Minimizing interference for the highway model in Wireless Ad-hoc and Sensor Networks Haisheng Tan, Tiancheng, Lou, Francis C.M. Lau, YuexuanWang, Shiteng.

Slides:



Advertisements
Similar presentations
Energy-Efficient Distributed Algorithms for Ad hoc Wireless Networks Gopal Pandurangan Department of Computer Science Purdue University.
Advertisements

Costas Busch Louisiana State University CCW08. Becomes an issue when designing algorithms The output of the algorithms may affect the energy efficiency.
Interference and Topology Control Does Topology Control Reduce Interference ? Feb 15, 2010 University of Freiburg H. K. Al-Hasani Seminar Ad Hoc Netzwerke.
Minimum Energy Mobile Wireless Networks IEEE JSAC 2001/10/18.
S. J. Shyu Chap. 1 Introduction 1 The Design and Analysis of Algorithms Chapter 1 Introduction S. J. Shyu.
Haiming Jin, He Huang, Lu Su and Klara Nahrstedt University of Illinois at Urbana-Champaign State University of New York at Buffalo October 22, 2014 Cost-minimizing.
PCPs and Inapproximability Introduction. My T. Thai 2 Why Approximation Algorithms  Problems that we cannot find an optimal solution.
Does Topology Control Reduce Interference? Martin Burkhart Pascal von Rickenbach Roger Wattenhofer Aaron Zollinger.
Ad-Hoc Networks Beyond Unit Disk Graphs
XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks
Errol Lloyd Design and Analysis of Algorithms Approximation Algorithms for NP-complete Problems Bin Packing Computer Networks.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
1 Complexity of Network Synchronization Raeda Naamnieh.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 21st Lecture Christian Schindelhauer.
1 Minimum-energy broadcasting in multi-hop wireless networks using a single broadcast tree Department of Computer Science and Information Engineering National.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 18th Lecture Christian Schindelhauer.
Fast Distributed Algorithm for Convergecast in Ad Hoc Geometric Radio Networks Alex Kesselman, Darek Kowalski MPI Informatik.
A Robust Interference Model for Wireless Ad-Hoc Networks Pascal von Rickenbach Stefan Schmid Roger Wattenhofer Aaron Zollinger.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
Deployment of Surface Gateways for Underwater Wireless Sensor Networks Saleh Ibrahim Advising Committee Prof. Reda Ammar Prof. Jun-Hong Cui Prof. Sanguthevar.
1 Delay-efficient Data Gathering in Sensor Networks Bin Tang, Xianjin Zhu and Deng Pan.
Energy-Efficient Target Coverage in Wireless Sensor Networks Mihaela Cardei, My T. Thai, YingshuLi, WeiliWu Annual Joint Conference of the IEEE Computer.
Ad Hoc and Sensor Networks – Roger Wattenhofer –3/1Ad Hoc and Sensor Networks – Roger Wattenhofer – Topology Control Chapter 3 TexPoint fonts used in EMF.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Mobility (III) 12th Week
1 Caching/storage problems and solutions in wireless sensor network Bin Tang CSE 658 Seminar on Wireless and Mobile Networking.
1 TTS: A Two-Tiered Scheduling Algorithm for Effective Energy Conservation in Wireless Sensor Networks Nurcan Tezcan & Wenye Wang Department of Electrical.
WiOpt’04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks March 24-26, 2004, University of Cambridge, UK Session 2 : Energy Management.
[1][1][1][1] Lecture 2-3: Coping with NP-Hardness of Optimization Problems in Practice May 26 + June 1, Introduction to Algorithmic Wireless.
1 Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network Prof. Yu-Chee Tseng Department of Computer Science National Chiao-Tung University.
Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) Wireless Sensor Networks:
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha Presented by Ray Lam Oct 23, 2004.
GS 3 GS 3 : Scalable Self-configuration and Self-healing in Wireless Networks Hongwei Zhang & Anish Arora.
On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithms National.
Hongyu Gong, Lutian Zhao, Kainan Wang, Weijie Wu, Xinbing Wang
Energy Efficient Routing and Self-Configuring Networks Stephen B. Wicker Bart Selman Terrence L. Fine Carla Gomes Bhaskar KrishnamachariDepartment of CS.
Simple and Improved Parameterized Algorithms for Multiterminal Cuts Mingyu Xiao The Chinese University of Hong Kong Hong Kong SAR, CHINA CSR 2008 Presentation,
Efficient Gathering of Correlated Data in Sensor Networks
IEEE Globecom 2010 Tan Le Yong Liu Department of Electrical and Computer Engineering Polytechnic Institute of NYU Opportunistic Overlay Multicast in Wireless.
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
Prediction-based Object Tracking and Coverage in Visual Sensor Networks Tzung-Shi Chen Jiun-Jie Peng,De-Wei Lee Hua-Wen Tsai Dept. of Com. Sci. and Info.
Ad Hoc and Sensor Networks – Roger Wattenhofer –3/1Ad Hoc and Sensor Networks – Roger Wattenhofer – Topology Control Chapter 3 TexPoint fonts used in EMF.
On Graphs Supporting Greedy Forwarding for Directional Wireless Networks W. Si, B. Scholz, G. Mao, R. Boreli, et al. University of Western Sydney National.
Combinatorial Auctions with Structured Item Graphs Vincent Conitzer, Jonathan Derryberry, and Tuomas Sandholm Computer Science Department Carnegie Mellon.
Trade-offs Between Mobility and Density for Coverage in Wireless Sensor Networks Wei Wang, Vikram Srinivasan and Kee-Chaing Chua National University of.
Ad Hoc and Sensor Networks – Roger Wattenhofer –4/1Ad Hoc and Sensor Networks – Roger Wattenhofer – Topology Control Chapter 4 TexPoint fonts used in EMF.
Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Efficient k-Coverage Algorithms for Wireless Sensor Networks Mohamed Hefeeda.
SenProbe: Path Capacity Estimation in Wireless Sensor Networks Tony Sun, Ling-Jyh Chen, Guang Yang M. Y. Sanadidi, Mario Gerla.
Guinian Feng, Pingyi Fan Dept. of Electronic Engineerin Tsinghua University Beijing, China Soung Chang Liew Dept. of Information Engineering Chinese University.
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
Maximizing Lifetime per Unit Cost in Wireless Sensor Networks
Minimizing Energy Expense for Chain-Based Data Gathering in Wireless Sensor Networks Li-Hsing Yen Chung Hua University Taiwan EWSN 05.
Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion.
Efficient Point Coverage in Wireless Sensor Networks Jie Wang and Ning Zhong Department of Computer Science University of Massachusetts Journal of Combinatorial.
Construction of Optimal Data Aggregation Trees for Wireless Sensor Networks Deying Li, Jiannong Cao, Ming Liu, and Yuan Zheng Computer Communications and.
Introduction Wireless Ad-Hoc Network  Set of transceivers communicating by radio.
Errol Lloyd Design and Analysis of Algorithms Approximation Algorithms for NP-complete Problems Bin Packing Networks.
1 HEINZ NIXDORF INSTITUTE University of Paderborn Algorithms and Complexity Christian Schindelhauer Algorithms for Radio Networks Winter Term 2005/2006.
Wireless Sensor Network: A Promising Approach for Distributed Sensing Tasks.
Prof. Yu-Chee Tseng Department of Computer Science
Does Topology Control Reduce Interference?
Topology Control –power control
A Distributed Algorithm for Minimum-Weight Spanning Trees
Dynamic Coverage In Wireless Ed-Hoc Sensor Networks
Research: algorithmic solutions for networking
Robustness of wireless ad hoc network topologies
Robustness of wireless ad hoc network topologies
Introduction Wireless Ad-Hoc Network
Minimizing the Aggregate Movements for Interval Coverage
The Coverage Problem in a Wireless Sensor Network
Survey on Coverage Problems in Wireless Sensor Networks - 2
Presentation transcript:

Minimizing interference for the highway model in Wireless Ad-hoc and Sensor Networks Haisheng Tan, Tiancheng, Lou, Francis C.M. Lau, YuexuanWang, Shiteng Chen CS, The University of Hong Kong, Hong Kong, China ITCS, Tsinghua University, Beijing, China Jan. 25 th, SOFSEM, 2011

Outline Introduction Problem Definitions Minimizing the Average Interference Minimizing the Maximum Interference Discussions and Future work Q & A

Introduction Wireless Ad hoc and Sensor Networks

Introduction Wireless Ad hoc and Sensor Networks Environmental monitoring, intrusion detection, health care, etc. Smart Earth (IBM), Sense China …

Introduction Energy !

Introduction Energy ! Interference

Introduction Energy ! Interference Receiver-centric interference transmission radius of u

Problem Definitions the average interference of a graph G the maximum interference of a graph G

Problem Definitions the average interference of a graph G the maximum interference of a graph G Problems: Given nodes arbitrarily deployed along a 1D line (the highway model) Connected Min-Avg or Min-max interference The optimal solution is actually a spanning tree.

Observations

small node degrees

Observations small node degrees sparse topology

Observations small node degrees sparse topology Nearest Neighbor Forest (each node is connected to its nearest neighbor)

Observations small node degrees  sparse topology  Nearest Neighbor Forest (each node is connected to its nearest neighbor)  a) b) c)

Minimizing the Average Interference In 2D networks: an asymptotically optimal algorithm with an approximation ratio of O(log n) (Moscibroda et al. 2005)

Minimizing the Average Interference In 2D networks: an asymptotically optimal algorithm with an approximation ratio of O(log n) (Moscibroda et al. 2005) In the highway model (Our work): a polynomial-time exact algorithm

Minimizing the Average Interference In 2D networks: an asymptotically optimal algorithm with an approximation ratio of O(log n) (Moscibroda et al. 2005) In the highway model (Our work): 1. No-cross property

Minimizing the Average Interference In 2D networks: an asymptotically optimal algorithm with an approximation ratio of O(log n) (Moscibroda et al. 2005) In the highway model (Our work): 1. No-cross property when |ac| <=|bc|+|cd| 

Minimizing the Average Interference In the highway model: 2. Calculate the total interference via the interference created by each node

Minimizing the Average Interference In the highway model: 2. Calculate the total interference via the interference created by each node

Minimizing the Average Interference In the highway model: 2. Calculate the total interference via the interference created by each node Independent sub-problems

Minimizing the Average Interference Two questions: How to divide the whole line into sub-segments How to connect the nodes inside each segment

Minimizing the Average Interference Two questions: How to divide the whole line into sub-segments How to connect the nodes inside each segment Functions for DP F(s,t), s<t, which is short for Compute the minimum total interference created by the nodes from s+1 to t-1, such that

Minimizing the Average Interference Two questions: How to divide the whole line into sub-segments How to connect the nodes inside each segment Functions for DP F(s,t), s<t, which is short for OR

Minimizing the Average Interference Two questions: How to divide the whole line into sub-segments How to connect the nodes inside each segment Functions for DP F(s,t), s<t, which is short for OR

Minimizing the Average Interference Functions for DP G(s,t), s<t Compute the minimum total interference created by nodes from s +1 to t-1, such that

Minimizing the Average Interference Functions for DP G(s,t), s<t

Minimizing the Average Interference Functions for DP G(s,t), s<t

Minimizing the Average Interference Functions for DP G(s,t), s<t The minimum average interference

Minimizing the Average Interference Correctness Verified by the brute-force search running in time the maximum node degree

Minimizing the Average Interference Correctness Verified by the brute-force search running in time Time complexity: the maximum node degree

Minimizing the Average Interference Correctness Verified by the brute-force search running in time Time complexity: (the numbers are the interference created by the nodes) the maximum node degree

Minimizing the Average Interference Correctness Verified by the brute-force search running in time Time complexity: (the numbers are the interference created by the nodes) Can we do better ?? Y! the maximum node degree

Minimizing the Maximum Interference Harder!! No-cross property: still holds

Minimizing the Maximum Interference Harder!! No-cross property: still holds Independent sub-segments: not found 

Minimizing the Maximum Interference Harder!! No-cross property: still holds Independent sub-segments: not found  In 2D networks: NP-hard (Buchin 2008) Bounded in

Minimizing the Maximum Interference Harder!! No-cross property: still holds Independent sub-segments: not found  In 2D networks: NP-hard (Buchin 2008) Bounded in In 1D networks: An appr. with ratio (von Richenbach, et al. 2005) A sub-exponential-time exact algorithm (Our work )

Minimizing the Maximum Interference Check whether the min-max can be k, where 1<k<n

Minimizing the Maximum Interference Check whether the min-max can be k, where 1<k<n A skeleton : Record the nodes from s to t that can interfere with nodes outside [s,t] with their transmission radii

Minimizing the Maximum Interference Check whether the min-max can be k, where 1<k<n A skeleton : Record the nodes from s to t that can interfere with nodes outside [s,t] with their transmission radii

Minimizing the Maximum Interference Check whether the min-max can be k, where 1<k<n A skeleton : Record the nodes from s to t that can interfere with nodes outside [s,t] with their transmission radii

Minimizing the Maximum Interference Functions: boolean F*(s,t), which is short for

Minimizing the Maximum Interference Functions: boolean F*(s,t), which is short for OR

Minimizing the Maximum Interference Functions: boolean F*(s,t), which is short for OR

Minimizing the Maximum Interference Functions: boolean G*(s,t)

Minimizing the Maximum Interference Functions: boolean G*(s,t)

Minimizing the Maximum Interference Functions: boolean G*(s,t)

Minimizing the Maximum Interference Functions: boolean G*(s,t) Check the whole line

Minimizing the Maximum Interference Time complexity # of the different valid skeletons for a segment from s to t, where s>0 and t<n-1:

Minimizing the Maximum Interference Time complexity # of the different valid skeletons for a segment from s to t, where s>0 and t<n-1: Time complexity:

Minimizing the Maximum Interference Time complexity # of the different valid skeletons for a segment from s to t, where s>0 and t<n-1: Time complexity: Can we do better? No idea yet 

Discussion and Future work Planarity Multiple optimal spanning trees the min-max for the 6-node exponential chain

Discussion and Future work Planarity Multiple optimal spanning trees Is min-max in 1D NP-hard? How about 3D networks? How to design efficient approximations to minimize the maximum in 2D networks? How to tackle interference minimization with other network properties, such as small node degree and spanner? …

Q & A Thanks!