Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

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

Outline Introduction Motivation Problem Definition CASCADE Local View Component Extended View Component Data Security Component Data Dissemination Component Summary

Introduction What is a Vehicular Ad-Hoc Network (VANET)?

Introduction Communication Models In VANET: Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) Hybrid of V2V and V2I

Introduction Assumptions Transceiver GPS (D-GPS) Set of Public/Private Key Pairs Tamper-Proof Device Laser Rangefinder

Motivation VANET Applications: Safety Applications Informational Applications Entertainment Applications Collision Warning Congestion Notification Music/Movie Sharing

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

Motivation The Common Data Characteristics: Refresh or update rate Accuracy Volume Each category of applications needs a customized version

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)

Problem Definition How to securely and efficiently provide each VANET application with a customized version of the vehicular data based on its category.

CASCADE CASCADE Cluster-based Accurate Syntactic Compression of Aggregated Data in VANETs

CASCADE Major Framework Components Local View Extended View Data Security Data Dissemination

CASCADE Local View Receiving Aggregated Frame Broadcasting Aggregated Frame Receiving Primary Frame Broadcasting Primary Frame Data Flow in CASCADE

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

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

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)

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%

Local View Component What is the cluster dimension? Smallest aggregated frame Longest local view length

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

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

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.

Extended View Component Build and Maintain Extended View Determine if two vehicles match Determine if two intersecting regions match

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%

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

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?

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

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

Extended View Component Matching percentage threshold vs. extended view length Safety Applications Informational Applications Entertainment Applications

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)

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

Data Dissemination Broadcast DSRC  300 m A

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]

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

Data Dissemination Re-broadcast Probabilistic- IVG (p-IVG)

Data Dissemination p-IVG Evaluation Metrics MAC Delay Reception Rate Backoff Percentage Dissemination Delay and Hop Count Redundancy Factor Coverage Percentage

Data Dissemination Because using p-IVG reduces the media contention, the reception rate increases

Data Dissemination p-IVG takes less time to send the messages further using smaller number of hops

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.

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.

Data Dissemination IVG Number of Extra Copies

Data Dissemination p-IVG Number of Extra Copies

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.

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

Data Dissemination Component Sparse Traffic Clustered Traffic

Data Dissemination Component Yah rab Extended View Length (km)

Data Dissemination Component On-Demand Vehicular Gap-Bridging (OD-V-GB) Broadcasting GBR Messages Handling Received Aggregated Frames On Demand Broadcasting

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%

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

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?

Data Dissemination Component Extended View Length (km)

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

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

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

Summary Data Security Component a light-weight position verification technique that quickly detects false data with very low false positives

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

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

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.

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.

Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

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