4 Introduction 1 2 3 5 Carrier-sensing Range Network Model Distributed Data Collection Simulation 6 Conclusion 2.

Slides:

Advertisements

Similar presentations

The Capacity of Wireless Networks Danss Course, Sunday, 23/11/03.

Advertisements

The Capacity of Wireless Networks

Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.

* Distributed Algorithms in Multi-channel Wireless Ad Hoc Networks under the SINR Model Dongxiao Yu Department of Computer Science The University of Hong.

A Centralized Scheduling Algorithm based on Multi-path Routing in WiMax Mesh Network Yang Cao, Zhimin Liu and Yi Yang International Conference on Wireless.

Min Song 1, Yanxiao Zhao 1, Jun Wang 1, E. K. Park 2 1 Old Dominion University, USA 2 University of Missouri at Kansas City, USA IEEE ICC 2009 A High Throughput.

Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting Stephan Olariu Department.

Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.

TDMA Scheduling in Wireless Sensor Networks

Queuing Network Models for Delay Analysis of Multihop Wireless Ad Hoc Networks Nabhendra Bisnik and Alhussein Abouzeid Rensselaer Polytechnic Institute.

July, 2007Simon Fraser University1 Probabilistic Coverage and Connectivity in Wireless Sensor Networks Hossein Ahmadi

Fast Distributed Algorithm for Convergecast in Ad Hoc Geometric Radio Networks Alex Kesselman, Darek Kowalski MPI Informatik.

Dynamic Tuning of the IEEE Protocol to Achieve a Theoretical Throughput Limit Frederico Calì, Marco Conti, and Enrico Gregori IEEE/ACM TRANSACTIONS.

Analyzing Multi-channel MAC Protocols for Underwater Sensor Networks Presenter: Zhong Zhou.

1 Delay-efficient Data Gathering in Sensor Networks Bin Tang, Xianjin Zhu and Deng Pan.

1 Quick Convergecast in ZigBee/IEEE Tree-Based Wireless Sensor Networks Yu-Chee Tseng and Meng-Shiung Pan Department of Computer Science National.

Avoiding Energy Holes in Wireless Sensor Network with Nonuniform Node Distribution Xiaobing Wu, Guihai Chen and Sajal K. Das Parallel and Distributed Systems.

1 On the Performance of Slotted Aloha with Capture Effect in Wireless Networks Arash Behzad and Julan Hsu Professor Mario Gerla CS218 Project UCLA December.

Mobility Increases Capacity In Ad-Hoc Wireless Networks Lecture 17 October 28, 2004 EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor.

On Tree-Based Convergecasting in Wireless Sensor Networks V. Annamalai, S. K. S. Gupta, L. Schwiebert IEEE 2003 Speaker : Chi-Chih Wu.

Mobility Increases The Capacity of Ad-hoc Wireless Networks By Grossglauser and Tse Gautam Pohare Heli Mehta Computer Science University of Southern California.

Yanyan Yang, Yunhuai Liu, and Lionel M. Ni Department of Computer Science and Engineering, Hong Kong University of Science and Technology IEEE MASS 2009.

International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences 1 Cooperative Wireless.

CS 712 | Fall 2007 Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks Wei Wang, Vikram Srinivasan, Kee-Chaing Chua. National University.

Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks Zizhan Zheng Authors: Kai-Wei Fan, Zizhan Zheng and Prasun Sinha.

Lifetime and Coverage Guarantees Through Distributed Coordinate- Free Sensor Activation ACM MOBICOM 2009.

Computing and Communicating Functions over Sensor Networks A.Giridhar and P. R. Kumar Presented by Srikanth Hariharan.

Toward Optimal Utilization of Shared Random Access Channels Joseph (Seffi) Naor, Technion Danny Raz, Technion Gabriel Scalosub, University of Toronto.

When rate of interferer’s codebook small Does not place burden for destination to decode interference When rate of interferer’s codebook large Treating.

By Avinash Sridrahan, Scott Moeller and Bhaskar Krishnamachari.

Mutual Exclusion in Wireless Sensor and Actor Networks IEEE SECON 2006 Ramanuja Vedantham, Zhenyun Zhuang and Raghupathy Sivakumar Presented.

June 21, 2007 Minimum Interference Channel Assignment in Multi-Radio Wireless Mesh Networks Anand Prabhu Subramanian, Himanshu Gupta.

Message-Passing for Wireless Scheduling: an Experimental Study Paolo Giaccone (Politecnico di Torino) Devavrat Shah (MIT) ICCCN 2010 – Zurich August 2.

On Energy-Efficient Trap Coverage in Wireless Sensor Networks Junkun Li, Jiming Chen, Shibo He, Tian He, Yu Gu, Youxian Sun Zhejiang University, China.

Minimum Average Routing Path Clustering Problem in Multi-hop 2-D Underwater Sensor Networks Presented By Donghyun Kim Data Communication and Data Management.

Routing and Scheduling for mobile ad hoc networks using an EINR approach Harshit Arora Advisor : Dr. Harlan Russell Mobile ad Hoc Networks A self-configuring.

Joint Scheduling and Power Control for Wireless Ad Hoc Networks Advisor: 王瑞騰 Student: 黃軍翰.

3 Introduction System Model Distributed Data Collection Simulation and Analysis 5 Conclusion 2.

1 Interplay of Spatial Reuse and SINR-determined Data Rates in CSMACA-based, Multi-hop, Multi- rate Wirless Networks Ting-Yu Lin and Jennifer C. Hou Department.

Converge-Cast: On the Capacity and Delay Tradeoffs Xinbing Wang Luoyi Fu Xiaohua Tian Qiuyu Peng Xiaoying Gan Hui Yu Jing Liu Department of Electronic.

Advanced Communication Network Joint Throughput Optimization for Wireless Mesh Networks R 戴智斌 R 蔡永斌 Xiang-Yang.

Secure and Energy-Efficient Disjoint Multi-Path Routing for WSNs Presented by Zhongming Zheng.

Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks I-Hong Hou.

Whitespace Measurement and Virtual Backbone Construction for Cognitive Radio Networks: From the Social Perspective Shouling Ji and Raheem Beyah Georgia.

Junfeng Xu, Keqiu Li, and Geyong Min IEEE Globecom 2010 Speak: Huei-Rung, Tsai Layered Multi-path Power Control in Underwater Sensor Networks.

4 Introduction Semi-Structure Routing Framework System Model Performance Analytical Framework Simulation 6 Conclusion.

A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks IEEE Transactions on Computers, vol. 60, no. 11, November.

4 Introduction Broadcasting Tree and Coloring System Model and Problem Definition Broadcast Scheduling Simulation 6 Conclusion and Future Work.

Copyright © 2011, Scalable and Energy-Efficient Broadcasting in Multi-hop Cluster-Based Wireless Sensor Networks Long Cheng ∗ †, Sajal K. Das†,

Variable Bandwidth Allocation Scheme for Energy Efficient Wireless Sensor Network SeongHwan Cho, Kee-Eung Kim Korea Advanced Institute of Science and Technology.

Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation Yanwei Wu, Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, YunHao Liu, Senior.

4 Introduction Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.

Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree- Based Wireless Sensor Networks Sk Kajal Arefin Imon, Adnan Khan, Mario Di Francesco,

Coverage and Scheduling in Wireless Sensor Networks Yong Hwan Kim Korea University of Technology and Education Laboratory of Intelligent.

A Bandwidth Scheduling Algorithm Based on Minimum Interference Traffic in Mesh Mode Xu-Yajing, Li-ZhiTao, Zhong-XiuFang and Xu-HuiMin International Conference.

GholamHossein Ekbatanifard, Reza Monseﬁ, Mohammad H. Yaghmaee M., Seyed Amin Hosseini S. ELSEVIER Computer Networks 2012 Queen-MAC: A quorum-based energy-efﬁcient.

1 Grid-Based Access Scheduling for Mobile Data Intensive Sensor Networks C.-K. Lin, V. Zadorozhny and P. Krishnamurthy IEEE International Conference on.

1 Low Latency Multimedia Broadcast in Multi-Rate Wireless Meshes Chun Tung Chou, Archan Misra Proc. 1st IEEE Workshop on Wireless Mesh Networks (WIMESH),

A Bit-Map-Assisted Energy- Efficient MAC Scheme for Wireless Sensor Networks Jing Li and Georgios Y. Lazarou Department of Electrical and Computer Engineering,

I owa S tate U niversity Laboratory for Advanced Networks (LAN) Coverage and Connectivity Control of Wireless Sensor Networks under Mobility Qiang QiuAhmed.

-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.

Presented by Tae-Seok Kim

Computing and Compressive Sensing in Wireless Sensor Networks

Abdul Kader Kabbani (Stanford University)

Introduction Secondary Users (SUs) Primary Users (PUs)

IEEE Student Paper Contest

Outline Introduction Network Model and Problem Formulation

Totally Disjoint Multipath Routing in Multihop Wireless Networks Sonia Waharte and Raoef Boutaba Presented by: Anthony Calce.

Throughput-Optimal Broadcast in Dynamic Wireless Networks

Pradeep Kyasanur Nitin H. Vaidya Presented by Chen, Chun-cheng

Presentation transcript:

4 Introduction Carrier-sensing Range Network Model Distributed Data Collection Simulation 6 Conclusion 2

3

 Centralized algorithms vs. Distributed algorithms in WSNs  Challenges in Distributed and asynchronous data collection  C1: only local information is available  C2: how to avoid disadvantages introduced by time asynchronization  C3: how to theoretical analyze a distributed and asynchronous data collection algorithm  Contributions  Derive a under the generalized physical interference model for each node  Propose a scalable and order-optimal Distributed Data Collection (DDC) algorithm  Simulations are conducted to validate DDC 4

5

 n sensor nodes, s i (1≤i≤n), i.i.d. in a square area  transmission radius r,  Single radio, one common channel with bandwidth W bits/second   Interference model 6

7

 R 0 -feasible state: a set of active nodes, s.t. all the nodes in this set can conduct data transmission simultaneously and each has a data transmitting rate no less than R 0.  R-set (S R ): assume R is the carrier-sensing range. An R-set S R is any maximal subset of V s.t. any two nodes in S R has a distance of at least R.  R 0 -Proper Carrier-sensing Range (R 0 -PCR): the carrier-sensing range R is a R 0 -PCR if for any R-set S R, it is always a R 0 -feasible state. 8

 How to decide the proper carrier-sensing range? . (Theorem 1) 9

10

 Connected Dominating Set (CDS)-based data collection tree 11

 Distributed Data Collection (DDC) algorithm 12

 Analysis  Any node with data for transmission can transmit at least one data packet within time. (Theorem 2)  The time consumption of collecting all the data packets at to is upper bounded by. (Corollary 2)  After time, it takes DDC at most time to collection all the data to the sink. (Theorem 3)  The lower bound of data collection capacity achieved by DDC is, which is scalable and order optimal. (Theorem 4) 13

14

 Network setting  Power = 1, data packet size = 1, time slot = 1, and ……  Multi-Path Scheduling (MPS) algorithm [7] 15

 Capacity 16

 Scalability 17

 We study the distributed data collection problem in asynchronous WSNs  We propose a Distributed Data Collection (DDC) algorithm, which is scalable and order-optimal  Simulations are conducted to validate the performance of DDC 18