1. Algorithms for Ad Hoc and Sensor Networks Roger Wattenhofer Herfstdagen, 2004

2. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20042 Overview Introduction –Ad-Hoc and Sensor Networks –Routing / Broadcasting Clustering Topology Control Geo-Routing Conclusions

3. 3 Power Processor Radio Sensors Memory

4. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20044 What are Ad-Hoc/Sensor Networks?

5. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20045 Ad-Hoc Networks vs. Sensor Networks Laptops, PDA’s, cars, soldiers All-to-all routing Often with mobility (MANET’s) Trust/Security an issue –No central coordinator Maybe high bandwidth Tiny nodes: 4 MHz, 32 kB, … Broadcast/Echo from/to sink Usually no mobility –but link failures One administrative control Long lifetime  Energy

6. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20046 Open Problem #1: Positioning and Virtual Coordinates Unit Disk Graph: Link if and only if Euclidean distance at most 1. Positioning: Some nodes know their position (“anchor nodes”). Virtual Coordinates: Unit Disk Graph Embedding –Graph Drawing? (Edge crossings  no problem) –Known to be NP-hard [Breu & Kirkpatrick 1998] –There is no PTAS [Kuhn et al., 2004] –Approximation algorithms? Minimize ratio of longest edge over shortest non-edge. Polylogarithmic approximation ratio [Moscibroda et al., 2004] –Mobile/dynamic nodes  Local updates, stability

7. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20047 Routing in Ad-Hoc Networks Multi-Hop Routing –Moving information through a network from a source to a destination if source and destination are not within mutual transmission range Reliability –Nodes in an ad-hoc network are not 100% reliable –Algorithms need to find alternate routes when nodes are failing Mobile Ad-Hoc Network (MANET) –It is often assumed that the nodes are mobile (“Moteran”)

8. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20048 Simple Classification of Ad-hoc Routing Algorithms Proactive Routing Small topology changes trigger a lot of updates, even when there is no communication  does not scale Flooding: when node received message the first time, forward it to all neighbors Distance Vector Routing: as in a fixnet nodes maintain routing tables using update messages Reactive Routing Flooding the whole network does not scale no mobilitymobility very highcritical mobility Source Routing (DSR, AODV): flooding, but re-use old routes

9. Roger Wattenhofer, ETH Zurich @ Herfstdagen 20049 Discussion Lecture “Mobile Computing”: 10 Tricks  2 10 routing algorithms In reality there are almost that many! Q: How good are these routing algorithms?!? Any hard results? A: Almost none! Method-of-choice is simulation… Perkins: “if you simulate three times, you get three different results” Flooding is key component of (many) proposed algorithms At least flooding should be efficient

10. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200410 Overview Introduction Clustering –Flooding vs. Dominating Sets –Algorithm Overview –Phase A –Phase B –Lower Bounds Topology Control Geo-Routing Conclusions

11. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200411 Finding a Destination by Flooding

12. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200412 Finding a Destination Efficiently

13. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200413 (Connected) Dominating Set A Dominating Set DS is a subset of nodes such that each node is either in DS or has a neighbor in DS. A Connected Dominating Set CDS is a connected DS, that is, there is a path between any two nodes in CDS that does not use nodes that are not in CDS. It might be favorable to have few nodes in the (C)DS. This is known as the Minimum (C)DS problem.

14. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200414 Formal Problem Definition: M(C)DS Input: We are given an (arbitrary) undirected graph. Output: Find a Minimum (Connected) Dominating Set, that is, a (C)DS with a minimum number of nodes. Problems –M(C)DS is NP-hard –Find a (C)DS that is “close” to minimum (approximation) –The solution must be local (global solutions are impractical for mobile ad-hoc network) – topology of graph “far away” should not influence decision who belongs to (C)DS

15. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200415 Overview Introduction Clustering –Flooding vs. Dominating Sets –Algorithm Overview –Phase A –Phase B –Lower Bounds Topology Control Geo-Routing Conclusions

16. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200416 Algorithm Overview 0.2 0.5 0.2 0.8 0 0.2 0.3 0.1 0.3 0 Input: Local Graph Fractional Dominating Set Dominating Set Connected Dominating Set 0.5 Phase C: Connect DS by “tree” of “bridges” Phase B: Probabilistic algorithm Phase A: Distributed linear program rel. high degree gives high value

17. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200417 Overview Introduction Clustering –Flooding vs. Dominating Sets –Algorithm Overview –Phase A –Phase B –Lower Bounds Topology Control Geo-Routing Conclusions

18. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200418 Phase A is a Distributed Linear Program Nodes 1, …, n: Each node u has variable x u with x u ¸ 0 Sum of x-values in each neighborhood at least 1 (local) Minimize sum of all x-values (global) 0.5+0.3+0.3+0.2+0.2+0 = 1.5 ¸ 1 Linear Programs can be solved optimally in polynomial time But not in a distributed fashion! That’s what we do here… 0.2 0.5 0.2 0.8 0 0.2 0.3 0.1 0.3 0 0.5 Linear Program Adjacency matrix with 1’s in diagonal

19. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200419 Phase A Algorithm

20. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200420 Result after Phase A Distributed Approximation for Linear Program Instead of the optimal values x i * at nodes, nodes have x i (  ), with The value of  depends on the number of rounds k (the locality)

21. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200421 Overview Introduction Clustering –Flooding vs. Dominating Sets –Algorithm Overview –Phase A –Phase B –Lower Bounds Topology Control Geo-Routing Conclusions

22. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200422 Dominating Set as Integer Program What we have after phase A What we want after phase B

23. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200423 Phase B Algorithm Each node applies the following algorithm: 1.Calculate (= maximum degree of neighbors in distance 2) 2.Become a dominator (i.e. go to the dominating set) with probability 3.Send status (dominator or not) to all neighbors 4.If no neighbor is a dominator, become a dominator yourself From phase A Highest degree in distance 2

24. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200424 Result after Phase B Randomized rounding technique Expected number of nodes joining the dominating set in step 2 is bounded by  log(  +1) ¢ |DS OPT |. Expected number of nodes joining the dominating set in step 4 is bounded by |DS OPT |. Theorem: E[|DS|] · O(  ln  ¢ |DS OPT |)

25. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200425 Related Work on (Connected) Dominating Sets Global algorithms –Johnson (1974), Lovasz (1975), Slavik (1996): Greedy is optimal –Guha, Kuller (1996): An optimal algorithm for CDS –Feige (1998): ln  lower bound unless NP 2 n O(log log n) Local (distributed) algorithms –“Handbook of Wireless Networks and Mobile Computing”: All algorithms presented have no guarantees –Gao, Guibas, Hershberger, Zhang, Zhu (2001): “Discrete Mobile Centers” O(loglog n) time, but nodes know coordinates –MIS-based algorithms (e.g. Alzoubi, Wan, Frieder, 2002) that only work on unit disk graphs. –Kuhn, Wattenhofer (2003): Tradeoff time vs. approximation

26. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200426 Recent Improvements Improved algorithms (Kuhn, Wattenhofer, 2004): –O(log 2  /  4 ) time for a (1+  )-approximation of phase A with logarithmic sized messages. –If messages can be of unbounded size there is a constant approximation of phase A in O(log n) time, using the graph decomposition by Linial and Saks. –An improved and generalized distributed randomized rounding technique for phase B. –Works for quite general linear programs. Is it any good…?

27. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200427 Overview Introduction Clustering –Flooding vs. Dominating Sets –Algorithm Overview –Phase A –Phase B –Lower Bounds Topology Control Geo-Routing Conclusions

28. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200428 Lower Bound for Dominating Sets: Intuition… m n-1 complete n m m … n n n Two graphs (m << n). Optimal dominating sets are marked red. |DS OPT | = 2. |DS OPT | = m+1.

29. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200429 Lower Bound for Dominating Sets: Intuition… In local algorithms, nodes must decide only using local knowledge. In the example green nodes see exactly the same neighborhood. So these green nodes must decide the same way! m n-1 n m …

30. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200430 Lower Bound for Dominating Sets: Intuition… m n-1 complete n m m … n n n But however they decide, one way will be devastating (with n = m 2 )! |DS OPT | = 2. |DS OPT without green | ¸ m. |DS OPT | = m+1. |DS OPT with green | > n

31. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200431 The Lower Bound Lower bounds (Kuhn, Moscibroda, Wattenhofer, 2004): –Model: In a network/graph G (nodes = processors), each node can exchange a message with all its neighbors for k rounds. After k rounds, node needs to decide. –We construct the graph such that there are nodes that see the same neighborhood up to distance k. We show that node ID’s do not help, and using Yao’s principle also randomization does not. –Results: Many problems (vertex cover, dominating set, matching, etc.) can only be approximated  (n c/k 2 / k) and/or  (  1/k / k). –It follows that a polylogarithmic dominating set approximation (or maximal independent set, etc.) needs at least  (log  / loglog  ) and/or  ((log n / loglog n) 1/2 ) time.

32. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200432 Graph Used in Dominating Set Lower Bound The example is for k = 3. All edges are in fact special bipartite graphs with large enough girth.

33. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200433 Clustering for Unstructured Radio Networks “Big Bang” (deployment) of a sensor and/or ad-hoc network: –Nodes wake up asynchronously (very late, maybe) –Neighbors unknown –Hidden terminal problem –No global clock –No established MAC protocol –No reliable collision detection –Limited knowledge of the number of nodes or degree of network. We have randomized algorithms that compute DS (or MIS) in polylog(n) time even under these harsh circumstances, where n is an upper bound on the number of nodes in the system. [Kuhn, Moscibroda, Wattenhofer, 2004]

34. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200434 Overview Introduction Clustering Topology Control –What is it? What is it good for? –Does Topology Control Reduce Interference? –Cellular Networks, Sensor Networks, etc. Geo-Routing Conclusions

35. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200435 Topology Control Drop long-range neighbors: Reduces interference and energy! But still stay connected (or even spanner)

36. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200436 Topology Control as a Trade-Off Network Connectivity Spanner Property Topology Control Conserve Energy Reduce Interference Sometimes also clustering (first part of the talk) is called topology control d(u,v) ¢ t ¸ d TC (u,v)

37. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200437 Classic Solution: Gabriel Graph Let disk(u,v) be a disk with diameter (u,v) that is determined by the two points u,v. The Gabriel Graph GG(V) is defined as an undirected graph (with E being a set of undirected edges). There is an edge between two nodes u,v iff the disk(u,v) including boundary contains no other points. Gabriel Graph is planar Gabriel Graph is energy optimal [energy of link is at least distance squared] disk(u,v) v u

38. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200438 Topology Control Drop long-range neighbors: Reduces interference and energy! But still stay connected (or even spanner) Really?!?

39. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200439 Related Work Mid-Eighties: randomly distributed nodes [Takagi & Kleinrock 1984, Hou & Li 1986] Second Wave: constructions from computational geometry, Delaunay Triangulation [Hu 1993], Minimum Spanning Tree [Ramanathan & Rosales-Hain INFOCOM 2000], Gabriel Graph [Rodoplu & Meng J.Sel.Ar.Com 1999] Cone-Based Topology Control [Wattenhofer et al. INFOCOM 2000] ; explicitly prove several properties (energy spanner, sparse graph), locality. Collecting more and more properties [Li et al. PODC 2001, Jia et al. SPAA 2003, Li et al. INFOCOM 2004] (e.g. local, planar, distance and energy spanner, constant node degree) Explicit interference [Meyer auf der Heide et al. SPAA 2002]. Interference between edges, time-step routing model, congestion; trade-offs; however, interference model based on network traffic

40. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200440 Overview Introduction Clustering Topology Control –What is it? What is it good for? –Does Topology Control Reduce Interference? –Cellular Networks, Sensor Networks, etc. Geo-Routing Conclusions

41. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200441 What Is Interference? Model –Transmitting edge e = (u,v) disturbs all nodes in vicinity –Interference of edge e = # Nodes covered by union of the two circles with center u and v, respectively, and radius |e| Problem statement –We want to minimize maximum interference! –At the same time topology must be connected or a spanner etc. 8 Exact size of interference range does not change the results

42. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200442 Low Node Degree Topology Control? Low node degree does not necessarily imply low interference: Very low node degree but huge interference

43. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200443 Let’s Study the Following Topology! …from a worst-case perspective

44. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200444 Topology Control Algorithms Produce… All known topology control algorithms (with symmetric edges) include the nearest neighbor forest as a subgraph and produce something like this: The interference of this graph is  (n)!

45. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200445 But Interference… Interference does not need to be high… This topology has interference O(1)!!

46. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200446 Algorithms and Lower Bounds [Burkhart, von Rickenbach, Wattenhofer, Zollinger, 2004] Interference-optimal connectivity-preserving topology Local interference-optimal spanner topology Algorithms also work if interference radius >> transmission radius No local algorithm can find a good topology Optimal topology is not planar UDG, I = 50RNG, I = 25 LLISE 10, I = 12

47. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200447 Overview Introduction Clustering Topology Control –What is it? What is it good for? –Does Topology Control Reduce Interference? –Cellular Networks, Sensor Networks, etc. Geo-Routing Conclusions

48. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200448 New Results… Interference-driven topology control is exciting new paradigm… We have a few other upcoming results: For cellular networks: minimize number of base stations a mobile station overhears by reducing the transmission power of the base stations  “minimum membership set cover” problem For sensor networks: data gathering without listening to lots of unwanted traffic…

49. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200449 Open Problem #2: In-Interference Given ad-hoc network represented by nodes in a plane. Connect nodes by spanning tree. Circle of each node centered at node with the radius being the length of longest adjacent edge in spanning tree. Coverage of node is the number of circles node falls into. Minimize the maximum (or average) coverage. 3 4 3 2 4 3

50. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200450 Overview Introduction Clustering Topology Control Geo-Routing –What is geometric routing? –Correct geometric routing –Worst-case efficient geometric routing –Average-case efficient geometric routing Conclusions

51. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200451 Geometric Routing t s s ??????

52. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200452 Greedy Routing Each node forwards message to “best” neighbor t s

53. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200453 Greedy Routing Each node forwards message to “best” neighbor But greedy routing may fail: message may get stuck in a “dead end” Needed: Correct geometric routing algorithm t ? s

54. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200454 What is Geometric Routing? A.k.a. location-based, position-based, geographic, etc. Chapter 18 (“Routing […] in Geometric and Wireless Networks”) in “Handbook of Wireless Networking and Mobile Computing” Each node knows its own position and position of neighbors Source knows the position of the destination No routing tables stored in nodes! Geometric routing makes sense –Own position: GPS/Galileo, local positioning algorithm –Destination: overlay P2P net, geocasting, source routing++ –Learn about ad-hoc routing in general

55. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200455 Overview Introduction Clustering Topology Control Geo-Routing –What is geometric routing? –Correct geometric routing –Worst-case efficient geometric routing –Average-case efficient geometric routing Conclusions

56. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200456 Face Routing Based on ideas by [Kranakis, Singh, Urrutia CCCG 1999] Here simplified (and actually improved)

57. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200457 Face Routing Remark: Planar graph can easily (and locally!) be computed with the Gabriel Graph, for example.

58. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200458 Face Routing st

59. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200459 Face Routing st

60. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200460 Face Routing st

61. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200461 Face Routing st

62. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200462 Face Routing st

63. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200463 Face Routing st

64. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200464 Face Routing st

65. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200465 All necessary information is stored in the message –Source and destination positions –Point of transition to next face Completely local: –Knowledge about direct neighbors‘ positions sufficient –Faces are implicit Planarity of graph is computed locally (not an assumption) Face Routing Properties “Right Hand Rule”

66. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200466 Face Routing Works on Any Graph s t

67. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200467 Overview Introduction Clustering Topology Control Geo-Routing –What is geometric routing? –Correct geometric routing –Worst-case efficient geometric routing –Average-case efficient geometric routing Conclusions

68. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200468 Face Routing Theorem: Face Routing reaches destination in O(n) steps But: Can be very bad compared to the optimal route

69. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200469 Bounding Searchable Area ts

70. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200470 Adaptively Bound Searchable Area What is the correct size of the bounding area? –Start with a small searchable area –Grow area each time you cannot reach the destination –In other words, adapt area size whenever it is too small Theorem: Algorithm finds destination after O(c 2 ) steps, where c is the cost of the optimal path from source to destination. Proof: Not in this presentation.

71. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200471 Algorithm is worst-case optimal Can we do any better? No, with “wheel” example –Destination is central node –Source is any node on ring –Any spoke can go to middle –Geometric routing: no routing tables  test many spines –Best path of size O(c)+O(c) –Test  (c) spokes of length  (c) –Cost  (c 2 ) instead of O(c) Theorem: Algorithm is asymptotically worst-case optimal.

72. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200472 Overview Introduction Clustering Topology Control Geo-Routing –What is geometric routing? –Correct geometric routing –Worst-case efficient geometric routing –Average-case efficient geometric routing Conclusions

73. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200473 GOAFR – Greedy Other Adaptive Face Routing Algorithm is not very efficient (especially in dense graphs) Combine Greedy and (Other Adaptive) Face Routing –Route greedily as long as possible –Circumvent “dead ends” by use of face routing –Then route greedily again Theorem: GOAFR is still asymptotically worst-case optimal… …and it is efficient in practice, in the average-case. What does “practice” mean? –Usually nodes placed uniformly at random

74. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200474 Average Case Not interesting when graph not dense enough Not interesting when graph is too dense Critical density range (“percolation”) –Shortest path is significantly longer than Euclidean distance too sparsetoo densecritical density

75. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200475 Shortest path is significantly longer than Euclidean distance Critical density range mandatory for the simulation of any routing algorithm (not only geometric) Critical Density: Shortest Path vs. Euclidean Distance

76. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200476 Simulation on Randomly Generated Graphs GFG/GPSR GOAFR+ Greedy success Connectivity better worse 1 2 3 4 5 6 7 8 9 024681012 Network Density [nodes per unit disk] Performance 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Frequency critical

77. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200477 Discussion Previously known –Non-competitive algorithms (Face Routing, GFG/GPSR, …) Three papers [DIALM 2002, MOBIHOC 2003, PODC 2003]: –The first worst-case optimal algorithm –The first worst-case optimal and average-case efficient algorithm –Percolation theory to evaluate routing algorithms –Various results for different cost metrics (“super-linear” not competitive) –Various related results (“bounded degree graphs”) Algorithm is simple: Can be implemented in network processor Quite a few implementations available Algorithm can be married with other approaches Geometric routing as a more stable form of source routing First tight result in ad-hoc routing (to our knowledge)

78. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200478 Overview Introduction Clustering Topology Control Geo-Routing Conclusions –Clustering vs. Topology Control –More realism, more realism, more realism, … –… Practice!

79. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200479 Clustering vs. Topology Control Clustering (Connected) Dominating Set (Connected) Domatic Partition Both approaches sparsen the graph in order to reduce energy… … by turning off fraction of the nodes, and thus interference. Two sides of the same medal? Topology Control Interference-Driven T.C. … by turning off long-range links, and thus interference.

80. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200480 Overview Introduction Clustering Topology Control Geo-Routing Conclusions –Clustering vs. Topology Control –More realism, more realism, more realism, … –… Practice!

81. 81 ? What does a typical ad-hoc network look like?

82. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200482 Like this?

83. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200483 Like this?

84. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200484 Or rather like this?

85. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200485 Or even like this?

86. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200486 What about typical mobility? Brownian Motion? Random Way-Point? Statistical Data Model? Maximum Speed Model? …?

87. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200487 Overview Introduction Clustering Topology Control Geo-Routing Conclusions –Clustering vs. Topology Control –More realism, more realism, more realism, … –… Practice!

88. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200488 Combine Theory with Practice Practical experiments… Shockfish btnodes of NCCR/MICS Scatterweb

89. Roger Wattenhofer, ETH Zurich @ Herfstdagen 200489 Some credit… Problem (Algorithm)Reference Dominating SetKuhn, W. @ PODC 2003 Kuhn, Moscibroda, W. @ PODC 2004 Topology Control and Interference (LISE, XTC, MMSC, Sensor Networks) Burkhart et al. @ MobiHoc 2004 W., Zollinger @ WMAN 2004 von Rickenbach et al. @ submitted Zollinger et al. @ submitted Geo-Routing (GOAFR)Kuhn, W., Zollinger @ MobiHoc 2003 Kuhn, W., Zhang, Zollinger @ PODC 2003 Positioning (GHoST)Bischoff, W. @ PerCom 2004 Kuhn, Moscibroda, W. @ DIALM 2004 Moscibroda et al. @ DIALM 2004. Data gatheringCristescu et al. @ submitted von Rickenbach, W. @ DIALM 2004 Models: Quasi-UDGKuhn, W., Zollinger @ DIALM 2003 “Deployment” problemMoscibroda, W. @ ESA & MobiCom & MASS 2004

90. 90 Thank you! Distributed Computing Group Roger Wattenhofer