J. Hwang, T. He, Y. Kim Presented by Shan Gao. Introduction  Target the scenarios where attackers announce phantom nodes.  Phantom node  Fake their.

Slides:



Advertisements
Similar presentations
Distributed Algorithm for a Mobile Wireless Sensor Network for Optimal Coverage of Non-stationary Signals Andrea Kulakov University Sts Cyril and Methodius.
Advertisements

Complex Networks for Representation and Characterization of Images For CS790g Project Bingdong Li 9/23/2009.
Yang Yang, Miao Jin, Hongyi Wu Presenter: Buri Ban The Center for Advanced Computer Studies (CACS) University of Louisiana at Lafayette 3D Surface Localization.
Bidding Protocols for Deploying Mobile Sensors Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic University.
A Distributed Algorithm for the Dead End Problem of Location Based Routing in Sensor Networks Le Zou, Mi Lu, Zixiang Xiong, Department of Electrical Engineering,
Computer Science Dr. Peng NingCSC 774 Adv. Net. Security1 CSC 774 Advanced Network Security Topic 7.3 Secure and Resilient Location Discovery in Wireless.
Exact Inference in Bayes Nets
1 Maximal Independent Set. 2 Independent Set (IS): In a graph G=(V,E), |V|=n, |E|=m, any set of nodes that are not adjacent.
TOPOLOGIES FOR POWER EFFICIENT WIRELESS SENSOR NETWORKS ---KRISHNA JETTI.
Movement-Assisted Sensor Deployment Author : Guiling Wang, Guohong Cao, Tom La Porta Presenter : Young-Hwan Kim.
Topological Hole Detection Ritesh Maheshwari CSE 590.
A Query-Based Routing Tree in Sensor Networks In Chul Song Yohan Roh Dongjoon Hyun Myoung Ho Kim GSN 2006 (Geosensor Network) 1.
Computer Science 1 CSC 774 Advanced Network Security Enhancing Source-Location Privacy in Sensor Network Routing (ICDCS ’05) Brian Rogers Nov. 21, 2005.
Detecting Phantom Nodes in Wireless Sensor Networks Joengmin Hwang Tian He Yongdae Kim Department of Computer Science, University of Minnesota, Minneapolis.
Edith C. H. Ngai1, Jiangchuan Liu2, and Michael R. Lyu1
1 Maximal Independent Set. 2 Independent Set (IS): In a graph, any set of nodes that are not adjacent.
Distinguishing Photographic Images and Photorealistic Computer Graphics Using Visual Vocabulary on Local Image Edges Rong Zhang,Rand-Ding Wang, and Tian-Tsong.
CSE 222 Systems Programming Graph Theory Basics Dr. Jim Holten.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 17th Lecture Christian Schindelhauer.
Scalable and Distributed GPS free Positioning for Sensor Networks Rajagopal Iyengar and Biplab Sikdar Department of ECSE, Rensselaer Polytechnic Institute.
Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces Kavraki, Svestka, Latombe, Overmars 1996 Presented by Chris Allocco.
Clustering Unsupervised learning Generating “classes”
Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling Xiang Ji and Hongyuan Zha Dept. of Computer Science and Engineering,
Target Tracking with Binary Proximity Sensors N. Shrivastava, R. Mudumbai, U. Madhow, S. Suri Presented By Shan Gao.
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.
Efficient Gathering of Correlated Data in Sensor Networks
IPCCC’111 Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks Abdelmajid Khelil, Hanbin Chang, Neeraj Suri.
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha.
Message-Optimal Connected Dominating Sets in Mobile Ad Hoc Networks Paper By: Khaled M. Alzoubi, Peng-Jun Wan, Ophir Frieder Presenter: Ke Gao Instructor:
June 21, 2007 Minimum Interference Channel Assignment in Multi-Radio Wireless Mesh Networks Anand Prabhu Subramanian, Himanshu Gupta.
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.
Hao Yang, Fan Ye, Yuan Yuan, Songwu Lu, William Arbaugh (UCLA, IBM, U. Maryland) MobiHoc 2005 Toward Resilient Security in Wireless Sensor Networks.
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
1 A Bidding Protocol for Deploying Mobile Sensors GuilingWang, Guohong Cao, and Tom LaPorta Department of Computer Science & Engineering The Pennsylvania.
Detecting Phantom Nodes in Wireless Sensor Networks Joengmin Hwang, Tian He, Yongdae Kim (ACM Infocom2007) Presenter : Justin.
1 Maximal Independent Set. 2 Independent Set (IS): In a graph G=(V,E), |V|=n, |E|=m, any set of nodes that are not adjacent.
Gennaro Cordasco - How Much Independent Should Individual Contacts be to Form a Small-World? - 19/12/2006 How Much Independent Should Individual Contacts.
Co-Grid: an Efficient Coverage Maintenance Protocol for Distributed Sensor Networks Guoliang Xing; Chenyang Lu; Robert Pless; Joseph A. O ’ Sullivan Department.
1 A Distributed Architecture for Multimedia in Dynamic Wireless Networks By UCLA C.R. Lin and M. Gerla IEEE GLOBECOM'95.
Localization and Secure Localization. The Problem The determination of the geographical locations of sensor nodes Why do we need Localization? –Manual.
P-Percent Coverage Schedule in Wireless Sensor Networks Shan Gao, Xiaoming Wang, Yingshu Li Georgia State University and Shaanxi Normal University IEEE.
A genetic approach to the automatic clustering problem Author : Lin Yu Tseng Shiueng Bien Yang Graduate : Chien-Ming Hsiao.
REECH ME: Regional Energy Efficient Cluster Heads based on Maximum Energy Routing Protocol Prepared by: Arslan Haider. 1.
1 Shape Segmentation and Applications in Sensor Networks Xianjin Xhu, Rik Sarkar, Jie Gao Department of CS, Stony Brook University INFOCOM 2007.
Random Graph Generator University of CS 8910 – Final Research Project Presentation Professor: Dr. Zhu Presented: December 8, 2010 By: Hanh Tran.
MobiQuitous 2007 Towards Scalable and Robust Service Discovery in Ubiquitous Computing Environments via Multi-hop Clustering Wei Gao.
Localization and Secure Localization. Learning Objectives Understand why WSNs need localization protocols Understand localization protocols in WSNs Understand.
2.1 “Relations & Functions” Relation: a set of ordered pairs. Function: a relation where the domain (“x” value) does NOT repeat. Domain: “x” values Range:
Positioning in Ad-Hoc Networks - A Problem Statement Jan Beutel Computer Engineering and Networks Lab Swiss Federal Institute of Technology (ETH) Zurich.
University “Ss. Cyril and Methodus” SKOPJE Cluster-based MDS Algorithm for Nodes Localization in Wireless Sensor Networks Ass. Biljana Stojkoska.
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
SybilGuard: Defending Against Sybil Attacks via Social Networks.
Performance of Adaptive Beam Nulling in Multihop Ad Hoc Networks Under Jamming Suman Bhunia, Vahid Behzadan, Paulo Alexandre Regis, Shamik Sengupta.
Course14 Dynamic Vision. Biological vision can cope with changing world Moving and changing objects Change illumination Change View-point.
Two Connected Dominating Set Algorithms for Wireless Sensor Networks Overview Najla Al-Nabhan* ♦ Bowu Zhang** ♦ Mznah Al-Rodhaan* ♦ Abdullah Al-Dhelaan*
Mobile Sensor Deployment for a Dynamic Cluster-based Target Tracking Sensor Network Niaoning Shan and Jindong Tan Department of Electrical and Computter.
A Key Management Scheme for Distributed Sensor Networks Laurent Eschaenauer and Virgil D. Gligor.
Selection and Navigation of Mobile Sensor Nodes Using a Sensor Network Atul Verma, Hemjit Sawant and Jindong Tan Department of Electrical and Computer.
Toward Resilient Security in Wireless Sensor Networks Rob Polak Feb CSE 535.
Hao Yang, Fan Ye, Yuan Yuan, Songwu Lu, William Arbaugh (UCLA, IBM, U. Maryland) MobiHoc 2005 Toward Resilient Security in Wireless Sensor Networks.
Repairing Sensor Network Using Mobile Robots Y. Mei, C. Xian, S. Das, Y. C. Hu and Y. H. Lu Purdue University, West Lafayette ICDCS 2006 Speaker : Shih-Yun.
Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods.
A Place-based Model for the Internet Topology Xiaotao Cai Victor T.-S. Shi William Perrizo NDSU {Xiaotao.cai, Victor.shi,
Net 435: Wireless sensor network (WSN)
Physics-based simulation for visual computing applications
Department of Computer Science University of York
Compact routing schemes with improved stretch
Overview: Chapter 4 Infrastructure Establishment
Planting trees in random graphs (and finding them back)
Presentation transcript:

J. Hwang, T. He, Y. Kim Presented by Shan Gao

Introduction  Target the scenarios where attackers announce phantom nodes.  Phantom node  Fake their ranging information  Identify and filter out  A location map for individual nodes  A visual representation on the locations of neighbors of a node

 Prevent phantom nodes from generating consistent ranging claims to multiple honest nodes.  If the phantom nodes generate a set of inconsistent ranging claims, they can be detected.  Only distances to other neighboring nodes are allowed to be claimed, not the location information.

Idea  To prevent phantom nodes generating a set of fake we can:  Accepting any ranging claims, not location claims  Hiding the location information during the ranging phase.

Problem Definition  Nbr(v) neighbor of v and v  D the distance set  measured distance  calculated distance  A set of nodes is consistent, if they can be projected on the unique Euclidean plane, keeping the measured distances among themselves.

Approach  2 phases 1. Distance measurement phase  Each node measures the distances to its neighbors.  TOA, TDOA 2. Filtering phase  Each node projects its neighboring nodes to a virtual local plane to determine the largest consistent subset of nodes.  Eventually, each node establishes a local view without phantom nodes.  Useful in location-based routing and sensing coverage.

1. Distance measurement phase 1. Measures distance to each neighbor through a certain ranging method such as TDOA or TOA. 2. Announces the measured distances. 3. Collect neighbors’ announcement on the measured distances to their neighbors. 4. Compare collected data.  Prevent attack: round robin fashion announcement

2. Filtering phase 1. Each node v randomly picks up 2 neighbors to construct a coordinate system. 2. Use a graph G(V, E) to construct a consistent subset.  If, drop this edge.  The largest connected set V that contains node v is regarded as the largest consistent subset.  ε depends on the noise in the ranging measurement.  Repeat iter times. The cluster with the largest size is chosen as a final result.

Locations of nodes, node 6 is a phantom node. Computed plane from pivot 0, 5, 18 Computed plane from pivot 0, 6, 18

Simulation result

Distribution of number of nodes verified

Thanks Q&A?