23 research outputs found

    Coloring the complements of intersection graphs of geometric figures

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    AbstractLet G¯ be the complement of the intersection graph G of a family of translations of a compact convex figure in Rn. When n=2, we show that χ(G¯)⩽min{3α(G)-2,6γ(G)}, where γ(G) is the size of the minimum dominating set of G. The bound on χ(G¯)⩽6γ(G) is sharp. For higher dimension we show that χ(G¯)⩽⌈2(n2-n+1)1/2⌉n-1⌈(n2-n+1)1/2⌉(α(G)-1)+1, for n⩾3. We also study the chromatic number of the complement of the intersection graph of homothetic copies of a fixed convex body in Rn

    Frequency reassignment in cellular phone networks

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    In cellular communications networks, cells use beacon frequencies to ensure the smooth operation of the network, for example in handling call handovers from one cell to another. These frequencies are assigned according to a frequency plan, which is updated from time to time, in response to evolving network requirements. The migration from one frequency plan to a new one proceeds in stages, governed by the network's base station controllers. Existing methods result in periods of reduced network availability or performance during the reassgnment process. The problem posed to the Study Group was to develop a dynamic reassignment algorithm for implementing a new frequency plan so that there is little or no disruption of the network's performance during the transition. This problem was naturally formulated in terms of graph colouring and an effective algorithm was developed based on a straightforward approach of search and random colouring

    Approximating Maximum Diameter-Bounded Subgraph in Unit Disk Graphs

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    We consider a well studied generalization of the maximum clique problem which is defined as follows. Given a graph G on n vertices and an integer d >= 1, in the maximum diameter-bounded subgraph problem (MaxDBS for short), the goal is to find a (vertex) maximum subgraph of G of diameter at most d. For d=1, this problem is equivalent to the maximum clique problem and thus it is NP-hard to approximate it within a factor n^{1-epsilon}, for any epsilon > 0. Moreover, it is known that, for any d >= 2, it is NP-hard to approximate MaxDBS within a factor n^{1/2 - epsilon}, for any epsilon > 0. In this paper we focus on MaxDBS for the class of unit disk graphs. We provide a polynomial-time constant-factor approximation algorithm for the problem. The approximation ratio of our algorithm does not depend on the diameter d. Even though the algorithm itself is simple, its analysis is rather involved. We combine tools from the theory of hypergraphs with bounded VC-dimension, k-quasi planar graphs, fractional Helly theorems and several geometric properties of unit disk graphs

    Colouring stability two unit disk graphs

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    We prove that every stability two unit disk graph has chromatic number at most 3/2 times its clique number

    New Graph Model for Channel Assignment in Ad Hoc Wireless Networks

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    The channel assignment problem in ad hoc wireless networks is investigated. The problem is to assign channels to hosts in such a way that interference among hosts is eliminated and the total number of channels is minimised. Interference is caused by direct collisions from hosts that can hear each other or indirect collisions from hosts that cannot hear each other, but simultaneously transmit to the same destination. A new class of disk graphs (FDD: interFerence Double Disk graphs) is proposed that include both kinds of interference edges. Channel assignment in wireless networks is a vertex colouring problem in FDD graphs. It is shown that vertex colouring in FDD graphs is NP-complete and the chromatic number of an FDD graph is bounded by its clique number times a constant. A polynomial time approximation algorithm is presented for channel assignment and an upper bound 14 on its performance ratio is obtained. Results from a simulation study reveal that the new graph model can provide a more accurate estimation of the number of channels required for collision avoidance than previous models

    Joint Routing and STDMA-based Scheduling to Minimize Delays in Grid Wireless Sensor Networks

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    In this report, we study the issue of delay optimization and energy efficiency in grid wireless sensor networks (WSNs). We focus on STDMA (Spatial Reuse TDMA)) scheduling, where a predefined cycle is repeated, and where each node has fixed transmission opportunities during specific slots (defined by colors). We assume a STDMA algorithm that takes advantage of the regularity of grid topology to also provide a spatially periodic coloring ("tiling" of the same color pattern). In this setting, the key challenges are: 1) minimizing the average routing delay by ordering the slots in the cycle 2) being energy efficient. Our work follows two directions: first, the baseline performance is evaluated when nothing specific is done and the colors are randomly ordered in the STDMA cycle. Then, we propose a solution, ORCHID that deliberately constructs an efficient STDMA schedule. It proceeds in two steps. In the first step, ORCHID starts form a colored grid and builds a hierarchical routing based on these colors. In the second step, ORCHID builds a color ordering, by considering jointly both routing and scheduling so as to ensure that any node will reach a sink in a single STDMA cycle. We study the performance of these solutions by means of simulations and modeling. Results show the excellent performance of ORCHID in terms of delays and energy compared to a shortest path routing that uses the delay as a heuristic. We also present the adaptation of ORCHID to general networks under the SINR interference model

    Classifying Convex Bodies by Their Contact and Intersection Graphs

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    Distinguishing Classes of Intersection Graphs of Homothets or Similarities of Two Convex Disks

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    For smooth convex disks A, i.e., convex compact subsets of the plane with non-empty interior, we classify the classes G^{hom}(A) and G^{sim}(A) of intersection graphs that can be obtained from homothets and similarities of A, respectively. Namely, we prove that G^{hom}(A) = G^{hom}(B) if and only if A and B are affine equivalent, and G^{sim}(A) = G^{sim}(B) if and only if A and B are similar
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