1. Distance and Routing Labeling Schemes in Graphs with Applications in Cellular and Sensor Networks Feodor F. Dragan Computer Science Department Kent State University http://www.cs.kent.edu/~dragan

2. Talk is based on Known results for trees [Kannan&Naor&Rudich’88, Peleg’99 and ’00, Thorup&Zwick’01, Fraigniaud&Gavoille’01] Two recent (Fall 2003) papers (by V.D. Chepoi (U.Marseille), F. F. Dragan and Yann Vaxes (U.Marseille) –Distance and Routing Labeling Schemes for Non- Positively Curved Plane Graphs (subm. to J. of Algorithms); –Addressing, Distances and Routing in Triangular Systems with Applications in Cellular and Sensor Networks (to appear in Proc of 4 th WNAN 04) We start with an overview on labeling schemes …

3. Traditional graph representation Store adjacency information in global data structure –Requires much storage –Node labels contain no information, serve only as “pointers” to global data structure

4. Local graph representation IDEA: Store local pieces of information, i.e., –Associate label Label(v) with each node v, s.t. labels allow inferring adjacency information locally, without using additional memory and information Question: Can this be done compactly & efficiently?

5. “Ancient” Example: “Ancient” Example: Hamming adjacency labeling Goal: Label each node v with m-bit label Label(v) s.t. Label(v), Label (u) allow deciding if u and v are adjacent: Question: Can this be done with short labels? [Breuer&Folkman’67]: For any n-node graph there exists a Hamming distance adjacency labeling with m=2n , T=4  -4, where  = max node degree.

6. Interest Revived: Interest Revived: Adjacency Labeling [Kannan&Naor&Rudich’88] What’s in label? Note: - Algorithm knows nothing beyond the labels (even, it does not know which graph they come from) Adjacency-labeling (for graph family ): Adjacency-labeling (for graph family F ): 1.Function Label labeling nodes of any graph in F with short distinct labels (compactness) 2.Efficient algorithm for deciding adjacency of two nodes given their labels (efficiency) (polylogarithmic labels  polylogarithmic time)

7. Adjacency Labeling on Trees [Kannan&Naor&Rudich’88] Labeling: 1. Arbitrarily root the tree and prelabel each node v with distinct integer I(v) from [1..n]. 2. Label root r by Label(r)=(I(r)). 3. Label each non-root node v with parent w by Label(v)=(I(v),I(w)). Labels contain bits

8. Adjacency Labeling on Trees [Kannan&Naor&Rudich’88] Adjacency Decoder for u,v: Check if 1 st entry in Label(v) = 2 nd entry in Label(u) or vice versa. Constant time! Labeling: (with bits) 1. Arbitrarily root the tree and prelabel each node v with distinct integer I(v) from [1..n]. 2. Label root r by Label(r)=(I(r)). 3. Label each non-root node v with parent w by Label(v)=(I(v),I(w)).

9. Extensions Question (compactness): Is this doable for all graphs with -bit labels? 1.Bounded arboricity graphs –Bounded degree –Bounded-genus graphs (e.a., planar graphs) 2.Intersection-based graphs (e.a., interval graphs) 3.c-decomposable graphs Labels contain bits

10. Negative results Negative examples: Bipartite / chordal graphs Note: Graph class Note: Graph class F is adjacency-labelable using -bit labels  each G from F is fully defined by bits  # of represented graphs Therefore: Graph family F containing n-node graphs is not adjacency-labelable using -bit labels.

11. Question: Can this be done for other types of information beyond adjacency? Distance In general graphs Distance Edge / node connectivity Routing Flow … In trees Ancestry Lowest common ancestor (LCA or NCA) Depth of NCA (separation level)

12. Question: Can this be done for other types of information beyond adjacency? Distance In general graphs Distance Edge / node connectivity Routing Flow … In trees Ancestry Lowest common ancestor (LCA or NCA) Depth of NCA (separation level)

13. Distance Labeling Scheme Distance Goal: Goal: Short labels that encode distances and distance decoder, an algorithm for inferring the distance between two nodes only from their labels (in time polynomial in the label length) …a method for storing and extracting distances in a “distributed” fashion… Labeling: v  Label(v) Distance decoder: D (Label(v), Label(u))  dist(u,v)

14. Simple examples Distance Enjoy -bit distance labeling schemes with constant-time decoder

15. General graphs Distance Easy labeling: Label each node in n-node graph G on with all distances Hence, for the class of all n-node graphs Currently: Similar bounds apply for other large graph families (bipartite / chordal / … ) [Gavoille,&Peleg&Perennes&Raz ’ 01]

16. Distance labeling on trees Distance labeling on trees [Peleg’99] Preprocessing: Arbitrarily label each node v of T with distinct integers I(v) from [1..n]

17. Recursive partitioning and decomposition tree Tree separator: node v in n-node tree T, whose removal breaks T into subtrees of size at most n/2. Known fact: Every tree has a separator (can be found in linear time) T H

18. Labeling T H Construct for H Depth of NCA labeling scheme ( -bit labels [Peleg’00] and constant (can be made) decision time) Then, v  A(v) Hence, -bit labels

19. Constant Time Distance Decoder T H Given Label(u) and Label(v) Function distance_decoder_trees(Label(u),Label(v)) Extract A(u), A(v) Use A(u), A(v) to find the depth k of NCA(u,v) in H  1 Output, by extracting appropriate entries from labels  2+2=4 (A(u),2,2,0)) (A(v),6,2,1,0))

20. Lower bounds and Planar graphs [Gavoille,&Peleg&Perennes&Raz ’ 01]

21. Routing Labeling Scheme Distance Goal: Goal: Short labels that encode the routing information and routing protocol, an algorithm for inferring port number of the first edge on a shortest path from source to destination, giving only labels and nothing else Labeling: v  Label(v) Distance decoder: R (Label(v), Label(u))  port(v,u)

22. General graphs Distance Easy labeling (full routing tables): Label each node in n-node graph G on with full routing information Hence, for the class of all n-node graphs one needs nlogn -bit labels Currently: Nothing better can be done for general graphs if we insist on routing via shortest paths

23. Labeling trees with -bit labels T H Construct for H Depth of NCA labeling scheme ( -bit labels Then, v  A(v) Hence, -bit labels

24. Constant Time Routing Decision T H Given Label(v) and Label(u) Function routing_decision_trees(Label(u),Label(v)) Extract A(v), A(u) Use A(v), A(u) to find the depth k of NCA(v,u) in H  1 If k<h(v) output from Label(v)  port(4,12)=1 else (i.e., k=h(v)) output from Label(u)

25. Best routing labeling scheme for trees and Planar graphs [Thorup&Zwick ’ 01] For family of n-node trees there is a routing labeling scheme with labels of size O(logn)-bits per node and constant time routing decision. [Fraigniaud&Gavoille ’ 01] For family of n-node trees there is a routing labeling scheme with labels of size -bits per node and constant time routing decision. [Gavoille&Hanusse ’ 99] For family of n-node planar graphs there is a routing labeling scheme with labels of size (8n +o(n))-bits per node.

26. The Duals to Benzenoid Systems –Which is a simple circuit of the hexagonal grid and the region bounded by this circuit. Thus, they are subgraphs of the regular triangular grid Triangular systems

27. Motivation Applications in cellular networks Identification code (CIC) for tracking mobile users Dynamic location update (or registration) scheme time based movement based distance based (cell-distance based is best, according to [Bar-Noy&Kessler&Sidi’94])  Distances Routing protocol

28. Current cellular networks do not provide information that can be used to derive cell distances –It is hard to compute the distances between cells (claim from [Bar-Noy&Kessler&Sidi’94] ) –It requires a lot of storage to maintain the distance information among cells (claim from [Akyildiz&Ho&Lin’96] and [Li&Kameda&Li’00] ) Current situation

29. [Nocetti&Stojmenovic&Zhang’02] recently considered isometric subgraphs of the regular triangular grid and give among others –A new cell addressing scheme using only three small integers, one of them being zero –A very simple method to compute the distance between two sells –A short and elegant routing protocol isometric not isometric Recent results

30. Our results for triangular systems Scale 2 isometric embedding into Cartesian product of 3 trees  cell addressing scheme using only three small integers  distance labeling scheme with labels of size -bits per node and constant time distance decoder  routing labeling scheme with labels of size O(logn)- bits per node and constant time routing decision.

31. Three edge directions  three trees

35. Addressing

36. Scale 2 embedding into 3 trees

37. Distance labeling scheme for triangular systems Given G, find three corresponding trees and addressing (O(n) time) Construct distance labeling scheme for each tree (O(nlogn) time) Then, set -bit labels and constructible in total time

38. Distance decoder for triangular systems Given Label(u) and Label(v) Function distance_decoder_triang_syst(Label(u),Label(v)) Output ½(distance_decoder_trees( ) Thm: The family of n-node triangular systems enjoys a distance labeling scheme with -bit labels and a constant time distance decoder. +(distance_decoder_trees( ) +(distance_decoder_trees( ))

39. Routing labeling scheme for triangular systems Given G, find three corresponding trees and addressing Construct routing labeling scheme for each tree using Thorup&Zwick method (log n bit labels) Then, set Something more

40. Choosing direction to go from v Direction seen twice is good

41. Mapping tree ports to graph ports Then, (i.e., 3xlog n+3x4x3xlogn bit labels)

42. Routing Decision for triangular systems Given Label(u) and Label(v) Thm: The family of n-node triangular systems enjoys a routing labeling scheme with -bit labels and a constant time routing decision.

43. Other Results Thm: The families of n-node (6,3)-,(4,4)-,(3,6)-planar graphs enjoy distance and routing labeling schemes with -bit labels and constant time distance decoder and routing decision. (p,q)-planar graphs: inner faces of length at least p inner vertices of degree at least q

44. Squaregraphs: a cut and the semiplanes

45. Squaregraphs: cones and a decomposition tree

46. Squaregraphs: distances, neighbor cones

47. Thank you

48. Cellular Network