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Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks
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Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

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Khaled Ibrahim's PhD Defense Slides …

Khaled Ibrahim's PhD Defense Slides
Department of Computer Science
Old Dominion University
February 21, 2011

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  • Prepare the pros and cons for each model.
  • 1- Find an example for the application specific data 2- I need to show that the common data is being sent more frequently while the application specific data is occasionally
  • I have to mention that each category of applications needs different data refresh rate, accuracy and volume and support my argument by examples to make it easy for comprehend.
  • I have to mention that each category of applications needs different data refresh rate, accuracy and volume and support my argument by examples to make it easy for comprehend.
  • Transcript

    • 1. PhD Defense Exam Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks Khaled Ibrahim Advisor: Dr. Michele C. Weigle Computer Science Department Old Dominion University, Norfolk, VA 23529 February 21, 2011
    • 2. Outline
      • Introduction
      • Motivation
      • Problem Definition
      • CASCADE
        • Local View Component
        • Extended View Component
        • Data Security Component
        • Data Dissemination Component
      • Summary
    • 3. Introduction
      • What is a Vehicular Ad-Hoc Network (VANET)?
    • 4. Introduction
      • Communication Models In VANET:
        • Vehicle-to-Vehicle (V2V)
        • Vehicle-to-Infrastructure (V2I)
        • Hybrid of V2V and V2I
    • 5. Introduction
      • Assumptions
        • Transceiver
        • GPS (D-GPS)
        • Set of Public/Private Key Pairs
        • Tamper-Proof Device
        • Laser Rangefinder
    • 6. Motivation
      • VANET Applications:
        • Safety Applications
        • Informational Applications
        • Entertainment Applications
        • Collision Warning
        • Congestion Notification
        • Music/Movie Sharing
    • 7. Motivation
      • Data Needed by VANET Applications:
        • Common Data
          • Vehicle Location
          • Vehicle Speed
        • Application Specific Data
          • Collision Location
          • Congestion Location
          • Songs/Movies to be shared
        • Collision Warning
        • Congestion Notification
        • Music/Movie Sharing
    • 8. Motivation
      • The Common Data Characteristics:
        • Refresh or update rate
        • Accuracy
        • Volume
      • Each category of applications needs a customized version
    • 9. Motivation
      • The Scalability Problem Example
        • N 1 Safety Applications
        • N 2 Informational Applications
        • N 3 Entertainment Applications
        • N 1 *10 + N 2 *3 + N 3 * 1
        • 10 + 3 + 1 (Better Solution)
        • 10 (The Best Solution)
    • 10. Problem Definition How to securely and efficiently provide each VANET application with a customized version of the vehicular data based on its category.
    • 11. CASCADE CASCADE Cluster-based Accurate Syntactic Compression of Aggregated Data in VANETs
    • 12. CASCADE
      • Major Framework Components
        • Local View
        • Extended View
        • Data Security
        • Data Dissemination
    • 13. CASCADE Local View Receiving Aggregated Frame Broadcasting Aggregated Frame Receiving Primary Frame Broadcasting Primary Frame Data Flow in CASCADE
    • 14. Contributions
      • a lossless data compression technique based on differential encoding that has compression ratio of 86%
      • a syntactic data aggregation mechanism that can represent the vehicular data in a local view of length 1.5km in one single MAC frame
      • a probabilistic data dissemination technique that alleviates the spatial broadcast storm problem and effectively uses the bandwidth to disseminate data to distant areas in a short amount of time in addition to having less redundancy and reasonable coverage than other techniques.
      • a mechanism for recovering from the communication discontinuity problem in short time based on the traffic density in the opposite direction
      • an investigation of the possible data structures for representing the vehicular data in a searchable format
      • a parametric mechanism for matching the vehicular data and providing a customized version of the data that satisfies certain characteristics based on the parameter value
      • a light-weight position verification technique that quickly detects false data with very low false positives
    • 15. CASCADE
    • 16. Local View Component
    • 17. Local View Component
      • What is Local View?
      • Local View Component Responsibility?
        • Maintain an accurate Local View
          • Add new vehicle
          • Update vehicles locations
          • Delete out of scope vehicles
    • 18. Local View Component
      • Local View Component Responsibility?
      • Compress and aggregate the vehicular data in the local view and compose one aggregated frame that fits into a single MAC frame (2312 B)
    • 19. Local View Component
      • Data Compression
        • Differential coding
        • CASCADE-Max
      • Vehicular Data Compression
        •  X (5 Bits)
        •  Y ( 7 Bits)
        •  Speed (5 Bits)
      • Compression ratio is 86%
    • 20. Local View Component
      • What is the cluster dimension?
        • Smallest aggregated frame
        • Longest local view length
    • 21. Local View Component
      • Determined best cluster size experimentally
      • Cluster sizes
        • Cluster length (62m,126m, 254m and 510m)
        • Cluster width (1 lane, 2 lanes, 4 lanes )
      • Vehicular densities
        • low, medium and high
      • Vehicular distribution
        • worst distribution (uniform distribution)
        • best distribution (clustered distribution)
        • expected distribution
    • 22. Local View Component
    • 23. Local View Component
      • Local View Component:
      • Maintain an accurate view for the traffic ahead for short distances (1.5 km)
      • Compress and aggregate the local view data to fit into a single MAC frame
    • 24. Extended View Component
    • 25. Extended View Component
      • Extended View Component Responsibility?
        • Build and maintain the extended view
        • Customize the extended view based on the predefined settings for each registered application.
    • 26. Extended View Component
    • 27. Extended View Component
      • Build and Maintain Extended View
        • Determine if two vehicles match
        • Determine if two intersecting regions match
    • 28. Extended View Component
      • Determine if two vehicles match
        • What threshold of difference for two vehicles should we accept as matching?
          • Evaluated experimentally through simulation
          • To maximize true positive and true negative and minimize false positive and false negative, use vehicle difference threshold of 16%
    • 29. Extended View Component
      • Determine if two intersecting regions match
        • Does the data structure used to represent the regions matter?
          • implemented comparison with graph structure and KD Tree structure
          • KD Tree is 22% faster than graph, but uses 39% more memory
    • 30. Extended View Component
      • Customize the extended view
        • Matching percentage - % of vehicles in the intersecting regions that match
        • What matching % is required to accept the received aggregated frame?
    • 31. Extended View Component
      • What matching % is required to accept the received aggregated frame?
      • Small matching %
        • more aggregated frames will be accepted
        • longer extended view
        • may be less accurate
    • 32. Extended View Component
      • What matching % is required to accept the received aggregated frame?
      • Large matching %
        • fewer aggregated frames will be accepted
        • shorter extended view
        • may be more accurate
    • 33. Extended View Component Matching percentage threshold vs. extended view length
        • Safety Applications
        • Informational Applications
        • Entertainment Applications
    • 34. Extended View Component
      • Extended View Component:
      • Build and maintain an extended view with maximum accuracy
      • Customize the extended view based on the application settings (refresh rate, accuracy, view length)
    • 35. Data Dissemination Component
    • 36. Data Dissemination Component
      • Disseminate data to distant areas in a short amount of time in addition to having less redundancy and reasonable coverage than other techniques
      • Recover from the communication discontinuity problem in short time based on the traffic density in the opposite direction
    • 37. Data Dissemination
      • Broadcast
      • DSRC  300 m
      A
    • 38. Data Dissemination
      • Re-broadcast
        • Flooding [Ni –MOBICOM’99]
        • Weighted p-Persistence [Wisitpongphan-IWC’07]
        • Slotted 1-Persistence [Wisitpongphan-IWC’07]
        • Slotted p-Persistence [Wisitpongphan-IWC’07]
        • Inter-Vehicle Geocast (IVG) [Bachir –VTC’03]
    • 39. Data Dissemination
      • Re-broadcast
        • Inter-Vehicle Geocast (IVG)
          • i is the message sender
          • j is the message receiver
          • D ij is the distance between vehicle i and vehicle j
          • T ij is the re-broadcast timer
    • 40. Data Dissemination
      • Re-broadcast
        • Probabilistic- IVG (p-IVG)
    • 41. Data Dissemination
      • p-IVG Evaluation
        • Metrics
          • MAC Delay
          • Reception Rate
          • Backoff Percentage
          • Dissemination Delay and Hop Count
          • Redundancy Factor
          • Coverage Percentage
    • 42. Data Dissemination Because using p-IVG reduces the media contention, the reception rate increases
    • 43. Data Dissemination p-IVG takes less time to send the messages further using smaller number of hops
    • 44. Data Dissemination
      • Redundancy Factor
        • The optimal case is to receive each message once  redundancy factor = 0
        • Realistically 1 the minimum redundancy factor = 0.4
      [1] S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, “The broadcast storm problem in a mobile ad hoc network,” in Proceedings of ACM Mobicom , Seattle, WA, Aug. 1999, pp. 151–162.
    • 45. Data Dissemination
      • Coverage %
        • Definition: % of vehicles within the transmission range that received the message or any of its rebroadcast.
        • The optimal dissemination technique should have 100% coverage.
    • 46. Data Dissemination IVG Number of Extra Copies
    • 47. Data Dissemination p-IVG Number of Extra Copies
    • 48. Data Dissemination
      • p-IVG Summary
      • It can disseminate data to distant areas in a short amount of time in addition to, having less redundancy and reasonable coverage than IVG.
    • 49. Data Dissemination Component
      • Communication Discontinuity
      • We have been assuming that the distance between any two communicating vehicles will not be greater that 250m.
      • Removing this assumption results in possible breaks in communication
    • 50. Data Dissemination Component Sparse Traffic Clustered Traffic
    • 51. Data Dissemination Component
      • Yah rab
      Extended View Length (km)
    • 52. Data Dissemination Component
      • On-Demand Vehicular Gap-Bridging (OD-V-GB)
      Broadcasting GBR Messages Handling Received Aggregated Frames On Demand Broadcasting
    • 53. Data Dissemination Component
      • On-Demand Vehicular Gap-Bridging (OD-V-GB)
      Handling Received Aggregated Frames
      • Background process to build an extended view for the opposite direction (2 sec aggregated frames repository)
      • Matching Percentage Threshold is 0%
    • 54. Data Dissemination Component
      • On-Demand Vehicular Gap-Bridging (OD-V-GB)
      Broadcasting GBR Messages
      • Timer to track the most recent message received from traffic ahead
      • If timer expires  Discontinuity or Gap detected
      • Then send a GBR request
    • 55. Data Dissemination Component
      • On-Demand Vehicular Gap-Bridging (OD-V-GB)
      On Demand Broadcasting
      • Once they get in contact with a vehicle in the direction requesting help, they broadcast their opposite direction extended view in one aggregated frame
      • What is the impact of the vehicular density?
    • 56. Data Dissemination Component Extended View Length (km)
    • 57. Data Dissemination
      • OD-V-GB Summary:
      • It can recover from the communication discontinuity problem in short time based on the traffic density in the opposite direction
    • 58. Data Dissemination Component
    • 59. Summary
      • Local View Component
        • a lossless data compression technique with compression ratio of 86%
        • a syntactic data aggregation mechanism that can represent the vehicular data in a 1.5km area in single MAC frame
    • 60. Summary
      • Extended View Component
        • an investigation of the possible data structures for representing the vehicular data in a searchable format
        • a parametric mechanism for matching the vehicular data and providing a customized extended view
    • 61. Summary
      • Data Security Component
        • a light-weight position verification technique that quickly detects false data with very low false positives
    • 62. Summary
      • Data Dissemination Component
        • a probabilistic data dissemination technique that
          • alleviates the spatial broadcast storm problem
          • disseminate data to distant areas in a short amount of time in addition to having less redundancy and reasonable coverage than other techniques.
        • a mechanism for recovering from the communication discontinuity problem in short time based on the traffic density in the opposite direction
    • 63. Summary
      • Case Studies
        • CASCADE-Based Advertising System
        • CASCADE-Based Merge Assistant System
      • VANET Simulator
        • Application-aware Simulator SWANS with Highway mobility (ASH)
        • Details are in the dissertation
        • Informational Applications
        • Entertainment Applications
    • 64. Summary
      • Local View:
        • K. Ibrahim and M. C. Weigle. Accurate data aggregation for VANETs (poster). In Proceedings of ACM VANET, pages 71-72, Montreal, Canada, Sept. 2007.
        • K. Ibrahim , M. C. Weigle. Towards an Optimized and Secure CASCADE for Data Aggregation in VANETs (poster). In Proceedings of ACM VANET, pages 84-85, San Francisco, CA, Sept. 2008.
        • K. Ibrahim and M. C. Weigle. Optimizing CASCADE data aggregation for VANETs. In Proceedings of the IEEE MoVeNet, pages 724-729, Atlanta, GA, Sept. 2008.
        • K. Ibrahim and M. C. Weigle. CASCADE: Cluster-based accurate syntactic compression of aggregated data in VANETs. In Proceedings of IEEE AutoNet, New Orleans, LA, Dec. 2008.
    • 65. Summary
      • Data Dissemination:
        • K. Ibrahim , M. C. Weigle. “p-IVG: Probabilistic Inter-Vehicle Geocast for Dense Vehicular Networks”. In Proceedings of the IEEE VTC- Spring . Barcelona, Spain, Apr. 2009
      • Security:
        • K. Ibrahim , M. C. Weigle. Securing CASCADE Data Aggregation for VANETs. Poster in IEEE MoVeNet, Atlanta, GA, Sept. 2008.
        • K. Ibrahim and M. C. Weigle. Light-weight laser-aided position verification for CASCADE. In Proceedings of the WAVE, Dearborn, MI, Dec. 2008.
      • Simulation:
        • K. Ibrahim , M. C. Weigle. ASH: Application-aware SWANS with Highway mobility. In Proceedings of IEEE MOVE, Phoenix, AZ, Apr. 2008.
    • 66. Questions
    • 67. Thanks

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