Random Geometric Graph Diameter in the Unit Disk

Slides:



Advertisements
Similar presentations
Routing Complexity of Faulty Networks Omer Angel Itai Benjamini Eran Ofek Udi Wieder The Weizmann Institute of Science.
Advertisements

Analysis of a Cone-Based Distributed Topology Control Algorithm for Wireless Multi-hop Networks L. Li, J. Y. Halpern Cornell University P. Bahl, Y. M.
Solving connectivity problems parameterized by treewidth in single exponential time Marek Cygan, Marcin Pilipczuk, Michal Pilipczuk Jesper Nederlof, Dagstuhl.
Chapter 4 Partition I. Covering and Dominating.
The Capacity of Wireless Networks Danss Course, Sunday, 23/11/03.
Secure connectivity of wireless sensor networks Ayalvadi Ganesh University of Bristol Joint work with Santhana Krishnan and D. Manjunath.
Algorithmic and Economic Aspects of Networks Nicole Immorlica.
On the Density of a Graph and its Blowup Raphael Yuster Joint work with Asaf Shapira.
Definitions Distance Diameter Radio Labeling Span Radio Number Gear Graph.
 Distance Problems: › Post Office Problem › Nearest Neighbors and Closest Pair › Largest Empty and Smallest Enclosing Circle  Sub graphs of Delaunay.
Week 5 - Models of Complex Networks I Dr. Anthony Bonato Ryerson University AM8002 Fall 2014.
Random Geometric Graph Diameter in the Unit Disk Robert B. Ellis, Texas A&M University coauthors Jeremy L. Martin, University of Minnesota Catherine Yan,
Shakhar Smorodinsky Courant Institute, New-York University (NYU) On the Chromatic Number of Some Geometric Hypergraphs.
Size of giant component in Random Geometric Graphs
On the Spread of Viruses on the Internet Noam Berger Joint work with C. Borgs, J.T. Chayes and A. Saberi.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 19th Lecture Christian Schindelhauer.
What is the next line of the proof? a). Let G be a graph with k vertices. b). Assume the theorem holds for all graphs with k+1 vertices. c). Let G be a.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Theory of Interferences, Trade-Offs between.
University of CreteCS4831 The use of Minimum Spanning Trees in microarray expression data Gkirtzou Ekaterini.
Geometric Spanners for Routing in Mobile Networks Jie Gao, Leonidas Guibas, John Hershberger, Li Zhang, An Zhu.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Theory of Data Flow and Random Placement.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
UMass Lowell Computer Science Analysis of Algorithms Prof. Karen Daniels Fall, 2001 Lecture 4 Tuesday, 10/2/01 Graph Algorithms: Part 2 Network.
Asymptotic Critical Transmission Radius for Greedy Forward Routing in Wireless Ad Hoc Networks Chih-Wei Yi Submitted to INFOCOM 2006.
Is the following graph Hamiltonian- connected from vertex v? a). Yes b). No c). I have absolutely no idea v.
Dept. of Computer Science Distributed Computing Group Asymptotically Optimal Mobile Ad-Hoc Routing Fabian Kuhn Roger Wattenhofer Aaron Zollinger.
On Neighbors in Geometric Permutations Shakhar Smorodinsky Tel-Aviv University Joint work with Micha Sharir.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
Theory of Computing Lecture 10 MAS 714 Hartmut Klauck.
1 By: MOSES CHARIKAR, CHANDRA CHEKURI, TOMAS FEDER, AND RAJEEV MOTWANI Presented By: Sarah Hegab.
Searching via Your Neighbor’s Neighbor: The Power of Lookahead in P2P Networks Moni Naor Udi Wieder The Weizmann Institute of Science Gurmeet Manku Stanford.
7.1 and 7.2: Spanning Trees. A network is a graph that is connected –The network must be a sub-graph of the original graph (its edges must come from the.
Edge-disjoint induced subgraphs with given minimum degree Raphael Yuster 2012.
Expanders via Random Spanning Trees R 許榮財 R 黃佳婷 R 黃怡嘉.
Center for Graphics and Geometric Computing, Technion 1 Computational Geometry Chapter 8 Arrangements and Duality.
1 Shape Segmentation and Applications in Sensor Networks Xianjin Xhu, Rik Sarkar, Jie Gao Department of CS, Stony Brook University INFOCOM 2007.
CSE 589 Part VI. Reading Skiena, Sections 5.5 and 6.8 CLR, chapter 37.
Approximate Inference: Decomposition Methods with Applications to Computer Vision Kyomin Jung ( KAIST ) Joint work with Pushmeet Kohli (Microsoft Research)
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
Miniconference on the Mathematics of Computation
1 How to burn a graph Anthony Bonato Ryerson University GRASCan 2015.
Random Geometric Graph Model Model for ad hoc/sensor networks n nodes placed in d-dim space Connectivity threshold r Two nodes u,v connected iff ||u-v||
Maximal Independent Set and Connected Dominating Set Xiaofeng Gao Research Group on Mobile Computing and Wireless Networking Univ. of Texas at Dallas.
NOTE: To change the image on this slide, select the picture and delete it. Then click the Pictures icon in the placeholder to insert your own image. Fast.
The geometric GMST problem with grid clustering Presented by 楊劭文, 游岳齊, 吳郁君, 林信仲, 萬高維 Department of Computer Science and Information Engineering, National.
A New Class of Mobility Models for Ad Hoc Wireless Networks Rahul Amin Advisor: Dr. Carl Baum Clemson University SURE 2006.
Introduction Wireless Ad-Hoc Network  Set of transceivers communicating by radio.
The walkers problem J.D., X.Perez, M.Serna, N.Wormald Partially supported by the EC 6th FP : DELIS.
Theory of Computational Complexity Probability and Computing Chapter Hikaru Inada Iwama and Ito lab M1.
Node Distribution of a New Scalable Mobility Model for Ad Hoc Wireless Networks Sheeraz Ahmad.
Khaled M. Alzoubi, Peng-Jun Wan, Ophir Frieder
L. Li, J. Y. Halpern Cornell University
Proof technique (pigeonhole principle)
Topology Control –power control
Minimum Spanning Tree 8/7/2018 4:26 AM
Vapnik–Chervonenkis Dimension
Depth Estimation via Sampling
Introduction Wireless Ad-Hoc Network
On the Geodesic Centers of Polygonal Domains
Miniconference on the Mathematics of Computation
Kinetic Collision Detection for Convex Fat Objects
Computational Geometry
Gaph Theory Planar Graphs
Clustering.
Winter 2019 Lecture 11 Minimum Spanning Trees (Part II)
Discrete Mathematics and its Applications Lecture 5 – Random graphs
Constructing a m-connected k-Dominating Set in Unit Disc Graphs
Locality In Distributed Graph Algorithms
Autumn 2019 Lecture 11 Minimum Spanning Trees (Part II)
Presentation transcript:

Random Geometric Graph Diameter in the Unit Disk Robert B. Ellis, IIT Jeremy L. Martin, Kansas University Catherine Yan, Texas A&M University

Definition of Gp(λ,n) Fix 1 ≤ p ≤ ∞. Randomly place vertices Vn:={ v1,v2,…,vn } in unit disk D (independent identical uniform distributions) {u,v} is an edge iff ||u-v||p ≤ λ. p=∞ λ p=1 λ p=2 λ u B∞(u,λ) B2(u,λ) B1(u,λ) p=1 p=2 p=∞

Motivation Simulate wireless multi-hop networks, Mobile ad hoc networks Provide an alternative to the Erdős-Rényi model for testing heuristics: Traveling salesman, minimal matching, minimal spanning tree, partitioning, clustering, etc. Model systems with intrinsic spatial relationships

Sample of History Kolchin (1978+): asymptotic distributions for the balls-in-bins problem Godehardt, Jaworski (1996): Connectivity/isolated vertices thresholds for d=1 Penrose (1999): k-connectivity  min degree k. An authority: Random Geometric Graphs, Penrose (2003) Franceschetti et al. (2007): Capacity of wireless networks Li, Liu, Li (2008): Multicast capacity of wireless networks

Connectivity Regime If then Gp(λ,n) is superconnected If then Gp(λ,n) is subconnected/disconnected From now on, we take λ of the form where c is constant. Notation. “Almost Always (a.a.), Gp(λ,n) has property P” means:

Threshold for Connectivity Thm (Penrose, `99). Connectivity threshold = min degree 1 threshold. Specifically, Xu := event that u is an isolated vertex. Ignoring boundary effects, Second moment method:

Major Question: Diameter of Gp(λ,n) Assume Gp(λ,n) is connected. Determine Assume Gp(λ,n) is connected. Then almost always, Lower bound. Define diamp(D) := ℓp-diameter of unit disk D ( ) 2 D diam = ¥

Sharpened Lower Bound Prop. Let c>ap-1/2, and choose h(n) such that h(n)/n-2/3  ∞. Then a.a., h(n) << λ Picture for 1≤p≤2 Line ℓ2-distance = 2-2h(n) ℓp-distance = (2-2h(n))21/p-1/2 Proof: examine probability that both caps have a vertex

Diameter Upper Bound, c>ap-1/2 “Lozenge” Lemma (extended from Penrose). Let c>ap-1/2. There exists a k>0 such that a.a., for all u,v in Gp(λ,n), u and v are connected inside the convex hull of B2(u,kλ) U B2(v,kλ). (k+2-1/2)λ kλ u v Bp(·,λ/2) ||u-v||p Corollary. Let c>ap-1/2. There exists a K>0 (independent of p) such that almost always, for all u,v in Gp(λ,n),

Diameter Upper Bound: A Spoke Construction Bp(·,λ/2) Vertices in consecutive gray regions are joined by an edge. ℓ2-distance=r Ap*(r, λ/2):=min area of intersection of two ℓp-balls of radius λ/2 with centers at Euclidean distance r # ℓp-balls in spoke: 2/r

Diameter Upper Bound: A Spoke Construction (con’t) Building a path from u to v: Instantiate Θ(log n) spokes. Suppose every gray region has a vertex. Use “lozenge lemma” to get from u to u’, and v to v’ on nearby spokes. Use spokes to meet at center. u u’ v’ v

A Diameter Upper Bound Theorem. Let 1≤p≤∞ and r = min{λ2-1/2-1/p, λ/2}. Suppose that Then almost always, diam(Gp(λ,n)) ≤ (2·diamp(D)+o(1)) ∕ λ. Proof Sketch. M := #gray regions in all spokes = Θ((2/r)·log n). Pr[a single gray region has no vertex] ≤ (1-Ap*(r, λ/2)/π)n.

Three Improvements Increase average distance of two gray regions in spoke, letting rmin{λ21/2-1/p, λ}. Allow o(1/λ) gray regions to have no vertex and use “lozenge lemma” to take K-step detours around empty regions. Theorem. Let 1≤p≤∞, h(n)/n-2/3  ∞, and c > ap-1/2. Then almost always, diamp(D)(1-h(n))/λ ≤ diam(Gp(λ,n)) ≤ diamp(D)(1+o(1))/λ. By putting ln(n) spokes in parallel with each original spoke, we can get a pairwise distance bound :