
  tradeoffs
• Addressing changing vehicle traffic conditions
• Multi-hop forwarding
• Pro extend coverage -gt reduce number of access
  points -gt 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

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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)

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Thanks for your attention.
Questions?
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References

• H. Wu, J. Lee, M. Hunter, R. M. Fujimoto, R. L.
  Guensler, J. Ko, Simulated Vehicle-to-Vehicle
  Message Propagation Efficiency on Atlantas 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.