Priority Based Enhancement of Online Power-Aware Routing in Wireless Sensor Network. Ronit Nossenson Jerusalem College of Technology
|
|
- Melvyn Bell
- 8 years ago
- Views:
Transcription
1 Priority Based Enhancement of Online Power-Aware Routing in Wireless Sensor Network Ronit Nossenson Jerusalem College of Technology COMCAS
2 What are sensor networks? Infrastructure-less networks Typically connects a large number of small static nodes capable of: Sensing Processing information Storing information Wireless communication Limited power Power Aware routing Ronit Nossenson COMCAS
3 The goal To maximize the network lifetime of on-line power-aware routing algorithm The lifetime of a network with respect to a sequence of messages is the earliest time when a message cannot be sent due to saturated nodes NP-hard problem [Li, Aslam, Rus]: no on-line routing algorithm has a constant competitive ratio in terms of the lifetime of the network Ronit Nossenson COMCAS
4 The optimization problem Let m 1, m 2, be a sequence of messages to be delivered between nodes in the network (on-line routing) We wish to: Maximize the number of delivered messages in the system Subject To: (1) message m s from v i to v j can be delivered iff a) m 1,, m s-1 are successfully delivered; and b) There exists at least one path from v i to v j with enough power to deliver the message m s (2) the total power used to send all messages from node v i does not exceed the initial power of v i Ronit Nossenson COMCAS
5 The means Priority based enhancement to on-line power - aware routing algorithms The node priority assignment is driven from the network connectivity model It represents the importance of the node in the network topology structure Ronit Nossenson COMCAS
6 The induced graph A vertex: a node An edge: a wireless link A vertex weight: the node's power level (finite) An edge weight: the power cost of sending a unit message (w = kd c ) Ronit Nossenson COMCAS
7 Connectivity definitions (1/2) Let G(V,E) be an undirected connected graph A minimal edge-cut C of G is an edge set whose removal disconnects G and removal of any proper part of C does not disconnect G If C = k then C is called a k-cut, 1-cut is also called a bridge Ronit Nossenson COMCAS
8 Connectivity definitions (2/2) Two vertices {u,v} are called k-connected if no k'-cut, k' < k, separates u from v The equivalence classes of this relation are called k-classes The partition of V into the (k+1)-classes is a refinement of the partition of V into k-classes. Thus, the connectivity classes have a hierarchical structure Example: {1,2,3,4,5,6} are in the same 2-class; This 2-class is divided into three 3-classes: {1,2,4,5}, {3}, and {6} Ronit Nossenson COMCAS
9 Connectivity models (1/3) For a k-connected graph, its connectivity model represents both its (k+1)-classes and its k-cuts The well known bridge-tree model of a 1-connected graph represents its bridges and its 2-classes [Westbrook and Tarjan] Ronit Nossenson COMCAS
10 Connectivity models (2/3) The cycle-tree model of a 2-connected graph represents its 2-cuts and its 3-classes [Galil and Italiano] Cycle-tree = each edge participates in one cycle the number of edges is O( V ) v 1 v 2 a) The induced graph v ={v 1,v 2 } b) The cycle-tree Ronit Nossenson COMCAS
11 Connectivity models (3/3) These connectivity models are, in fact, special cases of a more general cactus-tree model [Dinic, Karzanov and Lomonosov] We use: the bridge-tree and the cycle-tree This connectivity model represents two levels of connectivity the 1-classes, their partition refinement of 2-classes, the 1-cuts and the 2-cuts A special case of the two-level cactus-tree model of [Dinitz and Nutov] Ronit Nossenson COMCAS
12 Example Important observation: edges of this connectivity model are real network edges! Ronit Nossenson COMCAS
13 The priority enhancement (1/2) Vertex which is attached to a bridge receives the highest priority (red color) Vertex which is attached to an edge from the cycletree receives a medium priority level (yellow color) Others receive low priority (green color) Ronit Nossenson COMCAS
14 The priority enhancement (2/2) The routing algorithm prefers paths with low priority nodes (a to b) Initial power assignment according to nodes priority Ronit Nossenson COMCAS
15 Connectivity models references E. A. Dinic, A. V. Karzanov and M. V. Lomonosov, On the structure of the system of minimum edge cuts in a graph Ye. Dinitz and Z. Nutov, A 2-level cactus tree model Z. Galil and G. F. Italiano, Maintaining the 3- edge-connected components of a graph on line J. Westbrook and R. E. Tarjan, Maintaining bridge-connected and bi-connected components on line Ronit Nossenson COMCAS
16 Questions? Ronit Nossenson COMCAS
2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8]
Code No: R05220502 Set No. 1 1. (a) Describe the performance analysis in detail. (b) Show that f 1 (n)+f 2 (n) = 0(max(g 1 (n), g 2 (n)) where f 1 (n) = 0(g 1 (n)) and f 2 (n) = 0(g 2 (n)). [8+8] 2. (a)
More informationConnectivity and cuts
Math 104, Graph Theory February 19, 2013 Measure of connectivity How connected are each of these graphs? > increasing connectivity > I G 1 is a tree, so it is a connected graph w/minimum # of edges. Every
More informationThe Binary Blocking Flow Algorithm. Andrew V. Goldberg Microsoft Research Silicon Valley www.research.microsoft.com/ goldberg/
The Binary Blocking Flow Algorithm Andrew V. Goldberg Microsoft Research Silicon Valley www.research.microsoft.com/ goldberg/ Why this Max-Flow Talk? The result: O(min(n 2/3, m 1/2 )mlog(n 2 /m)log(u))
More informationCourse on Social Network Analysis Graphs and Networks
Course on Social Network Analysis Graphs and Networks Vladimir Batagelj University of Ljubljana Slovenia V. Batagelj: Social Network Analysis / Graphs and Networks 1 Outline 1 Graph...............................
More informationThe Binary Blocking Flow Algorithm. Andrew V. Goldberg Microsoft Research Silicon Valley www.research.microsoft.com/ goldberg/
The Binary Blocking Flow Algorithm Andrew V. Goldberg Microsoft Research Silicon Valley www.research.microsoft.com/ goldberg/ Theory vs. Practice In theory, there is no difference between theory and practice.
More informationConnected Identifying Codes for Sensor Network Monitoring
Connected Identifying Codes for Sensor Network Monitoring Niloofar Fazlollahi, David Starobinski and Ari Trachtenberg Dept. of Electrical and Computer Engineering Boston University, Boston, MA 02215 Email:
More informationSocial Media Mining. Graph Essentials
Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures
More informationCIS 700: algorithms for Big Data
CIS 700: algorithms for Big Data Lecture 6: Graph Sketching Slides at http://grigory.us/big-data-class.html Grigory Yaroslavtsev http://grigory.us Sketching Graphs? We know how to sketch vectors: v Mv
More informationResearch Article Robust Monitor Assignment with Minimum Cost for Sensor Network Tomography
Distributed Sensor Networks Volume 2015, Article ID 512463, 6 pages http://dx.doi.org/10.1155/2015/512463 Research Article Robust Monitor Assignment with Minimum Cost for Sensor Network Tomography Xiaojin
More informationDistributed Computing over Communication Networks: Topology. (with an excursion to P2P)
Distributed Computing over Communication Networks: Topology (with an excursion to P2P) Some administrative comments... There will be a Skript for this part of the lecture. (Same as slides, except for today...
More informationGraph Theory and Complex Networks: An Introduction. Chapter 06: Network analysis
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.0, steen@cs.vu.nl Chapter 06: Network analysis Version: April 8, 04 / 3 Contents Chapter
More informationScheduling Shop Scheduling. Tim Nieberg
Scheduling Shop Scheduling Tim Nieberg Shop models: General Introduction Remark: Consider non preemptive problems with regular objectives Notation Shop Problems: m machines, n jobs 1,..., n operations
More informationCMPSCI611: Approximating MAX-CUT Lecture 20
CMPSCI611: Approximating MAX-CUT Lecture 20 For the next two lectures we ll be seeing examples of approximation algorithms for interesting NP-hard problems. Today we consider MAX-CUT, which we proved to
More informationComplexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar
Complexity Theory IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Outline Goals Computation of Problems Concepts and Definitions Complexity Classes and Problems Polynomial Time Reductions Examples
More informationGraph Theoretic Models and Tools for the Analysis of Dynamic Wireless Multihop Networks
Graph Theoretic Models and Tools for the Analysis of Dynamic Wireless Multihop Networks Guoqiang Mao School of Electrical and Information Engineering The University of Sydney National ICT Australia Limited[1],
More informationNetwork (Tree) Topology Inference Based on Prüfer Sequence
Network (Tree) Topology Inference Based on Prüfer Sequence C. Vanniarajan and Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 vanniarajanc@hcl.in,
More informationDiscrete Mathematics & Mathematical Reasoning Chapter 10: Graphs
Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs Kousha Etessami U. of Edinburgh, UK Kousha Etessami (U. of Edinburgh, UK) Discrete Mathematics (Chapter 6) 1 / 13 Overview Graphs and Graph
More information5.1 Bipartite Matching
CS787: Advanced Algorithms Lecture 5: Applications of Network Flow In the last lecture, we looked at the problem of finding the maximum flow in a graph, and how it can be efficiently solved using the Ford-Fulkerson
More informationGraph Theory and Complex Networks: An Introduction. Chapter 06: Network analysis. Contents. Introduction. Maarten van Steen. Version: April 28, 2014
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R.0, steen@cs.vu.nl Chapter 0: Version: April 8, 0 / Contents Chapter Description 0: Introduction
More informationEuler Paths and Euler Circuits
Euler Paths and Euler Circuits An Euler path is a path that uses every edge of a graph exactly once. An Euler circuit is a circuit that uses every edge of a graph exactly once. An Euler path starts and
More informationNetwork Design and Protection Using Network Coding
Network Design and Protection Using Network Coding Salah A. Aly Electrical & Computer Eng. Dept. New Jersey Institute of Technology salah@njit.edu Ahmed E. Kamal Electrical & Computer Eng. Dept. Iowa State
More informationHandout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs
MCS-236: Graph Theory Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010 Chapter 7: Digraphs Strong Digraphs Definitions. A digraph is an ordered pair (V, E), where V is the set
More informationDistributed Computing over Communication Networks: Maximal Independent Set
Distributed Computing over Communication Networks: Maximal Independent Set What is a MIS? MIS An independent set (IS) of an undirected graph is a subset U of nodes such that no two nodes in U are adjacent.
More informationDecentralized Utility-based Sensor Network Design
Decentralized Utility-based Sensor Network Design Narayanan Sadagopan and Bhaskar Krishnamachari University of Southern California, Los Angeles, CA 90089-0781, USA narayans@cs.usc.edu, bkrishna@usc.edu
More informationObjectives. Explain the Role of Redundancy in a Converged Switched Network. Explain the Role of Redundancy in a Converged Switched Network
Implement Spanning Tree Protocols LAN Switching and Wireless Chapter 5 Objectives Explain the role of redundancy in a converged network Summarize how STP works to eliminate Layer 2 loops in a converged
More informationMonitor Placement for Maximal Identifiability in Network Tomography
Monitor Placement for Maximal Identifiability in Network Tomography Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley Imperial College, London, UK. Email: {l.ma10, kin.leung}@imperial.ac.uk
More informationNetwork Metrics, Planar Graphs, and Software Tools. Based on materials by Lala Adamic, UMichigan
Network Metrics, Planar Graphs, and Software Tools Based on materials by Lala Adamic, UMichigan Network Metrics: Bowtie Model of the Web n The Web is a directed graph: n webpages link to other webpages
More informationArchitectural Level Power Consumption of Network on Chip. Presenter: YUAN Zheng
Architectural Level Power Consumption of Network Presenter: YUAN Zheng Why Architectural Low Power Design? High-speed and large volume communication among different parts on a chip Problem: Power consumption
More informationA Survey on Rendezvous Data Collection in Wireless Sensor Networks. Presented by Longfei Shangguan Supervisor:Dr.Yunhao Liu
A Survey on Rendezvous Data Collection in Wireless Sensor Networks Presented by Longfei Shangguan Supervisor:Dr.Yunhao Liu Roadmap Background Introduction of state-of-art solutions Future works References
More informationLink Identifiability in Communication Networks with Two Monitors
Link Identifiability in Communication Networks with Two Monitors Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley Imperial College, London, UK. Email: {l.ma10, kin.leung}@imperial.ac.uk
More informationTopological Properties
Advanced Computer Architecture Topological Properties Routing Distance: Number of links on route Node degree: Number of channels per node Network diameter: Longest minimum routing distance between any
More informationCS 598CSC: Combinatorial Optimization Lecture date: 2/4/2010
CS 598CSC: Combinatorial Optimization Lecture date: /4/010 Instructor: Chandra Chekuri Scribe: David Morrison Gomory-Hu Trees (The work in this section closely follows [3]) Let G = (V, E) be an undirected
More informationV. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer
More informationOn the k-path cover problem for cacti
On the k-path cover problem for cacti Zemin Jin and Xueliang Li Center for Combinatorics and LPMC Nankai University Tianjin 300071, P.R. China zeminjin@eyou.com, x.li@eyou.com Abstract In this paper we
More informationAnalysis of Minimum-Energy Path-Preserving Graphs for Ad-hoc Wireless Networks
Analysis of Minimum-Energy Path-Preserving Graphs for Ad-hoc Wireless Networks Mahmuda Ahmed, Mehrab Shariar, Shobnom Zerin and Ashikur Rahman Department of Computer Science and Engineering Bangladesh
More informationA Tool For Active FLEET Management and Analysis of Activities of a Snow PlowING and a Road Salting Fleet
A Tool For Active FLEET Management and Analysis Activities a Snow PlowING and a Road Salting Fleet Rok Strašek, Tina Vukasović Abstract - Current economic crisis combined with increasing fuel costs rises
More informationOn Optimal Monitor Placement for Localizing Node Failures via Network Tomography
On Optimal Monitor Placement for Localizing Node Failures via Network Tomography Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung IBM T. J. Watson Research Center, Yorktown, NY, USA. Email:
More informationSystem Interconnect Architectures. Goals and Analysis. Network Properties and Routing. Terminology - 2. Terminology - 1
System Interconnect Architectures CSCI 8150 Advanced Computer Architecture Hwang, Chapter 2 Program and Network Properties 2.4 System Interconnect Architectures Direct networks for static connections Indirect
More informationPart 2: Community Detection
Chapter 8: Graph Data Part 2: Community Detection Based on Leskovec, Rajaraman, Ullman 2014: Mining of Massive Datasets Big Data Management and Analytics Outline Community Detection - Social networks -
More informationSmall Maximal Independent Sets and Faster Exact Graph Coloring
Small Maximal Independent Sets and Faster Exact Graph Coloring David Eppstein Univ. of California, Irvine Dept. of Information and Computer Science The Exact Graph Coloring Problem: Given an undirected
More informationGraph theoretic techniques in the analysis of uniquely localizable sensor networks
Graph theoretic techniques in the analysis of uniquely localizable sensor networks Bill Jackson 1 and Tibor Jordán 2 ABSTRACT In the network localization problem the goal is to determine the location of
More informationSCAN: A Structural Clustering Algorithm for Networks
SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu, Nurcan Yuruk, Zhidan Feng (University of Arkansas at Little Rock) Thomas A. J. Schweiger (Acxiom Corporation) Networks scaling: #edges connected
More informationLecture 2.1 : The Distributed Bellman-Ford Algorithm. Lecture 2.2 : The Destination Sequenced Distance Vector (DSDV) protocol
Lecture 2 : The DSDV Protocol Lecture 2.1 : The Distributed Bellman-Ford Algorithm Lecture 2.2 : The Destination Sequenced Distance Vector (DSDV) protocol The Routing Problem S S D D The routing problem
More informationMinimum cost maximum flow, Minimum cost circulation, Cost/Capacity scaling
6.854 Advanced Algorithms Lecture 16: 10/11/2006 Lecturer: David Karger Scribe: Kermin Fleming and Chris Crutchfield, based on notes by Wendy Chu and Tudor Leu Minimum cost maximum flow, Minimum cost circulation,
More informationIE 680 Special Topics in Production Systems: Networks, Routing and Logistics*
IE 680 Special Topics in Production Systems: Networks, Routing and Logistics* Rakesh Nagi Department of Industrial Engineering University at Buffalo (SUNY) *Lecture notes from Network Flows by Ahuja, Magnanti
More information5. A full binary tree with n leaves contains [A] n nodes. [B] log n 2 nodes. [C] 2n 1 nodes. [D] n 2 nodes.
1. The advantage of.. is that they solve the problem if sequential storage representation. But disadvantage in that is they are sequential lists. [A] Lists [B] Linked Lists [A] Trees [A] Queues 2. The
More informationOPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION
OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION Sérgio Pequito, Stephen Kruzick, Soummya Kar, José M. F. Moura, A. Pedro Aguiar Department of Electrical and Computer Engineering
More informationVISUALIZING HIERARCHICAL DATA. Graham Wills SPSS Inc., http://willsfamily.org/gwills
VISUALIZING HIERARCHICAL DATA Graham Wills SPSS Inc., http://willsfamily.org/gwills SYNONYMS Hierarchical Graph Layout, Visualizing Trees, Tree Drawing, Information Visualization on Hierarchies; Hierarchical
More informationThe Basics of Graphical Models
The Basics of Graphical Models David M. Blei Columbia University October 3, 2015 Introduction These notes follow Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. Many figures
More informationEfficient Throughput for Wireless Mesh Networks by CDMA/OVSF Code Assignment
Ad Hoc & Sensor Wireless Networks Vol. 00, pp. 1 27 Reprints available directly from the publisher Photocopying permitted by license only 2008 Old City Publishing, Inc. Published by license under the OCP
More informationNetwork Algorithms for Homeland Security
Network Algorithms for Homeland Security Mark Goldberg and Malik Magdon-Ismail Rensselaer Polytechnic Institute September 27, 2004. Collaborators J. Baumes, M. Krishmamoorthy, N. Preston, W. Wallace. Partially
More informationOn the independence number of graphs with maximum degree 3
On the independence number of graphs with maximum degree 3 Iyad A. Kanj Fenghui Zhang Abstract Let G be an undirected graph with maximum degree at most 3 such that G does not contain any of the three graphs
More informationComputer Communications
Computer Communications 36 (2013) 135 148 Contents lists available at SciVerse ScienceDirect Computer Communications journal homepage: www.elsevier.com/locate/comcom Efficient deployment of wireless sensor
More informationWhat cannot be measured on the Internet? Yvonne-Anne Pignolet, Stefan Schmid, G. Trédan. Misleading stars
: What cannot be measured on the Internet? Yvonne-Anne Pignolet, Stefan Schmid, Gilles Tredan How accurate are network maps? Why? To develop/adapt protocols to Internet PaDIS, RMTP To understand the impact
More informationEnergy Efficient Load Balancing among Heterogeneous Nodes of Wireless Sensor Network
Energy Efficient Load Balancing among Heterogeneous Nodes of Wireless Sensor Network Chandrakant N Bangalore, India nadhachandra@gmail.com Abstract Energy efficient load balancing in a Wireless Sensor
More informationPower Efficiency Metrics for Geographical Routing In Multihop Wireless Networks
Power Efficiency Metrics for Geographical Routing In Multihop Wireless Networks Gowthami.A, Lavanya.R Abstract - A number of energy-aware routing protocols are proposed to provide the energy efficiency
More informationMath 179: Graph Theory
Math 179: Graph Theory Evan Chen May 17, 2015 Notes for the course M179: Introduction to Graph Theory, instructed by Wasin So. 1 1 January 23, 2013 1.1 Logistics Website: www.math.sjsu.edu/~so contains
More informationOutline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits
Outline NP-completeness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2-pairs sum vs. general Subset Sum Reducing one problem to another Clique
More informationHyacinth An IEEE 802.11-based Multi-channel Wireless Mesh Network
Hyacinth An IEEE 802.11-based Multi-channel Wireless Mesh Network 1 Gliederung Einführung Vergleich und Problemstellung Algorithmen Evaluation 2 Aspects Backbone Last mile access stationary commodity equipment
More informationSimplified External memory Algorithms for Planar DAGs. July 2004
Simplified External Memory Algorithms for Planar DAGs Lars Arge Duke University Laura Toma Bowdoin College July 2004 Graph Problems Graph G = (V, E) with V vertices and E edges DAG: directed acyclic graph
More informationProtracted Network: Quality of Service
Protracted Network: Quality of Service Varsha Gautam Abstract The capability of a system to continuously deliver services in compliance with the given requirements in the presence of failures and other
More informationOn the Unique Games Conjecture
On the Unique Games Conjecture Antonios Angelakis National Technical University of Athens June 16, 2015 Antonios Angelakis (NTUA) Theory of Computation June 16, 2015 1 / 20 Overview 1 Introduction 2 Preliminary
More informationSteiner Tree Approximation via IRR. Randomized Rounding
Steiner Tree Approximation via Iterative Randomized Rounding Graduate Program in Logic, Algorithms and Computation μπλ Network Algorithms and Complexity June 18, 2013 Overview 1 Introduction Scope Related
More informationTHE PROBLEM WORMS (1) WORMS (2) THE PROBLEM OF WORM PROPAGATION/PREVENTION THE MINIMUM VERTEX COVER PROBLEM
1 THE PROBLEM OF WORM PROPAGATION/PREVENTION I.E. THE MINIMUM VERTEX COVER PROBLEM Prof. Tiziana Calamoneri Network Algorithms A.y. 2014/15 2 THE PROBLEM WORMS (1)! A computer worm is a standalone malware
More informationTORA : Temporally Ordered Routing Algorithm
TORA : Temporally Ordered Routing Algorithm Invented by Vincent Park and M.Scott Corson from University of Maryland. TORA is an on-demand routing protocol. The main objective of TORA is to limit control
More informationOn 2-vertex-connected orientations of graphs
On 2-vertex-connected orientations of graphs Zoltán Szigeti Laboratoire G-SCOP INP Grenoble, France 12 January 2012 Joint work with : Joseph Cheriyan (Waterloo) and Olivier Durand de Gevigney (Grenoble)
More informationChapter 6: Graph Theory
Chapter 6: Graph Theory Graph theory deals with routing and network problems and if it is possible to find a best route, whether that means the least expensive, least amount of time or the least distance.
More informationCSC 373: Algorithm Design and Analysis Lecture 16
CSC 373: Algorithm Design and Analysis Lecture 16 Allan Borodin February 25, 2013 Some materials are from Stephen Cook s IIT talk and Keven Wayne s slides. 1 / 17 Announcements and Outline Announcements
More informationProtein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics
More informationGraph Theory Applications in Network Security
Graph Theory Applications in Network Security Jonathan Webb1, Fernando Docemmilli2, and Mikhail Bonin3 Theory Lab - Central Queensland University Wayville SA 5034 E-mail addresses: (1) jonwebb@cqu.edu.au
More informationGraph Theory Problems and Solutions
raph Theory Problems and Solutions Tom Davis tomrdavis@earthlink.net http://www.geometer.org/mathcircles November, 005 Problems. Prove that the sum of the degrees of the vertices of any finite graph is
More informationLecture 2 Introduction to Data Flow Analysis
Lecture 2 Introduction to Data Flow Analysis I. Introduction II. Example: Reaching definition analysis III. Example: Liveness analysis IV. A General Framework (Theory in next lecture) Reading: Chapter
More informationA Study of Sufficient Conditions for Hamiltonian Cycles
DeLeon 1 A Study of Sufficient Conditions for Hamiltonian Cycles Melissa DeLeon Department of Mathematics and Computer Science Seton Hall University South Orange, New Jersey 07079, U.S.A. ABSTRACT A graph
More informationNodeXL for Network analysis Demo/hands-on at NICAR 2012, St Louis, Feb 24. Peter Aldhous, San Francisco Bureau Chief. peter@peteraldhous.
NodeXL for Network analysis Demo/hands-on at NICAR 2012, St Louis, Feb 24 Peter Aldhous, San Francisco Bureau Chief peter@peteraldhous.com NodeXL is a template for Microsoft Excel 2007 and 2010, which
More informationSimple Graphs Degrees, Isomorphism, Paths
Mathematics for Computer Science MIT 6.042J/18.062J Simple Graphs Degrees, Isomorphism, Types of Graphs Simple Graph this week Multi-Graph Directed Graph next week Albert R Meyer, March 10, 2010 lec 6W.1
More informationAnalysis of Algorithms, I
Analysis of Algorithms, I CSOR W4231.002 Eleni Drinea Computer Science Department Columbia University Thursday, February 26, 2015 Outline 1 Recap 2 Representing graphs 3 Breadth-first search (BFS) 4 Applications
More informationA 2-factor in which each cycle has long length in claw-free graphs
A -factor in which each cycle has long length in claw-free graphs Roman Čada Shuya Chiba Kiyoshi Yoshimoto 3 Department of Mathematics University of West Bohemia and Institute of Theoretical Computer Science
More informationData Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining
Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004
More informationA Fast Algorithm For Finding Hamilton Cycles
A Fast Algorithm For Finding Hamilton Cycles by Andrew Chalaturnyk A thesis presented to the University of Manitoba in partial fulfillment of the requirements for the degree of Masters of Science in Computer
More informationIRMA: Integrated Routing and MAC Scheduling in Multihop Wireless Mesh Networks
IRMA: Integrated Routing and MAC Scheduling in Multihop Wireless Mesh Networks Zhibin Wu, Sachin Ganu and Dipankar Raychaudhuri WINLAB, Rutgers University 2006-11-16 IAB Research Review, Fall 2006 1 Contents
More informationGraph Powers: Hardness Results, Good Characterizations and Efficient Algorithms. Dissertation
Graph Powers: Hardness Results, Good Characterizations and Efficient Algorithms Dissertation zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) der Fakultät für Informatik und Elektrotechnik
More informationFinding and counting given length cycles
Finding and counting given length cycles Noga Alon Raphael Yuster Uri Zwick Abstract We present an assortment of methods for finding and counting simple cycles of a given length in directed and undirected
More informationInvestment Analysis using the Portfolio Analysis Machine (PALMA 1 ) Tool by Richard A. Moynihan 21 July 2005
Investment Analysis using the Portfolio Analysis Machine (PALMA 1 ) Tool by Richard A. Moynihan 21 July 2005 Government Investment Analysis Guidance Current Government acquisition guidelines mandate the
More informationOption 1: empirical network analysis. Task: find data, analyze data (and visualize it), then interpret.
Programming project Task Option 1: empirical network analysis. Task: find data, analyze data (and visualize it), then interpret. Obtaining data This project focuses upon cocktail ingredients. Data was
More informationComputer Science Department. Technion - IIT, Haifa, Israel. Itai and Rodeh [IR] have proved that for any 2-connected graph G and any vertex s G there
- 1 - THREE TREE-PATHS Avram Zehavi Alon Itai Computer Science Department Technion - IIT, Haifa, Israel Abstract Itai and Rodeh [IR] have proved that for any 2-connected graph G and any vertex s G there
More informationApproximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs
Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs Yong Zhang 1.2, Francis Y.L. Chin 2, and Hing-Fung Ting 2 1 College of Mathematics and Computer Science, Hebei University,
More informationGraph Theory Algorithms for Mobile Ad Hoc Networks
Informatica 36 (2012) 185-200 185 Graph Theory Algorithms for Mobile Ad Hoc Networks Natarajan Meghanathan Department of Computer Science, Jackson State University Jackson, MS 39217, USA E-mail: natarajan.meghanathan@jsums.edu
More informationRegular Augmentations of Planar Graphs with Low Degree
Regular Augmentations of Planar Graphs with Low Degree Bachelor Thesis of Huyen Chau Nguyen At the Department of Informatics Institute of Theoretical Computer Science Reviewers: Advisors: Prof. Dr. Dorothea
More informationNP-complete? NP-hard? Some Foundations of Complexity. Prof. Sven Hartmann Clausthal University of Technology Department of Informatics
NP-complete? NP-hard? Some Foundations of Complexity Prof. Sven Hartmann Clausthal University of Technology Department of Informatics Tractability of Problems Some problems are undecidable: no computer
More informationCapacity of Inter-Cloud Layer-2 Virtual Networking!
Capacity of Inter-Cloud Layer-2 Virtual Networking! Yufeng Xin, Ilya Baldin, Chris Heermann, Anirban Mandal, and Paul Ruth!! Renci, University of North Carolina at Chapel Hill, NC, USA! yxin@renci.org!
More informationNetwork Flow I. Lecture 16. 16.1 Overview. 16.2 The Network Flow Problem
Lecture 6 Network Flow I 6. Overview In these next two lectures we are going to talk about an important algorithmic problem called the Network Flow Problem. Network flow is important because it can be
More informationDATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
More informationA Graph-Theoretic Network Security Game
A Graph-Theoretic Network Security Game Marios Mavronicolas 1, Vicky Papadopoulou 1, Anna Philippou 1, and Paul Spirakis 2 1 Department of Computer Science, University of Cyprus, Nicosia CY-1678, Cyprus.
More informationSocial and Economic Networks: Lecture 1, Networks?
Social and Economic Networks: Lecture 1, Networks? Alper Duman Izmir University Economics, February 26, 2013 Conventional economics assume that all agents are either completely connected or totally isolated.
More informationRouting and Packet Forwarding
Routing and Packet Forwarding Tina Schmidt September 2008 Contents 1 Introduction 1 1.1 The Different Layers of the Internet...................... 2 2 IPv4 3 3 Routing and Packet Forwarding 5 3.1 The Shortest
More informationSubgraph Patterns: Network Motifs and Graphlets. Pedro Ribeiro
Subgraph Patterns: Network Motifs and Graphlets Pedro Ribeiro Analyzing Complex Networks We have been talking about extracting information from networks Some possible tasks: General Patterns Ex: scale-free,
More informationMedial Axis Construction and Applications in 3D Wireless Sensor Networks
Medial Axis Construction and Applications in 3D Wireless Sensor Networks Su Xia, Ning Ding, Miao Jin, Hongyi Wu, and Yang Yang Presenter: Hongyi Wu University of Louisiana at Lafayette Outline Introduction
More informationEnergy Efficient Monitoring in Sensor Networks
Energy Efficient Monitoring in Sensor Networks Amol Deshpande, Samir Khuller, Azarakhsh Malekian, Mohammed Toossi Computer Science Department, University of Maryland, A.V. Williams Building, College Park,
More informationCSE 4351/5351 Notes 7: Task Scheduling & Load Balancing
CSE / Notes : Task Scheduling & Load Balancing Task Scheduling A task is a (sequential) activity that uses a set of inputs to produce a set of outputs. A task (precedence) graph is an acyclic, directed
More informationCHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS
137 CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS 8.1 CONCLUSION In this thesis, efficient schemes have been designed and analyzed to control congestion and distribute the load in the routing process of
More information