Result: Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

Title:
Greedy distributed node selection for node-specific signal estimation in wireless sensor networks
Authors:
SZURLEY, Joseph, BERTRAND, Alexander, RUCKEBUSCH, Peter, MOERMAN, Ingrid, MOONEN, Marc
Source:
Signal processing. 94:57-73
Publisher Information:
Amsterdam: Elsevier, 2014.
Publication Year:
2014
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Algorithme, Algorithm, Algoritmo, Borne inférieure, Lower bound, Cota inferior, Borne supérieure, Upper bound, Cota superior, Complexité calcul, Computational complexity, Complejidad computación, Détection signal, Signal detection, Detección señal, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Estimation signal, Signal estimation, Estimación señal, Méthode combinatoire, Combinatorial method, Método combinatorio, Méthode itérative, Iterative method, Método iterativo, Problème NP difficile, NP hard problem, Problema NP duro, Réseau capteur, Sensor array, Red sensores, Réseau sans fil, Wireless network, Red sin hilo, Stratégie optimale, Optimal strategy, Estrategia optima, Traitement réparti, Distributed processing, Tratamiento repartido, Traitement signal, Signal processing, Procesamiento señal, Télécommunication sans fil, Wireless telecommunication, Telecomunicación sin hilo, Télédétection, Remote sensing, Teledetección, Distributed signal estimation, Node selection, Wireless sensor networks
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
ESAT-SCD (SISTA)/iMinds ― Future Health Department, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
Ghent University ― iMinds, Department of Information Technology (INTEC), Gaston Crommenlaan 8 Bus 201, 9050 Ghent, Belgium
ISSN:
0165-1684
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27907402
Rights:
Copyright 2014 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:
Telecommunications and information theory
Accession Number:
edscal.27907402
Database:
PASCAL Archive

Further Information

A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive node-specific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However, by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection.