1. 1 15-441 Computer Networking Lecture 10 – Geographic Ad Hoc Routing

2. 2 Readings [I.1] GPSR: Greedy Perimeter Stateless Routing for Wireless Networks, Karp, B., and Kung. H. T., Proc. 6th Annual International Conference on Mobile Computing and Networking (MobiCom 2000), 243-254.GPSR: Greedy Perimeter Stateless Routing for Wireless Networks [I.7] Jinyang Li, John Jannotti, Douglas S. J. De Couto, David R. Karger, and Robert Morris, "A Scalable Location Service for Geographic Ad Hoc Routing." Proceedings of the sixth ACM International Conference on Mobile Computing and Networking (MobiCom '00), July 2000, Boston, Massachusetts. Pages 120-130.A Scalable Location Service for Geographic Ad Hoc Routing [I.11] Ananth Rao, Sylvia Ratnasamy, Christos Papadimitriou, Scott Shenker, and Ion Stoica. Geographic Routing without Location Information. In Proc. of ACM Mobicom 2003, Sept. 2003.Geographic Routing without Location Information. OPTIONAL [I.12] Young-Bae Ko and Nitin H. Vaidya, "Location-Aided Routing(LAR) in Mobile Ad Hoc Networks" In Proceedings of the 4th Annual International Conference on Mobile Computing and Networking (MOBICOM'98), October, 1998."Location-Aided Routing(LAR) in Mobile Ad Hoc Networks"

3. 3 Context Early 90s: availability of off-the-shelf wireless network cards and laptops 1994: first papers on Destination-Sequenced Distance Vector (DSDV) routing and Dynamic Source Routing (DSR) spark tremendous interest in routing on mobile wireless (ad hoc) networks 1998: Broch et al.’s comparison of leading ad hoc routing protocol proposals in ns-2 simulator in MobiCom [2000: GPSR in MobiCom] 2000: Estrin et al.’s Directed Diffusion in MobiCom sparks interest in wireless sensor networks

4. 4 Scaling Routing (cont’d) Dominant factors in cost of DV, LS, DSR: rate of change of topology (bandwidth) number of routers in routing domain (b/w, state) Scaling strategies: Hierarchy: at AS boundaries (BGP) or on finer scale (OSPF) Goal: reduce number of routers in routing domain Assumption: address aggregation Caching: store source routes overheard (DSR) Goal: limit propagation of future queries Assumption: source route remains fixed while cached

5. 5 Problem A B E C XSD D C XSD C S D C XS t0t0 t1t1

6. 6 Route Discovery Using Flooding A B C E XSD route request route reply

7. 7 Location-Aided Routing Main Idea Using location information to reduce the number of nodes to whom route request is propagated. Location-aided route discovery based on “limited” flooding Consider a node S that needs to find a route to node D. each host in the ad hoc network knows its current location node S knows that node D was at location L at time t 0, and that the current time is t 1

8. 8 Location Information Consider a node S that needs to find a route to node D. Assumption: each host in the ad hoc network knows its current location precisely (location error considered in one of their simulations) node S knows that node D was at location L at time t 0, and that the current time is t 1

9. 9 Expected Zone expected zone of D ---- the region that node S expects to contain node D at time t 1, only an estimate made by node S

10. 10 Request Zone LAR’s limited flooding A node forwards a route request only if it belongs to the request zone The request zone should include expected zone other regions around the expected zone No guarantee that a path can be found consisting only of the hosts in a chosen request zone. timeout expand request zone Trade-off between latency of route determination the message overhead

11. 11 Central idea:Machines can know their geographic locations by GPS (outdoors) by surveyed position (for non-mobile nodes) by short-range localization (indoors, [AT&T Camb, 1997], [Priyantha et al., 2000]) Route using geography. packet destination field: location of destination Greedy Perimeter Stateless Routing (GPSR)

12. 12 Assumptions Bi-directional radio links (unidirectional links may be blacklisted) Network nodes placed roughly in a plane Radio propagation in free space; distance from transmitter determines signal strength at receiver Fixed, uniform radio transmitter power

13. 13 Greedy Forwarding Nodes learn immediate neighbors’ positions from beaconing/piggybacking on data packets Locally optimal, greedy next hop choice: Neighbor geographically nearest destination D x y Neighbor must be strictly closer to avoid loops

14. 14 In Praise of Geography Self-describing As node density increases, shortest path tends toward Euclidean straight line between source and destination Node’s state concerns only one-hop neighbors: Low per-node state: O(density) Low routing protocol overhead: state pushed only one hop

15. 15 Greedy Forwarding Failure Greedy forwarding not always possible! Consider: D x z v void w y How can we circumnavigate voids? …based only on one-hop neighborhood?

16. 16 Node Density and Voids Voids more prevalent in sparser topologies

17. 17 Well-known graph traversal: right-hand rule Requires only neighbors’ positions Void Traversal: The Right-hand Rule x y z

18. 18 Planar vs. Non-planar Graphs On graphs with edges that cross (non-planar graphs), right-hand rule may not tour enclosed face boundary How to remove crossing edges without partitioning graph? And using only single-hop neighbors’ positions?

19. 19 Planarized Graphs Relative Neighborhood Graph (RNG) [Toussaint, ’80] and Gabriel Graph (GG) [Gabriel, ’69]: long-known planar graphs Assume edge exists between any pair of nodes separated by less than threshold distance (i.e., nominal radio range) RNG and GG can be constructed from only neighbors’ positions, and can be shown not to partition network! uv w ? uv w ? RNG GG Euclidean MST (so connected) RNG GG Delaunay Triangulation (so planar)

20. 20 Planarized Graphs: Example 200 nodes, placed uniformly at random on 2000-by-2000-meter region; 250-meter radio range Full Graph GG Subgraph RNG Subgraph

21. 21 Full Greedy Perimeter Stateless Routing All packets begin in greedy mode Greedy mode uses full graph Upon greedy failure, node marks its location in packet, marks packet in perimeter mode Perimeter mode packets follow simple planar graph traversal: Forward along successively closer faces by right-hand rule, until reaching destination Packets return to greedy mode upon reaching node closer to destination than perimeter mode entry point

22. 22 Perimeter Mode Forwarding Example Traverse face closer to D along xD by right-hand rule, until crossing xD Repeat with next-closer face, &c. x D

23. 23 Packet Delivery Success Rate (50, 200; Dense)

24. 24 State Size (200; Dense) How would you expect GPSR’s state size to change the number of nodes in the network increases? Why does DSR hold state for more nodes than there are in the network?

25. 25 Critical Thinking Based on the results thus far (indeed, all results in the paper), what do we know about the performance of GPSR’s perimeter mode? Would you expect it to be more or less reliable than greedy mode? Would you expect use of perimeter mode to affect path length? Evaluation in paper reveals nearly nothing about performance of perimeter mode! Why doesn’t it?

26. 26 Other Issues How to route geographically in 3D? Greedy mode? Perimeter mode? Effect of radio-opaque obstacles? Effect of position errors?

27. 27 Geographic Forwarding Scales Well A addresses a packet to G’s latitude, longitude C only needs to know its immediate neighbors to forward packets towards G. Geographic forwarding needs a location service! A B C D F C’s radio range E G

28. 28 Possible Designs for a Location Service Flood to get a node’s location (LAR, DREAM). excessive flooding messages Central static location server. not fault tolerant too much load on central server and nearby nodes the server might be far away for nearby nodes or inaccessible due to network partition. Every node acts as server for a few others. good for spreading load and tolerating failures.

29. 29 Desirable Properties of a Distributed Location Service Spread load evenly over all nodes. Degrade gracefully as nodes fail. Queries for nearby nodes stay local. Per-node storage and communication costs grow slowly as the network size grows.

30. 30 Grid Location Service (GLS) Overview A E H G B D F C J I K L Each node has a few servers that know its location. 1. Node D sends location updates to its servers (B, H, K). 2. Node J sends a query for D to one of D’s close servers. “D?”

31. 31 Grid Node Identifiers Each Grid node has a unique identifier. Identifiers are numbers. Perhaps a hash of the node’s IP address. Identifier X is the “successor” of Y if X is the smallest identifier greater than Y.

32. 32 GLS’s spatial hierarchy level-0 level-1 level-2 level-3 All nodes agree on the global origin of the grid hierarchy

33. 33 n s s s s s s s s s s is n’s successor in that square. (Successor is the node with “least ID greater than” n ) sibling level-0 squares sibling level-1 squares sibling level-2 squares 3 Servers Per Node Per Level

34. 34 Each query step: visit n’s successor at surrounding level. n s s s s s s s s s3 x s2 s1 location query path Queries Search for Destination’s Successors

35. 35 9 11 23 6 9 11 2 16 23 6 17 26 21 5 25 8 1 7 29 Base case: Each node in a level-0 square “knows” about all other nodes in the same square. location table content Invariant (for all levels): For node n in a square, n’s successor in each sibling square “knows” about n. 19 4 3 GLS Update (level 0)

36. 36 9 11 23 6 2 9 11 2 16 23 6 17 4 26 21 5 19 25 8 1 7 29 2 2 Invariant (for all levels): For node n in a square, n’s successor in each sibling square “knows” about n. location table content location update 3 GLS Update (level 1)

37. 37... 9 11, 2 23, 2 6 9 11 16 23 6 17 4 26 21 19 25 1 3 2... 8 5 29 7... location table content 2 Invariant (for all levels): For node n in a square, n’s successor in each sibling square “knows” about n. GLS Update (level 1)

38. 38... 1 9 23, 2 11, 2 6 9 11 16 23 6 17 4 26 21 5 19 25 7 3 29 2... 1 8 1 location table content location update 2 Invariant (for all levels): For node n in a square, n’s successor in each sibling square “knows” about n. GLS Update (level 2)

39. 39 1... 9 23, 2 11, 2 6 9 11 2 16 23 6 17 4 26 21 5 19 25 7 3 29 2... 1 8 1 location table content query from 23 for 1 GLS Query

40. 40 Challenges for GLS in a Mobile Network Slow updates risk out-of-date information. Packets dropped because we can’t find the destination. Aggressive updates risk congestion. Update packets leave no bandwidth for data. Large mobile ad-hoc nets usually suffer from one or the other.

41. 41 Simulations using ns with CMU’s wireless extension (IEEE 802.11) Mobility Model: random way-point with speed 0-10 m/s (22 mph) Area of square universe grows with the number of nodes in the network. Achieve spatial reuse of the spectrum GLS level-0 square is 250m x 250m 300 seconds per simulation Simulation Environment

42. 42 Failed queries are not retransmitted in this simulation Queries fail because of out-of-date information for destination nodes or intermediate servers Number of nodes query success rate Biggest network simulated: 600 nodes, 2900x2900m (4-level grid hierarchy) GLS Finds Nodes in Big Mobile Networks

43. 43 Protocol packets include: GLS update, GLS query/reply Number of nodes Avg. packets transmitted per node per second GLS Protocol Overhead Grows Slowly

44. 44 Why geographic routing without location information GPS takes power, doesn’t work indoors Obstacles, non-ideal radios Coordinates computed will reflect true connectivity and not the geographic locations of the nodes

45. 45 Step by Step Approach Perimeter nodes and their locations are known Perimeter nodes are known but their locations are not known Nothing is known about the perimeter Relaxation algorithm Balls and Springs Perimeter node detection Degree of Information

46. 46 Rubber Bands Every node moves to the average of its neighbors coordinates at each step in the iteration

47. 47 Perimeter nodes are known (10 iterations)

48. 48 Perimeter nodes are known (100 iterations)

49. 49 Perimeter nodes are known (1000 iterations)

50. 50 Stage 1 : Each perimeter node broadcasts a HELLO message to the entire network Stage 2 : Each perimeter node broadcasts its perimeter vector to the entire network Stage 3 : Every perimeter node uses a triangulation algorithm to compute the coordinates of all other perimeter nodes by minimizing Perimeter Nodes are Known but Their Locations are not Known

51. 51 Balls and Springs Ball : each perimeter node Spring : each ball is attached by a spring Spring’s length : the hop count distance Seen as minimizing the potential energy when a ball attached to every other ball by a spring

52. 52 Nothing is Known about the Perimeter Bootstrap Nodes flood the network and every node discovers its distance to these bootstrap nodes Nodes use the following criterion Perimeter Criterion “if a node is farthest away, among all its two-hop neighbors from the first bootstrap node, then the node decides that it is on the perimeter.” False Positives are relatively low and have little effect on the eventual outcome.

53. 53 Perimeter node detection

54. 54 Overall algorithm - Bootstrap the coordinate assignment 1.Two designated bootstrap beacon nodes broadcast to the entire network 2.Every perimeter node sends a broadcast message to the entire network to enable every other node to compute its perimeter vector 3.Perimeter and bootstrap nodes broadcast their perimeter vectors to the entire network 4.Each node uses these inter-perimeter distances to compute normalized coordinates for both itself and the perimeter nodes

55. 55 Overall Algorithms[Cont’d] – normal operation 5.Perimeter nodes stay fixed while other nodes run a relaxation algorithm 6.A designated bootstrap node periodically broadcasts by which nodes periodically re- asses whether they lie on the perimeter or not

56. 56 Two metrics to measure Success rate The fraction of times packets reach their intended destination using purely greedy routing Average Path length The average number of hops taken along the path

57. 57 Success Rate vs. Iterations

58. 58 Path Length vs. Number of Nodes identical

59. 59 Recap: Scalability via Geography with GPSR Key scalability properties: Small state per router: O(D), not O(N) or O(L) as for shortest-path routing, where D = density (neighbors), N = total nodes, L = total links Low routing protocol overhead: each node merely single-hop broadcasts own position periodically Approximates shortest paths on dense networks Delivers more packets successfully on dynamic topologies than shortest-paths routing protocols