TASC: Topology Adaptive Spatial Clustering for Sensor Networks Reino Virrankoski, Dimitrios Lymberopoulos and Andreas Savvides Embedded Networks and Application.

Slides:



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

Bidding Protocols for Deploying Mobile Sensors Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic University.
Infocom'04Ossama Younis, Purdue University1 Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach Ossama Younis and Sonia.
TASC: Topology Adaptive Spatial Clustering for Sensor Networks
1 An Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks Chengfa Li, Mao Ye, Guihai Chen State Key Laboratory for Novel Software.
KAIST Adaptive Triangular Deployment Algorithm for Unattended Mobile Sensor Networks Suho Yang (September 4, 2008) Ming Ma, Yuanyuan Yang IEEE Transactions.
Target Tracking Algorithm based on Minimal Contour in Wireless Sensor Networks Jaehoon Jeong, Taehyun Hwang, Tian He, and David Du Department of Computer.
Tracking a moving object with real-time obstacle avoidance Chung-Hao Chen, Chang Cheng, David Page, Andreas Koschan and Mongi Abidi Imaging, Robotics and.
Globecom 2004 Energy-Efficient Self-Organization for Wireless Sensor Networks: A Fully Distributed approach Liang Zhao, Xiang Hong, Qilian Liang Department.
May 14, Organization Design and Dynamic Resources Huzaifa Zafar Computer Science Department University of Massachusetts, Amherst.
The Impact of Spatial Correlation on Routing with Compression in WSN Sundeep Pattem, Bhaskar Krishnamachri, Ramesh Govindan University of Southern California.
Probability Grid: A Location Estimation Scheme for Wireless Sensor Networks Presented by cychen Date ： 3/7 In Secon (Sensor and Ad Hoc Communications and.
Scalable and Distributed GPS free Positioning for Sensor Networks Rajagopal Iyengar and Biplab Sikdar Department of ECSE, Rensselaer Polytechnic Institute.
Delay Analysis of Large-scale Wireless Sensor Networks Jun Yin, Dominican University, River Forest, IL, USA, Yun Wang, Southern Illinois University Edwardsville,
A Node-Centric Load Balancing Algorithm for Wireless Sensor Networks Hui Dai, Richar Han Department of Computer Science University of Colorado at Boulder.
CS 712 | Fall 2007 Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks Wei Wang, Vikram Srinivasan, Kee-Chaing Chua. National University.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007 (TPDS 2007)
LPT for Data Aggregation in Wireless Sensor Networks Marc Lee and Vincent W.S. Wong Department of Electrical and Computer Engineering, University of British.
Exposure In Wireless Ad-Hoc Sensor Networks Seapahn Meguerdichian Computer Science Department University of California, Los Angeles Farinaz Koushanfar.
Mobility Limited Flip-Based Sensor Networks Deployment Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic.
Lifetime and Coverage Guarantees Through Distributed Coordinate- Free Sensor Activation ACM MOBICOM 2009.
A Sweeper Scheme for Localization and Mobility Prediction in Underwater Acoustic Sensor Networks K. T. DharanC. Srimathi*Soo-Hyun Park VIT University Vellore,
Miao Zhao, Ming Ma and Yuanyuan Yang
2015/10/1 A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks Tai-Jung Chang, Kuochen Wang, Yi-Ling Hsieh Department.
A Distributed Framework for Correlated Data Gathering in Sensor Networks Kevin Yuen, Ben Liang, Baochun Li IEEE Transactions on Vehicular Technology 2008.
SoftCOM 2005: 13 th International Conference on Software, Telecommunications and Computer Networks September 15-17, 2005, Marina Frapa - Split, Croatia.
Minimal Hop Count Path Routing Algorithm for Mobile Sensor Networks Jae-Young Choi, Jun-Hui Lee, and Yeong-Jee Chung Dept. of Computer Engineering, College.
Rate-based Data Propagation in Sensor Networks Gurdip Singh and Sandeep Pujar Computing and Information Sciences Sanjoy Das Electrical and Computer Engineering.
Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department.
IEEE Globecom 2010 Tan Le Yong Liu Department of Electrical and Computer Engineering Polytechnic Institute of NYU Opportunistic Overlay Multicast in Wireless.
Boundary Recognition in Sensor Networks by Topology Methods Yue Wang, Jie Gao Dept. of Computer Science Stony Brook University Stony Brook, NY Joseph S.B.
Distributed Monitoring and Aggregation in Wireless Sensor Networks INFOCOM 2010 Changlei Liu and Guohong Cao Speaker: Wun-Cheng Li.
Design and Analysis of an MST-Based Topology Control Algorithm Ning Li, Jennifer C. Hou, and Lui Sha Department of Computer Science University of Illinois.
Selection and Navigation of Mobile sensor Nodes Using a Sensor Network Atul Verma, Hemjit Sawant and Jindong Tan Department of Electrical and Computer.
Collision-free Time Slot Reuse in Multi-hop Wireless Sensor Networks
1 Shape Segmentation and Applications in Sensor Networks Xianjin Xhu, Rik Sarkar, Jie Gao Department of CS, Stony Brook University INFOCOM 2007.
An Energy-Efficient Voting-Based Clustering Algorithm for Sensor Networks Min Qin and Roger Zimmermann Computer Science Department, Integrated Media Systems.
Bounded relay hop mobile data gathering in wireless sensor networks
A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks IEEE Transactions on Computers, vol. 60, no. 11, November.
A LOCALIZED SELF-HEALING ALGORITHM FOR NETWORKS OF MOVEABLE SENSOR NODES Mohamed Younis, Sookyoung Lee, Sheetal Gupta and Kevin Fisher Department of Computer.
University “Ss. Cyril and Methodus” SKOPJE Cluster-based MDS Algorithm for Nodes Localization in Wireless Sensor Networks Ass. Biljana Stojkoska.
Topological Hole Detection in Wireless Sensor Networks and its Applications Stefan Funke Department of Computer Science, Stanford University, U.S.A. DIAL-M-POMC.
Collaborative Broadcasting and Compression in Cluster-based Wireless Sensor Networks Anh Tuan Hoang and Mehul Motani National University of Singapore Wireless.
A Wakeup Scheme for Sensor Networks: Achieving Balance between Energy Saving and End-to-end Delay Xue Yang, Nitin H.Vaidya Department of Electrical and.
1 Efficient Backbone Synthesis Algorithm for Multi-Radio Wireless Mesh Networks Huei-jiun Ju and Izhak Rubin Electrical Engineering Department University.
Shibo He 、 Jiming Chen 、 Xu Li 、, Xuemin (Sherman) Shen and Youxian Sun State Key Laboratory of Industrial Control Technology, Zhejiang University, China.
An Energy-Efficient Geographic Routing with Location Errors in Wireless Sensor Networks Julien Champ and Clement Saad I-SPAN 2008, Sydney (The international.
Adaptive Tracking in Distributed Wireless Sensor Networks Lizhi Yang, Chuan Feng, Jerzy W. Rozenblit, Haiyan Qiao The University of Arizona Electrical.
GLIDER: Gradient Landmark-Based Distributed Routing for Sensor Networks Qing Fang, Jie Gao, Leonidas J. Guibas, Vin de Silva, Li Zhang Department of Electrical.
Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime Subhasis Bhattacharjee and Nabanita Das International.
An Adaptive Zone-based Storage Architecture for Wireless Sensor Networks Thang Nam Le, Dong Xuan and *Wei Yu Department of Computer Science and Engineering,
Adaptive Triangular Deployment Algorithm for Unattended Mobile Sensor Networks Ming Ma and Yuanyuan Yang Department of Electrical & Computer Engineering.
TreeCast: A Stateless Addressing and Routing Architecture for Sensor Networks Santashil PalChaudhuri, Shu Du, Ami K. Saha, and David B. Johnson Department.
Selection and Navigation of Mobile Sensor Nodes Using a Sensor Network Atul Verma, Hemjit Sawant and Jindong Tan Department of Electrical and Computer.
Distributed Algorithms for Dynamic Coverage in Sensor Networks Lan Lin and Hyunyoung Lee Department of Computer Science University of Denver.
Scalable and Robust Data Dissemination in Wireless Sensor Networks Wei Liu, Yanchao Zhang, Yuguang Fang, Tan Wong Department of Electrical and Computer.
Younghwan Yoo† and Dharma P. Agrawal‡ † School of Computer Science and Engineering, Pusan National University, Busan, KOREA ‡ OBR Center for Distributed.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
A Novel Virtual Anchor Node- based Localization Algorithm for Wireless Sensor Networks Pengxi Liu, Xinming Zhang, Shuang Tian, Zhiwei Zhao, Peng Sun Department.
Dynamic Proxy Tree-Based Data Dissemination Schemes for Wireless Sensor Networks Wensheng Zhang, Guohong Cao and Tom La Porta Department of Computer Science.
Construction of Optimal Data Aggregation Trees for Wireless Sensor Networks Deying Li, Jiannong Cao, Ming Liu, and Yuan Zheng Computer Communications and.
/ 24 1 Deploying Wireless Sensors to Achieve Both Coverage and Connectivity Xiaole Bai Santosh Kumar Dong Xuan Computer Science and Engineering The Ohio.
Reliable Mobicast via Face- Aware Routing Qingfeng Huang,Chenyang Lu and Gruia-Catalin Roman Department of Computer Science and Engineering Washington.
A Spatial-based Multi-resolution Data Dissemination Scheme for Wireless Sensor Networks Jian Chen, Udo Pooch Department of Computer Science Texas A&M University.
Straight Line Routing for Wireless Sensor Networks Cheng-Fu Chou, Jia-Jang Su, and Chao-Yu Chen Computer Science and Information Engineering Dept., National.
Distributed Algorithms for Mobile Sensor Networks
Vineet Mittal Should more be added here Committee Members:
Seema Bandyopadhyay and Edward J. Coyle
Party-by-Night Problem
Department of Computer Science University of York
Presentation transcript:

TASC: Topology Adaptive Spatial Clustering for Sensor Networks Reino Virrankoski, Dimitrios Lymberopoulos and Andreas Savvides Embedded Networks and Application Lab Electrical Engineering Department Yale University, New Haven Infocom 2005

Ju-Mei Li Outline Introduction TASC  Distributed Leader Election  Discovering Local Network Structure Weight computation  Grouping Similar Densities Density reachability Evaluation Conclusion

Ju-Mei Li Introduction A good topology of large-scale sensor networks should help  Sensor nodes coordination  Network management  Data aggregation and compression Goal  Through the development of weights and dynamic density reachablility Topology Adaptive Spatial Clustering Scheme (TASC)

Ju-Mei Li TASC: Distributed Leader Election Input information  2-hop neighborhood  Inter-node distance measurements  Min. cluster size  MinPoints Each node uses input information to compute  Weight  Number of density reachable node Midmost position on each shortest path, biggest weight

Ju-Mei Li TASC: Distributed Leader Election f g b a c e h d i k j BroadcastToNeighborhood(weight) Select the heaviest density reachable node as nominee BroadcastToNeighborhood(nominee) Select the heaviest density reachable node as nominee BroadcastToNeighborhood(nominee) Density reachable nodes of node i = 4 Density reachable nodes of node j = 7 Density reachable nodes of node k = 3 Select the closest nominee as leader BroadcastToNeighborhood(leaderID, nodeID) Select the closest nominee as leader BroadcastToNeighborhood(leaderID, nodeID)

Ju-Mei Li TASC: Distributed Leader Election f g b a c e h d i k j If this node is leader until election timeout; BroadcastToNeighborhood(clustermenbers) If this node is leader until election timeout; BroadcastToNeighborhood(clustermenbers) If clustersize is received If clustersize < min. cluster size = 4 select the closest neighbor for which clustersize ≥ min. cluster size = 4 and joints its cluster BroadcastToNeighborhood(leaderID, clustersize) If clustersize is received If clustersize < min. cluster size = 4 select the closest neighbor for which clustersize ≥ min. cluster size = 4 and joints its cluster BroadcastToNeighborhood(leaderID, clustersize)

Ju-Mei Li TASC: Weight computation ABCDE A-B A-B-C A-B-C-D A-B-C-D-E B-C B-C-D B-C-D-E C-D C-D-E D-E

Ju-Mei Li TASC: Weight computation Including distance in Weight Computation  If node k is found on path from node i to node j in between node a and node b  Then the weight increment of node k is given A B C DEG F H

Ju-Mei Li TASC: Density reachability i Sensing range <= transmission range If MinPoints = m = 3 riri Could be large, equal, or small than sensing range

Ju-Mei Li TASC: Density reachability i a b c jk d e Density reachable nodes of node i : node j, node k, node a, node b, and node c

Ju-Mei Li TASC: Density reachability i k j i k j

Ju-Mei Li TASC: Distributed Leader Election

Ju-Mei Li Evaluation PARSEC 100 random scenarios 100 nodes are deployed on 1000*1000 Measurement range  200, 250, 300, 350, 400 Minimum cluster size: 4 Shortest path is done on each node  Floyd-Warshall algorithm

Ju-Mei Li Evaluation

Ju-Mei Li Evaluation Measurement range: (a)200, (b)300 (a) (b)

Ju-Mei Li Evaluation Measurement range: (a)200, (b)300, (c)400

Ju-Mei Li Evaluation MinPoints = 2 MinPoints = 4 MinPoints = 6

Ju-Mei Li Conclusion This paper proposed a TASC algorithm  Which uses Weight Number of density reachable node  To decompose large network into smaller locally clusters

Thank You!!

Ju-Mei Li TASC: Density reachability i k j i j k

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.