Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks

PhD Defense ExamPhD Defense Exam

Khaled Ibrahim

Advisor: Dr. Michele C. Weigle

Computer Science Department

Old Dominion University,

Norfolk, VA 23529

February 21, 2011 1

OutlineOutline

• Introduction

• Motivation

• Problem Definition

• CASCADE– Local View Component– Extended View Component– Data Security Component– Data Dissemination Component

• Summary 2

IntroductionIntroduction

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

• Communication Models In VANET:

• Vehicle-to-Vehicle (V2V)

• Vehicle-to-Infrastructure (V2I)

• Hybrid of V2V and V2I

• Assumptions

• Transceiver

• GPS (D-GPS)

• Set of Public/Private Key Pairs

• Tamper-Proof Device

• Laser Rangefinder

MotivationMotivation

• VANET Applications:

• Safety Applications

• Informational Applications

• Entertainment Applications

Collision Warning

Congestion Notification

Music/Movie Sharing

• 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

• The Common Data Characteristics:

• Refresh or update rate

• Accuracy

• Volume

• Each category of applications needs a customized version

• The Scalability Problem Example• N1 Safety Applications• N2 Informational Applications• N3 Entertainment Applications

• N1*10 + N2*3 + N3* 1

• 10 + 3 + 1 (Better Solution)

• 10 (The Best Solution)9

Problem DefinitionProblem Definition

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

CASCADECASCADE

CASCADECluster-based Accurate Syntactic Compression of Aggregated Data in VANETs

CASCADECASCADE

• Major Framework Components

• Local View

• Extended View

• Data Security

• Data Dissemination

CASCADECASCADE

Local View

Receiving Aggregated FrameBroadcasting Aggregated FrameReceiving Primary FrameBroadcasting Primary FrameData Flow in CASCADE

ContributionsContributions

• 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

Local View Component

Data Dissemination ComponentExtended View Component

Data Security Component

• 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

CASCADECASCADE

Local View ComponentLocal 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 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)

• Data Compression– Differential coding– CASCADE-Max

• Vehicular Data Compression X (5 Bits) Y ( 7 Bits) Speed (5 Bits)

• Compression ratio is 86%

• What is the cluster dimension?

• Smallest aggregated frame

• Longest local view length

• 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:

• 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 ComponentExtended 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.

• Build and Maintain Extended View

• Determine if two vehicles match

• Determine if two intersecting regions match

• 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%

• 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

• Customize the extended view

• Matching percentage - % of vehicles in the intersecting regions that match

• 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

• Large matching %

• fewer aggregated frames will be accepted

• shorter extended view

• may be more accurate

Matching percentage threshold vs. extended view length

Safety Applications

Informational Applications

Entertainment Applications

• 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 Data Dissemination ComponentComponent

• 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 DisseminationData Dissemination

• Broadcast

• DSRC 300 m

• 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]

• Re-broadcast

– Inter-Vehicle Geocast (IVG)

• i is the message sender

• j is the message receiver

• Dij is the distance between vehicle i and vehicle j

• Tij is the re-broadcast timer

DRTT ij

• Re-broadcast

– Probabilistic- IVG (p-IVG) •

DRTT ij

densityp

• p-IVG Evaluation– Metrics

• MAC Delay• Reception Rate• Backoff Percentage• Dissemination Delay and Hop Count• Redundancy Factor• Coverage Percentage

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

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

• Redundancy Factor– The optimal case is to receive each message once

redundancy factor = 0– Realistically1 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.

• 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.

46Number of Extra Copies

47Number of Extra Copies

• 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.

• 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

Sparse Traffic Clustered Traffic 50

• Yah rab

On-Demand Vehicular Gap-Bridging (OD-V-GB)

Broadcasting GBR Messages

Handling Received Aggregated Frames

On Demand Broadcasting

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%

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

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? 55

• OD-V-GB Summary:

• It can recover from the communication discontinuity problem in short time based on the traffic density in the opposite direction

SummarySummary

• 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

SummarySummary

• 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

SummarySummary

• Data Security Component

• a light-weight position verification technique that quickly detects false data with very low false positives

SummarySummary

• 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

SummarySummary

• 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 63

Informational Applications

Entertainment Applications

SummarySummary

• 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.

SummarySummary

• 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.

Questions66

Thanks67

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