Department of Computer Engineering Koc University, Istanbul, Turkey

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Presentation transcript:

Department of Computer Engineering Koc University, Istanbul, Turkey Vehicle Mobility, Communication Channel Modeling and Traffic Density Estimation in VANETs Nabeel Akhtar Department of Computer Engineering Koc University, Istanbul, Turkey

Agenda Introduction Part 1: Vehicle Mobility & Channel Modeling Vehicle Mobility Modeling Channel Modeling Matching Mechanism Results: Mobility & Channel Modeling Validation of Matching Mechanism Part 2: Distributed Density Estimation Distributed Density Estimation Results: Distributed Algorithms CluSampling Results: CluSampling Conclusion

Introduction: Mobility & Channel Modeling Why do we need modeling and simulators? Realistic Mobility Modeling for vehicles on the road Realistic Channel Modeling for vehicles on the road Important for determining the performance of applications in VANETs

Introduction: Density Estimation Monitoring road traffic condition Congestion information of the road Important parameter for different applications in VANETs

Vehicle Mobility & Channel Modeling Part 1 Vehicle Mobility & Channel Modeling

Contributions Realistic Mobility Modeling for Highway scenario Real-world road topology and real-time data from PeMS database Microscopic Mobility SUMO simulator Large scale highway Analysis of Channel Models Comparison of different channel models: Unit Disc, Log Normal and Obstacle based model Extensive Analysis using different performance metrics: Node Degree, Link Duration, No. of Clusters, Neighbour Distribution, Closeness Centrality, Clustering Coefficient etc Matching Mechanism for Log normal model

Realistic Mobility Microscopic Mobility Modeling Acceleration and deceleration profiles Overtaking decisions Distance to the leading vehicle Traveling speed Dimension of the vehicles Poisson Distribution

Realistic Mobility (continued) Traffic Demand Modeling Data used for I-880S in Alameda County, California PeMS database - Historical and real-time database for state of California. Flow and Speed data- 25,000 individual sensors Low and High Density Traffic

Channel Models Unit Disc Model: Classical Log-Normal Shadowing Model:

Channel Models (continue) Obstacle Based Model:

Matching Mechanism (1) Running time of different channel models Matching Log Normal Model Parameters Values are adjusted such that the log-normal model matches closely to more realistic but hard to implement Obstacle Based Model. Introducing Time Correlation

Matching Mechanism (2) Matching Log Normal Model Parameters Values are adjusted such that the log-normal model matches closely to more realistic but hard to implement Obstacle Based Model.

Matching Mechanism (3) Introducing Time Correlation Gudmunson model with exponential correlation function used. Gaussian variable used in the classical log-normal model has zero correlation of the link characteristics over time

Results(1) Performance Metrics: Neighbour distance distribution Node Degree Number of neighbours of node Link Duration Time span between the instants at which the communication link between two vehicles is established and lost Closeness Centrality Inverse of the sum of the distances to all other nodes in the network Number of Clusters Size of the Largest Cluster Clustering coefficient Ratio of the number of links within a cluster to the maximum number of links that could exist within a cluster

Results(2)

Results(3)

Results(4)

Results(5)

Results(6)

Results(7)

Results(8)

Validation of Matching Mechanism (1) Additional highway road I5-S near Los Angeles

Validation of Matching Mechanism (2)

Distributed Algorithms for Density Estimation Part 2 Distributed Algorithms for Density Estimation

Current Density Estimation Techniques Infrastructure based techniques Road Side Radars Infra-red counters Cameras Pressure Pads Road Side Units Local Density based techniques Limitations

Contributions Adapted and implemented three fully distributed algorithms taken from system size estimation algorithms in peer-to-peer (P2P) for VANETs Algorithms: Sample & Collide HopSampling Gossip Based Aggregation CluSampling: Proposed Distributed Algorithm tailored for density estimation in VANETs Through analysis of CluSampling with four other density estimation algorithms shows that CluSampling is more robust to changes and perform better under different traffic conditions

Simulations Simulation of Urban MObility (SUMO) Tested for Validity & Performance based on real life data across Highway Roads. Urban Roads

Distributed Algorithms Adapted three fully distributed algorithms developed for system size estimation in peer-to-peer (P2P) Sample and Collide: Initiator node uniformly sample nodes from population It then estimate the system size depending on how many samples of the nodes are collected, before an already sampled node is re-selected Hop Sampling: Initiator node broadcasts initiator message to all the nodes in the network Nodes reply back probabilistically depending on their distance from initiator Initiator estimate network size based on replies Gossip-based Aggregation: Initiator node samples K initiators vehicles at random Each peer periodically exchanges information with its neighbors to estimate the size of the network

Results (1)

Results(2)

Results(3)

CluSampling (1) Distributed Algorithms tailored for Density Estimation is VANETs Use Clustering and Sampling techniques to estimate density

CluSampling (2) Clustering: Probe Vehicle selecting no. of clusters depending on its local density Selecting cluster head

CluSampling (3) Sampling: Started by cluster head node replies back to the cluster head with small probability p sending information about its local density Cluster head estimates the density of vehicles within a cluster as Probe vehicle calculate global density as

Results(1)

Results(2)

Results(3)

Results(4)

Conclusion Mobility & Channel Modeling: We analyze VANET topology characteristics by using both realistic large-scale mobility traces and realistic channel models. Used SUMO & PeMS Database for Microscopic Mobility Modeling Realistic channel model is obtained by implementing the recently proposed obstacle-based channel model Proposed a matching mechanism for Log Normal Model which includes exponential time correlation. Density Estimation: Proposed and analyze three fully distributed and infrastructure-free mechanisms for vehicle density estimation in VANETs inspired for system size estimation is P2P networks The high performance of Hop Sampling algorithm supports the usage of distributed approach in the density estimation in VANETs, instead of using infrastructure based solutions that suffers from limited coverage, high deployment and maintenance cost Proposed CluSampling: Clustering & Sampling based distributed algorithm for density estimation in VANETs Results show that CluSampling is better than previously proposed algorithms for density estimation.

Publications N. Akhtar, S. C. Ergen and O. Ozkasap, "Vehicle Mobility and Communication Channel Models for Realistic and Efficient VANET Simulation", accepted to IEEE Transactions on Vehicular Technology. [pdf | link | code] N. Akhtar, S. C. Ergen and O. Ozkasap, "Analysis of Distributed Algorithms for Density Estimation in VANETs", IEEE VNC, November 2012. [pdf | link] N. Akhtar, O. Ozkasap and S. C. Ergen, "VANET Topology Characteristics under Realistic Mobility and Channel Models", IEEE WCNC, April 2013. [pdf | link]

Acknowledgement ADVISORS: Dr. Sinem Ergen & Dr. Oznur Ozkasap Koc University Turk telecom

THANK YOU!  Question and Answers? Nabeel Akhtar: nakhtar@ku.edu.tr Wireless Networks Laboratory: http://wnl.ku.edu.tr THANK YOU!  Question and Answers?