1. A New Class of Mobility Models for Ad Hoc Wireless Networks Rahul Amin Advisor: Dr. Carl Baum Clemson University SURE 2006

2. Brief Overview  Background on Random Waypoint Model  Description of New Model  Observations and Conclusions  Future Work

3. Random Waypoint Model  Choose a random point (waypoint) distributed uniformly over some area  Choose a random velocity and move from current waypoint to the next using this velocity v1v1 v3v3 v2v2 0 3 1 2

4. Motivation  Random Waypoint Model is too idealistic Nodes can move freely without restrictions  Model a more real-world scenario Obstructions in mobility Obstructions in communication

5. New Model Description  Outer circle radius fixed at 1000 m  Inner circle represents obstruction and its radius can be varied  Obstruction can affect mobility as well as communication  Constant velocity model used (10 m/sec)  Distribution sampled every 1 sec

6. New Model Description (contd.)  Waypoint is described by Radius (R) and Angle (Θ)  R and Θ are independent  Generate a Uniform Random Variable in (0,1) interval using Mersenne Twister algorithm

7. New Model – Boundary Prevention  Node smartly predicts if it is going to collide with the obstruction  To prevent collision, the waypoint is discarded and a new waypoint is generated 0 1 2 2 3 3

8. Collision Prediction Calculations

9. Generated Waypoint Efficiency  Efficiency decreases as the radius of obstruction is increased  Acceptable efficiency – not going to slow simulation drastically

10. Steady State Density  Peak value shifts right as obstruction radius is increased  Close to being spatially uniform

11. Network Partitions  Partition The inability of any one node to be able to connect to any other node for a given distribution  Spanning Tree Tree that spans every node in the distribution without forming loops Kruskal’s Minimum Spanning Tree Algorithm used to study network partitions

12. Network Partitions - Mobility Blocking, No Communication Blocking  The maximum hop distance used was ½R = 500 m  In this range, lowest Probability of Partition when obstruction radius = 400 m

13. Network Partitions – Mobility & Communication Blocking  The maximum hop distance used was ½R = 500 m  Pretty similar characteristics to just mobility blocking

14. Average Required Power Per Node  Maximum hop Distance: 2R  No Partitions  Assumes perfect knowledge of required power values

15. Effects of Imperfect Knowledge on Required Power Values  Nodes = 30  Update Period: Time before nodes figure out that the best path to minimize power has changed  As the update period increases, required power increases

16. Conclusions  Effects of obstructions on Random Waypoint Model were studied  A more customizable model presented

17. Future Work  Use Markov velocity model  Create multiple obstructions with different radii  Change the path metrics for choosing the routes required for minimum power

18. Acknowledgements  Dr. Carl Baum  Clemson University SURE Program  National Science Foundation  ECE faculty and Graduate Students

19. Questions