Greedy sensor selection based on QR factorization

Abstract We address the problem of selecting a given number of sensor nodes in wireless sensor networks where noise-corrupted linear measurements are collected at the selected nodes to estimate the unknown parameter. Noting that this problem is combinatorial in nature and selection of sensor nodes f...

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Autor principal:	Yoon Hak Kim
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Greedy algorithm Sensor selection Linear inverse problem Near-optimality QR factorization Telecommunication TK5101-6720 Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/c79239bb16b34a3fa90ba52b6cfe7dc9
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spelling	oai:doaj.org-article:c79239bb16b34a3fa90ba52b6cfe7dc92021-12-05T12:10:30ZGreedy sensor selection based on QR factorization10.1186/s13634-021-00824-51687-6180https://doaj.org/article/c79239bb16b34a3fa90ba52b6cfe7dc92021-12-01T00:00:00Zhttps://doi.org/10.1186/s13634-021-00824-5https://doaj.org/toc/1687-6180Abstract We address the problem of selecting a given number of sensor nodes in wireless sensor networks where noise-corrupted linear measurements are collected at the selected nodes to estimate the unknown parameter. Noting that this problem is combinatorial in nature and selection of sensor nodes from a large number of nodes would require unfeasible computational cost, we propose a greedy sensor selection method that seeks to choose one node at each iteration until the desired number of sensor nodes are selected. We first apply the QR factorization to make the mean squared error (MSE) of estimation a simplified metric which is iteratively minimized. We present a simple criterion which enables selection of the next sensor node minimizing the MSE at iterations. We discuss that a near-optimality of the proposed method is guaranteed by using the approximate supermodularity and also make a complexity analysis for the proposed algorithm in comparison with different greedy selection methods, showing a reasonable complexity of the proposed method. We finally run extensive experiments to investigate the estimation performance of the different selection methods in various situations and demonstrate that the proposed algorithm provides a good estimation accuracy with a competitive complexity when compared with the other novel greedy methods.Yoon Hak KimSpringerOpenarticleGreedy algorithmSensor selectionLinear inverse problemNear-optimalityQR factorizationTelecommunicationTK5101-6720ElectronicsTK7800-8360ENEURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Greedy algorithm Sensor selection Linear inverse problem Near-optimality QR factorization Telecommunication TK5101-6720 Electronics TK7800-8360
spellingShingle	Greedy algorithm Sensor selection Linear inverse problem Near-optimality QR factorization Telecommunication TK5101-6720 Electronics TK7800-8360 Yoon Hak Kim Greedy sensor selection based on QR factorization
description	Abstract We address the problem of selecting a given number of sensor nodes in wireless sensor networks where noise-corrupted linear measurements are collected at the selected nodes to estimate the unknown parameter. Noting that this problem is combinatorial in nature and selection of sensor nodes from a large number of nodes would require unfeasible computational cost, we propose a greedy sensor selection method that seeks to choose one node at each iteration until the desired number of sensor nodes are selected. We first apply the QR factorization to make the mean squared error (MSE) of estimation a simplified metric which is iteratively minimized. We present a simple criterion which enables selection of the next sensor node minimizing the MSE at iterations. We discuss that a near-optimality of the proposed method is guaranteed by using the approximate supermodularity and also make a complexity analysis for the proposed algorithm in comparison with different greedy selection methods, showing a reasonable complexity of the proposed method. We finally run extensive experiments to investigate the estimation performance of the different selection methods in various situations and demonstrate that the proposed algorithm provides a good estimation accuracy with a competitive complexity when compared with the other novel greedy methods.
format	article
author	Yoon Hak Kim
author_facet	Yoon Hak Kim
author_sort	Yoon Hak Kim
title	Greedy sensor selection based on QR factorization
title_short	Greedy sensor selection based on QR factorization
title_full	Greedy sensor selection based on QR factorization
title_fullStr	Greedy sensor selection based on QR factorization
title_full_unstemmed	Greedy sensor selection based on QR factorization
title_sort	greedy sensor selection based on qr factorization
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/c79239bb16b34a3fa90ba52b6cfe7dc9
work_keys_str_mv	AT yoonhakkim greedysensorselectionbasedonqrfactorization
_version_	1718372218498449408

Greedy sensor selection based on QR factorization

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