Vehicle Mobility, Communication Channel Modeling and Traffic Density Estimation in VANETs

Nabeel AkhtarDepartment 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 differentapplications in VANETs

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 ParametersValues 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 CorrelationGudmunson 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)

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 AlgorithmsAdapted 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)

ConclusionMobility & 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

Turk telecom

ADVISORS: Dr. Sinem Ergen & Dr. Oznur Ozkasap

Koc University

THANK YOU!

Question and Answers?

Nabeel Akhtar: nakhtar@ku.edu.tr

Wireless Networks Laboratory: http://wnl.ku.edu.tr