1. 1 Routing in Internet vs. Sensor Network

2. 2 Sensor Network Routing –I Location/Geographic Based Routing Tian He Some materials are adapted from I. Stojmenovic and A. Arora

3. 3 Taxonomy Sensor Network Protocols Geo-Routing LAR, GPSR, GEDIR Network Encoding VGR, LCR, BVR, GEM Data Centric DDSPT, AODV, DSR ID-Based Decouple information from nodes ID. Care only about the source of the data, instead which node creates the data Skip TodayNext LectureLast Lecture

4. 4 Motivation: Location-based Routing In sensor networks, data is primarily characterized by its geographic location and/or its data content. NOT Which the id of the node that generates the data

5. 5 Discussion Benefits of Geographic Routing ScalabilityProtocol Cost

6. 6 B. Karp and H. T. Kung Mobicom 2000 GPSR: Greedy perimeter stateless routing for wireless networks Young-Jin Kim, Ramesh Govindan, Brad Karp and Scott Shenker NSDI 2005 Geographic Routing Made Practical Two papers

7. 7 Variation of geographic forwarding policy A B C D E F S 1.MFR: Choose closest projection on SD; 2.Greedy: Choose the closest node; 3.Random progress (Nelson, Kleinrock); 4.NFP- nearest forward progress (Hou, Li); A’ F’ AD < FD F A A,C,F C

8. 8 Information needed for geo-routing A node knows about three types of locations Its own location: GPS, localization methods Its neighbors’ locations Beaconing: broadcast, Piggybacking and overhearing in promiscuous The destination locations Application specify Location Directory Services

9. 9 Greedy Forwarding [Finn 1987] The next hop from a node is the neighbor that is geographically closest to the packet's destination. S D Closest to D R

10. 10 Algorithm: Greedy Forwarding Problem!!

11. 11 Right Hand Rule When arriving at a node x from node y, the next edge traversed is the next one sequentially counterclockwise about x from edge (x,y)

12. 12 Face Traversal X D F1F1 F2F2 F3F3 F4F4 Walking sequence: F 1 -> F 2 -> F 3 -> F 4 Two primitives: (1) the right-hand rule (2) face-changes Perimeter (Face) traversal on a planar graph

13. 13 Quiz Identify the GPSR Routing from s to t s t 2 3 4 5 6 9 7 10 11 19 16 20 17 21 1 8 22 23 14 24 15 18 13 12 Suppose |6t| < |2t|< |11t|, |9t| < |4t| < |14t| and |10t| < |5t|< |19t|

14. 14 Answer Planar Travesal

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22. 22 Answer Identify the GPSR Route from s to t s t 2 3 4 5 6 9 7 10 11 19 16 20 17 21 1 8 22 23 14 24 15 18 13 12 S  1  5  1  6  2  3  9  3  4  t

23. 23 Planarized Graphs Problem with Non-Planarized Graph 1 2 3 4 5 S-1-2-4-1-2-4- ….. 1 2 3 4 5 S-1-2-1-4-3-D

24. 24 Planarized Graphs The Relative Neighborhood Graph (RNG) and Gabriel Graph (GG) are two planar graphs long- known in varied disciplines. Removing edges from the graph to reduce it to the RNG or GG must not disconnect the graph; this would amount to partitioning the network.

25. 25 Relative Neighborhood Graph (RNG) Given a collection of vertices with known positions, the RNG isdefined as follows: An edge (u,v) exists between vertices u and v if the distance between them, d(u,v), is less than or equal to the distance between every other vertex w, and whichever of u and v is farther from w. In equational form:

26. 26 Gabriel Graph (GG) The GG is defined as follows: An edge (u,v) exists between vertices u and v if no other vertex w is present within the circle whose diameter is (u,v). In equational form:

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28. 28 Summary GPSR = Greedy Forwarding + Face Change + Planarized Graph

29. 29 B. Karp and H. T. Kung Mobicom 2000 GPSR: Greedy perimeter stateless routing for wireless networks Young-Jin Kim, Ramesh Govindan, Brad Karp and Scott Shenker NSDI 2005 Geographic Routing Made Practical Two papers

30. 30 Geographic Routing Made Practical GPSR assumes: 1) Symmetric link/ Unit Disk Graph 2) Accurate localization Research Question How well do planarization techniques work in real- world?

31. 31 GPSR in network test-beds Wireless Network Graph GG sub-graph  A test-bed deployed in UC Berkeley Soda Hall 50 MICA2dot, 433MHz radio, 5.2 average node density Cross-link Disconnected Unidirectional  68.2% routing success among node pairs  What’s happening?

32. 32 Mutual Witness Key idea Remove a link only if both ends of the link see a mutual witness: can eliminate unidirectional links, disconnections Raises success rate to 87% But, mutual witness introduces other failure modes converts unidirectional/disconnected links into cross links leaves cross links in a sub-graph

33. 33 Basic Idea Each node probes its links to determine crossed links using right hand rule A S B C p[“crossings of (S, A)?”] p[“(B, C) crosses (S, A)!”] p[“crossings of (S, A)?”] p[“(B, C) crosses (S, A)!”] CLDP (Cross-Link Detection Protocol)

34. 34 Summary Practical GPSR = Greedy Forwarding + Face Change + Planarized Graph with CLDP

35. 35 Location-based/Geometric/Geographic routing G. G. Finn (1987) GFGreedy forwarding with limited flooding Kleinrock et al.MFRGeometric Routing proposed Kranakis, Singh, UrrutiaFace RoutingFirst correct algorithm Bose, Morin, Stojmenovic, Urrutia GFGFirst average-case efficient algorithm (simulation but no proof) Karp, KungGPSRA new name for GFG Kuhn, Wattenhofer, Zollinger GOAFRWorst-case optimal and average-case efficient, percolation theory A little bit history

36. 36 Other variation of Geo-based Routing SPEED: Real-time routing that maintains a delivery speed.

37. 37 Other variation of Geo-based Routing Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks S D Closest to D, but with very low quality R Optimal metrics: product of the packet reception rate (PRR) and the distance traversed towards destination

38. 38 Thanks