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Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

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

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Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

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

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