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An Architecture Study of Ad-Hoc Vehicle Networks

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. > 3M.

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An Architecture Study of Ad-Hoc Vehicle Networks

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  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. > 3M • Disproportionate increase in car ownership relative to population growth in China, India 1M - 3M 0.5 - 1M < 0.5 Million Source: 2005 Annual Urban Mobility Report (http://mobility.tamu.edu) Texas Natural Resource Conservation Commission (http://www.tnrcc.state.tx.us/air) 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

  3. www.georgia-navigator.com 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. 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 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 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 Roadside-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

  6. 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

  7. Message 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

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

  10. Integrated Distributed Simulations Run Time Infrastructure Software Federation management Pub/Sub Communication Synchronization (Time Management) • Microscopic traffic simulation • Vehicle-to-vehicle and vehicle-to-infrastructure wireless communication • Distributed simulation over LANs and WANs CORSIM QualNet Traffic Simulator Comm. Simulator LAN/Internet

  11. 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 • 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) 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.

  14. 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, 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

  15. Penetration Ratio Propagation Delay (simulation) • Delay to propagate message 6 miles southbound on I-75 • Relatively heavy traffic conditions • Penetration ratio: fraction of instrumented vehicles 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.

  16. 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

  17. Delivery Ratio Penetration Ratio 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

  18. 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

  19. 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.

  20. Exit 255 -2440m -2270m -1400m Exit 254 -700m Peachtree Battle Bridge 0 600m Trees Exit 252B N Exit 252A 1000m 2500m S 3000m Exit 250 3370m 4700m 4250m 4570m Vehicle-to-Roadside (V2R) Communication

  21. View facing north View facing south north Trees Exit 254 Trees Peachtree Battle V2R Performance Success Ratio - Percentage of packet transmissions received by the receiver

  22. Exit 255 -2440m -2270m -1400m Exit 254 -700m Peachtree Battle Bridge 0 600m Trees Exit 252B N Exit 252A 1000m 2500m S 3000m Exit 250 3370m 4700m 4250m 4570m Vehicle-to-Vehicle (V2V) Communication

  23. Exit 255 -2440m -2270m -1400m Exit 254 -700m Peachtree Battle Bridge 0 600m Trees Exit 252B N Exit 252A 1000m 2500m S 3000m Exit 250 3370m 4700m 4250m 4570m V2V Performance (Southbound)

  24. Exit 255 -2440m -2270m -1400m Exit 254 -700m Peachtree Battle Bridge 0 600m Trees Exit 252B N Exit 252A 1000m 2500m S 3000m Exit 250 3370m 4700m 4250m 4570m Multi-hop Communication

  25. Performance Comparison

  26. 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

  27. 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

  28. 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

  29. 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 Network Architecture Options Backbone WWAN BS • WWAN last hop: broad coverage, limited capacity

  30. 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.

  31. 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

  32. 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)

  33. 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

  34. 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)

  35. Thanks for your attention. Questions?

  36. 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.

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