1. Minimizing Recovery State In Geographic Ad-Hoc Routing Noa Arad School of Electrical Engineering Tel Aviv University Yuval Shavitt School of Electrical Engineering Tel Aviv University MobiHoc ‘06

2. Outline Introduction The NEAR (Node Elevation Ad-hoc Routing) Algorithm Simulation Conclusion

3. Introduction_ background Ad-Hoc network is a network without AP, and they have mobile ability in general Routing schemes of mobile Ad-Hoc networks – Topology-based routing – Position-based routing

4. Introduction_ motivations Most position-based routing protocols can’t prevent the packet from reaching a concave node sourcedestination concave node

5. Introduction_ motivations Most position-based routing protocols can’t prevent the packet from reaching a concave node Recovery Algorithm may choose a long path sourcedestination

6. Introduction_ goals To prevent the routing algorithm from entering concave node To minimize the recovery state

7. Concave Node A node that has no neighbor that can make a greedy progress towards the destination A concave node can not be predicted in advance, based on the position of its neighbor nodes

8. The NEAR Algorithm Repositioning Algorithm Routing Algorithm

9. Repositioning Algorithm_ goals To identify and mark concave node To improve the greedy routing To improve the recovery process

10. Repositioning Algorithm_ identify and mark concave node A B C D α = 180°

11. A B C A’ floating node z = z+1 Repositioning Algorithm_repositioning

12. A’(x 1, y 1, 1) AB C ABC B’(x 2, y 2, 1) A”(x 1, y 1, 2)

13. Repositioning Algorithm_ threshold angle A minimal angle of 180° is simply too low, and almost all nodes will float α = 210° − 230° was found to be best for various scenarios

14. Repositioning Algorithm_ an example Before repositioningAfter repositioning

15. Routing Algorithm_ three states Descending source destination

16. Routing Algorithm_ three states Descending Ground to ground

17. Routing Algorithm_ three states Descending Ground to ground Climbing source destination

18. Routing Algorithm_ descending A’(x 1, y 1, 2) AB B’(x 2, y 2, 1) C ABC Z max = 1Z max Z max = 0

19. Routing Algorithm_ ground to ground Protocol – GPSR Z max – Always 0

20. Routing Algorithm_ climbing A’(x 1, y 1, 2) AB B’(x 2, y 2, 1) C ABC Z max = 2

21. Routing Algorithm_ recovery state Environment – Ground to ground Protocol – GPSR Minimizing the recovery state

22. Simulation_ environment 1 Field: 2000m x 2000m Variable network density

23. Simulation_ elimination of concave nodes

24. Simulation_ routing hops

25. Simulation_ routing distance

26. Simulation_ routing success

27. Simulation_ environment 2 Mobile node 1 – 90Km/h – Updating messages per second Mobile node 2 – 4.5Km/h – Updating messages every 20 seconds

28. Simulation_ average numbers of iterations

29. Simulation_ average numbers of iterations per node

30. Simulation_ change of physical links per node

31. Conclusions Smoothing the shape of voids and concave nodes can be predicted by their added virtual height Improving greedy routing and minimizing the recovery state NEAR is believed to improve ad-hoc networks’ ability to deal with voids and concave nodes

32. Thank You !