On Channel-Discontinuity-Constraint Routing in Multi-Channel Wireless Infrastructure Networks Abishek Gopalan, Swaminathan Sankararaman 1.

Slides:



Advertisements
Similar presentations
Maximum flow Main goals of the lecture:
Advertisements

The Capacity of Wireless Networks Danss Course, Sunday, 23/11/03.
Capacity of wireless ad-hoc networks By Kumar Manvendra October 31,2002.
Paths, Trees and Flowers
22C:19 Discrete Math Graphs Fall 2010 Sukumar Ghosh.
IKI 10100: Data Structures & Algorithms Ruli Manurung (acknowledgments to Denny & Ade Azurat) 1 Fasilkom UI Ruli Manurung (Fasilkom UI)IKI10100: Lecture10.
Label Placement and graph drawing Imo Lieberwerth.
Algorithms and Networks
Midwestern State University Department of Computer Science Dr. Ranette Halverson CMPS 2433 CHAPTER 4 - PART 2 GRAPHS 1.
Tradeoffs between performance guarantee and complexity for distributed scheduling in wireless networks Saswati Sarkar University of Pennsylvania Communication.
 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.
MAX FLOW APPLICATIONS CS302, Spring 2013 David Kauchak.
Midterm <  70 3.
Finding a Maximum Matching in Non-Bipartite Graphs Alicia Thilani Singham Goodwin /22/2013.
Interference Considerations for QoS in MANETs Rajarshi Gupta, John Musacchio, Jean Walrand {guptar, musacchj, University of California,
Data Transmission and Base Station Placement for Optimizing Network Lifetime. E. Arkin, V. Polishchuk, A. Efrat, S. Ramasubramanian,V. PolishchukA. EfratS.
Wireless Mesh Networks 1. Architecture 2 Wireless Mesh Network A wireless mesh network (WMN) is a multi-hop wireless network that consists of mesh clients.
Matchings Lecture 3: Jan 18. Bipartite matchings revisited Greedy method doesn’t work (add an edge with both endpoints free)
Scheduling Algorithms for Wireless Ad-Hoc Sensor Networks Department of Electrical Engineering California Institute of Technology. [Cedric Florens, Robert.
Lecture 11. Matching A set of edges which do not share a vertex is a matching. Application: Wireless Networks may consist of nodes with single radios,
CS541 Advanced Networking 1 Routing and Shortest Path Algorithms Neil Tang 2/18/2009.
CS/ENGRD 2110 Object-Oriented Programming and Data Structures Fall 2014 Doug James Lecture 17: Graphs.
Lecture 11. Matching A set of edges which do not share a vertex is a matching. Application: Wireless Networks may consist of nodes with single radios,
The Complexity of Channel Scheduling in Multi-Radio Multi-Channel Wireless Networks Wei Cheng & Xiuzhen Cheng The George Washington University Taieb Znati.
Data Transmission and Base-Station Placement for Optimizing Network Lifetime Esther M. Arkin and Joseph S. B. Mitchell Stony Brook University Swaminathan.
Lecture 12-2: Introduction to Computer Algorithms beyond Search & Sort.
CSE 6590 Fall 2010 Routing Metrics for Wireless Mesh Networks 1 4 October, 2015.
Lecture 16 Maximum Matching. Incremental Method Transform from a feasible solution to another feasible solution to increase (or decrease) the value of.
CSE 6590 Fall 2009 Routing Metrics for Wireless Mesh Networks 1 12 November, 2015.
Data Structures & Algorithms Graphs
MAX FLOW APPLICATIONS CS302, Spring 2012 David Kauchak.
End-to-End Performance and Fairness in Multihop Wireless Backhaul Networks V. Gambiroza, B. Sadeghi, and E. Knightly Rice University.
Bipartite Matching. Unweighted Bipartite Matching.
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
CS223 Advanced Data Structures and Algorithms 1 Maximum Flow Neil Tang 3/30/2010.
A New Link Scheduling Algorithm for Concurrent Tx/Rx Wireless Mesh Networks Author: Kwan-Wu Chin University of Wollongong, Australia From: ICC 2008 Speaker:
Matching Algorithms and Networks. Algorithms and Networks: Matching2 This lecture Matching: problem statement and applications Bipartite matching Matching.
Assignments and matchings Chapter 12 Presented by Yorai Geffen.
Homework - hints Problem 1. Node weights  Edge weights
An Adaptive, High Performance MAC for Long-Distance Multihop Wireless Networks Sergiu Nedevschi *, Rabin K. Patra *, Sonesh Surana *, Sylvia Ratnasamy.
Homework 1 Problem 1: (5 points) Both Dijkstras algorihm and Bellmanford Algorithm generates shortest paths to all destinations. Modify the algorithm to.
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
C&O 355 Lecture 19 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A.
Iterative Improvement for Domain-Specific Problems Lecturer: Jing Liu Homepage:
Optimization Models for Fixed Channel Assignment in Wireless Mesh Networks with Multiple Radios Arindam K. Das, Sumit Roy, SECON Kim Young.
A directed graph G consists of a set V of vertices and a set E of arcs where each arc in E is associated with an ordered pair of vertices from V. V={0,
1 Chapter 7 Network Flow Slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved.
A Maximum Fair Bandwidth Approach for Channel Assignment in Wireless Mesh Networks Bahador Bakhshi and Siavash Khorsandi WCNC 2008.
Matching in bipartite graphs Given: non-weighted bipartite graph not covered node extending alternating path initial matching Algorithm: so-called “extending.
Designing Multi-hop Wireless Backhaul Networks with Delay Guarantees Girija Narlikar, Gordon Wilfong, and Lisa Zhang Bell Lab. Infocom 2006.
Impact of Interference on Multi-hop Wireless Network Performance
Presented by Tae-Seok Kim
Graphs Lecture 19 CS2110 – Spring 2013.
Paths, Trees and Flowers
Algorithms and Networks
Chapter 7 Network Flow Slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved.
CS223 Advanced Data Structures and Algorithms
Graph Algorithm.
Lecture 16 Maximum Matching
Analysis of Algorithms
Lecture 19-Problem Solving 4 Incremental Method
Problem Solving 4.
Flow Networks and Bipartite Matching
Pradeep Kyasanur Nitin H. Vaidya Presented by Chen, Chun-cheng
Advisor: Frank Yeong-Sung Lin, Ph.D. Presented by Yu-Jen Hsieh 謝友仁
Algorithms (2IL15) – Lecture 7
Automated Layout and Phase Assignment for Dark Field PSM
Advisor: Yeong-Sung, Lin, Ph.D. Presented by Yu-Ren, Hsieh
Maximum Flow Neil Tang 4/8/2008
Presentation transcript:

On Channel-Discontinuity-Constraint Routing in Multi-Channel Wireless Infrastructure Networks Abishek Gopalan, Swaminathan Sankararaman 1

Wireless infrastructure networks Wireless infrastructure networks becoming more popular – Backbone may operate in a, while user interface may be on b/g – Increasing throughput in wireless infrastructure networks Simultaneous transmission on multiple orthogonal channels – Use of directional antenna for improved spatial throughput Inter-flow and Intra-flow interference – Inter-flow: Two links belonging to different flows cannot be scheduled at the same time – Intra-flow: Two links belonging to the same flow cannot be scheduled at the same time Routing and channel assignment – Compute path and channel assignment that avoids inter- and intra- flow interference 2

Omnidirectional and Directional transmission Omnidirectional transmission Directional transmission 3

Collinearity (distance-2) constraint Two non-adjacent links cannot be scheduled at the same time X-Y and Z-W transmission cannot take place simultaneously Distance-2 dependency – Logical distance-2; not physical distance-2 – Channel assignment problem is equivalent to distance-2 coloring problem (NP-Hard) Eliminating distance-2 dependency – Use directional transmission – Use power control – Space the nodes sufficiently apart to eliminate side and back lobe interference – Use of metamaterials for shaping the electromagnetic radiation 4

Link and path bandwidth Consider wireless infrastructure network with no distance-2 constraint Wireless interference constraints – A node cannot receive from two different transmitters on the same channel – A node cannot transmit and receive on the same channel Assume bandwidth of a link (for a channel) is B When is the bandwidth of a multi-hop path B? 5 No two consecutive links on the path are assigned the same channel

Routing and channel assignment Channel discontinuity constraint (CDC) No two consecutive links in a path are assigned the same channel A path that obeys the constraint is called CDC path Goal: To obtain the minimum-cost CDC path Example 6 Given a multi-channel wireless network with no collinear interference, the set of available channels at every node, the cost of the links, and a node pair (s, d) find the minimum cost path between s and d along with channel assignment on every link of the path such that no two consecutive links in the path are assigned the same channel.

Edmonds-Szeider expansion Node expansion Link expansion 7

Minimum cost perfect matching (MCPM) Example network and expanded graph Expand all nodes except s and d Complexity: O(ne) 8

CDC expansion Inspired by the channel discontinuity constraint Node expansion Link expansion 9

Looping with CDC expansion Employ Dijkstra’s algorithm with CDC expansion May result in looping 10

Modified expansions If a link has three channels, no need to expand that link Modified ES expansion Modified CDC expansion 11

Finding CDC Paths for Unweighted Graphs No Cost associated with each edge Geometric Setting – – Unit-Disk-Graph Model – Each node has range 1 – Two nodes u and v are connected by an edge if the disks of radius 1 centered at u and v overlap 12 Given a multi-channel wireless network with no collinear interference, the set of available channels at every node, and a node pair (s, d) find the minimum length path between s and d along with channel assignment on every link of the path such that no two consecutive links in the path are assigned the same channel.

Key Observation 13 Expand nodes as before We have a matching M where every vertex except s and d are matched A Minimum Length Alternating Path between s and d gives the Minimum Length CDC path between s and d

Cardinality Matching Problem Maximum Matching – A matching M of Maximum Cardinality General Graphs – Needs to work for both Bipartite and Non- Bipartite Graphs Solved by Jack Edmonds in "Paths Trees and Flowers", Canadian Journal of Math. 1965

Edmonds’ Matching Algorithm Preliminaries – Free Vertices A vertex u is free with respect to a matching M if it is not incident with any edge in M – Alternating Path A path is alternating with respect to a matching M if its edges are alternately in M and not in M – Augmenting Path Alternating Path between two free vertices 15

Edmonds’ Matching Algorithm Theorem: M is not a Maximum Matching if and only if there exists an augmenting path with respect to M Algorithm – 16

Finding an Augmenting Path Modify Breadth-First-Search to follow only Alternating Paths Problem – 17 Starting from 1 yields no path to 6 but one exists

Solution During the modified BFS, if a cycle of odd number of vertices is encountered, it is termed as a blossom Shrink the blossom to a single macrovertex Continue BFS 18

Finding a CDC-Path Find an Augmenting Path between source s and destination d Algorithm is Distributed Communication Complexity – O(n 2 ) Possible Improvements – Improve communication complexity by using a Divide-and-Conquer approach – Transform to Weighted Case 19

Thank You! 20