Paper Info
Title
Nonmetric Mds For Sensor Localization
Abstract
Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer errors. From this observation, Nonmetric Multidimensional Scaling (NMDS) based only the rank order of the dissimilarities is proposed in this paper. Different from MDS, NMDS obtain insights into the nature of "perceived" dissimilarities which makes it more suitable to the problem of sensor localization. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of NMDS for sensor localization problem in term of sensor location-estimated error.
Year
DOI
Venue
2008
10.1109/ISWPC.2008.4556237
2008 3RD INTERNATIONAL SYMPOSIUM ON WIRELESS PERVASIVE COMPUTING, VOLS 1-2
Keywords
Field
DocType
wireless sensor networks,estimation,multidimensional scaling,radar tracking,sensor network,euclidean distance,algorithm design and analysis,time difference of arrival,symmetric matrices,matrix decomposition,sensor networks,ubiquitous computing,hardware,time of arrival,stress,spectral decomposition,psychology,reflection,radio frequency,multidimensional systems
Multidimensional scaling,Computer science,Real-time computing,Artificial intelligence,Distributed computing,Time of arrival,Algorithm design,Pattern recognition,Matrix decomposition,Multilateration,RSS,Wireless sensor network,Multidimensional systems
Conference
Citations 
PageRank 
References 
7
0.53
13
Authors
4
Name
Order
Citations
PageRank
Nhat Minh Dinh Vo1336.05
duc vo270.53
Subhash Challa325224.96
Sungyoung Lee42932279.41