Treffer: Theory of semidefinite programming for Sensor Network Localization

Title:
Theory of semidefinite programming for Sensor Network Localization
Authors:
SO, Anthony Man-Cho, YINYU YE
Source:
Large-scale nonlinear and semidefinite programmingMathematical programming. 109(2-3):367-384
Publisher Information:
Heidelberg: Springer, 2007.
Publication Year:
2007
Physical Description:
print, 34 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Géométrie euclidienne, Euclidean geometry, Geometría euclidiana, Localisation, Localization, Localización, Modélisation, Modeling, Modelización, Méthode point intérieur, Interior point method, Método punto interior, Position transducteur, Transducer position, Posición transductor, Programmation mathématique, Mathematical programming, Programación matemática, Programmation semi définie, Semi definite programming, Programacíon semi definida, Réseau capteur, Sensor array, Red sensores, Temps polynomial, Polynomial time, Tiempo polinomial, Théorie graphe, Graph theory, Teoría grafo, Théorie programmation, Programming theory, Euclidean distance geometry, Graph realization, Rigidity theory, 51K05·52C25·68Q25·90C22·90C35, Semidefinite programming, Sensor network localization
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, Stanford University, Stanford, CA 94305, United States
Department of Management Science and Engineering and, by courtesy, Electrical Engineering. Stanford University, Stanford. CA 94305, United States
ISSN:
0025-5610
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18544557
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Operational research. Management
Accession Number:
edscal.18544557
Database:
PASCAL Archive

Weitere Informationen

We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in R2using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.