Landmark Selection and Greedy Landmark-Decent Routing for Sensor Network

1. INFOCOM 2007 Landmark Selection and Greedy Landmark-Decent Routingfor Sensor Network An Nguyen, Nikola Milosavljevic, Qing Fang, JieGao, and Leonidas J. Guibas Dept. of Computer Science, Stanford University 산업 및 시스템 공학과 통신시스템 및인터넷보안연구실 20075273 김효원

2. Outline • Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Simulations • Conclusion

3. Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Simulations • Conclusion

4. Introduction • Implementation of scalable point to point routing • Geographic location based method • Greedy routing • Robust to sensor location inaccuracy • Do not guarantee packet delivery rate • Face routing • Overcome problems of greedy routing • Use planar graph but fail in real world deployment • Accurate geographic location information is difficult and expensive to obtain, • Location free routing schemes have been developed

5. Introduction • Implementation of scalable point to point routing • Geographic location free method • Several virtual coordinate system use landmark • GLIDER (Gradient Landmark-based Routing) • BVR (Beacon Vector Routing) • Landmark-based schemes are favored by simplicity and independence of network, • and can be easily extended to sensors deployed in 3D → In this paper, propose • Distributed landmark selection protocol • Greedy Landmark Descent Routing (GLDR)

6. Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Simulations • Conclusion

7. Landmark Selection • Important issue in landmark-based scheme • Poorly selected landmarks can result in poor routing performance • To select set of landmarks (r-sampling) • Sequential algorithm • Best result possible • Expensive • Random sampling (Parallel algorithm) • Not expensive • Oversampling → Design middle ground between 2 algorithms r

8. Landmark Selection • Protocol • Each node generates v uniformly at random [0,1) • Each node waits for Kv time • K: network parameter • During waiting period, a node is not suppressed by any other node, it declares itself landmark • New landmark broadcasts its status to its r-neighborhood, suppressing all non-landmark nodes in it Completion time K is large → sequential algorithm K is small → parallel algorithm Tradeoff Over sampling rate

9. Landmark Selection • Protocol Each node generates v K=10 others : v > 0.4 v=0.1 v=0.2 During waiting time (Kv), it declare landmark v=0.4 New landmark broadcasts r-neighborhood v=0.3 : Landmark

10. Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Process • GLDR in Networks • Handling the Boundary Effect • Simulations • Conclusion

11. Landmark Descent – Greedy Rule • Process • Source node selects out of the landmarks that maximizes the ratio of distances to source and destination • It moves toward this landmark along the shortest path, until the landmark becomes equally distant from the source and destination • At this point, the process repeats • If the source and destination is coincide, landmark distance vector is equal [Theorem 3] If destination is an interior point, the landmark decent routing is always successful

12. Landmark Descent – Greedy Rule • Process Source node select out of landmarks that maximizes ratio of distances to source and destination Moves towards landmark along the shortest path, until landmark becomes equally distant from the source and destination Landmark d1 d1 Landmark d2 d2 Destination (interior point) Source 4r Landmark

13. Landmark Descent – Greedy Rule • GLDR in Networks • GLDR packet header includes • Address of the destination • ID of the landmark toward which packet is forwarded • List of recently encountered extreme nodes • Packet header is updated only at extreme node • Only keep the last 8 recently encountered nodes in the head (for small packet header) ※ If a packet gets stuck, resort to scope flooding [Extreme Nodes] Nodes at which the packet stopped proceeding to its current landmark (There is no landmark that is farther from the node than from the destination)

14. Landmark Descent – Greedy Rule • Handling the Boundary Effect • Routing to destinations near the boundaries is difficult because there are less and/or only one-sided reference point • To alleviate boundary effect, virtually expand the sensor field • Reason for using virtual landmarks is to compensate for the lack of addressing landmarks for non-interior nodes along the field boundary

15. Landmark Descent – Greedy Rule • Handling the Boundary Effect Duplicate non-interior part of the network, and place it below original Source Compensate for lack of addressing landmarks for non-interior nodes along field boundary d1 Virtual Landmark Real Landmark Nodes within 12r hops away from boundary learn their distances to virtual landmarks d1 d2 d2 Real Landmark Destination (interior point) Boundary

16. Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Simulations • Default Simulation Setup • Successful Delivery Rate • Remaining Path and Stretch Factor • Landmark Selection • Improving BVR with r-sampling LM selection • Improving routing performance using Virtual LM • Conclusion

17. Simulations • Default setup • Generate 3200 nodes • Communication radius : 10 node degree • Landmark generation : 10-sampling (r=10) • K = 1200 • Run landmark selection schemes : 100 times • When routing, use distance to the nearest 10 LMs • Simulation results • Average 28.2 landmarks • Success rate of proposed routing : 90.9% (without flooding) • When failed, hop distance from terminated source to destination : avg = 4.47, σ = 2.34

18. Simulations • Successful delivery rate • # of routing landmarks • Total # of landmarks • Communication radius

19. Simulations • Remaining paths and stretch factor • Changing the landmark separation distance • Average remaining path • How much we need to flood to reach destination • Path stretch factor • How longer our routes than the shortest path

20. Simulations • Landmark selection • As K increased, # of landmarks decreased • Landmarks had more time to suppress nearby nodes preventing them from becoming landmark • But, network initialization time is dominated by K • K should be small, at the expense of more landmarks

21. Simulations • Improving BCR with r-sampling landmark selection • Proposed LM selection scheme improve BVR • Improving routing performance using virtual landmarks • Virtual LMs improve routing quality

22. Introduction • Landmark Selection • Landmark Descent – Greedy Rule • Simulations • Conclusion

23. Conclusion • For good routing performance, authors propose • Practical landmark selection protocol • Uniformly samples a network of sensors • Beneficial to some other landmark based routing • BVR’s performance improves significantly • Simple greedy routing (GLDR) • Based on distances to landmark • Lower routing overhead compared to BVR