1. 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

2. 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

3. 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

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

5. Vehicle Mobility & Channel Modeling
Part 1
Vehicle Mobility & Channel Modeling

6. 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

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

8. 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

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

10. Channel Models (continue)
Obstacle Based Model:

11. 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

12. 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.

13. 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

14. 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

15. Results(2)

16. Results(3)

17. Results(4)

18. Results(5)

19. Results(6)

20. Results(7)

21. Results(8)

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

23. Validation of Matching Mechanism (2)

24. Distributed Algorithms for Density Estimation
Part 2
Distributed Algorithms for Density Estimation

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

26. 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

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

28. 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

29. Results (1)

30. Results(2)

31. Results(3)

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

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

34. 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

35. Results(1)

36. Results(2)

37. Results(3)

38. Results(4)

39. 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.

40. 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]

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

42. THANK YOU!  Question and Answers?
Nabeel Akhtar:
Wireless Networks Laboratory:
THANK YOU!  Question and Answers?