Improved trilateration for indoor localization: Neural network and centroid-based approach

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localizatio...

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Autores principales:	Satish R Jondhale, Amruta S Jondhale, Pallavi S Deshpande, Jaime Lloret
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/683c0a5d10d14611aa9bb2c72681c80a
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spelling	oai:doaj.org-article:683c0a5d10d14611aa9bb2c72681c80a2021-11-15T00:33:26ZImproved trilateration for indoor localization: Neural network and centroid-based approach1550-147710.1177/15501477211053997https://doaj.org/article/683c0a5d10d14611aa9bb2c72681c80a2021-11-01T00:00:00Zhttps://doi.org/10.1177/15501477211053997https://doaj.org/toc/1550-1477Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.Satish R JondhaleAmruta S JondhalePallavi S DeshpandeJaime LloretSAGE PublishingarticleElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Distributed Sensor Networks, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Satish R Jondhale Amruta S Jondhale Pallavi S Deshpande Jaime Lloret Improved trilateration for indoor localization: Neural network and centroid-based approach
description	Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.
format	article
author	Satish R Jondhale Amruta S Jondhale Pallavi S Deshpande Jaime Lloret
author_facet	Satish R Jondhale Amruta S Jondhale Pallavi S Deshpande Jaime Lloret
author_sort	Satish R Jondhale
title	Improved trilateration for indoor localization: Neural network and centroid-based approach
title_short	Improved trilateration for indoor localization: Neural network and centroid-based approach
title_full	Improved trilateration for indoor localization: Neural network and centroid-based approach
title_fullStr	Improved trilateration for indoor localization: Neural network and centroid-based approach
title_full_unstemmed	Improved trilateration for indoor localization: Neural network and centroid-based approach
title_sort	improved trilateration for indoor localization: neural network and centroid-based approach
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/683c0a5d10d14611aa9bb2c72681c80a
work_keys_str_mv	AT satishrjondhale improvedtrilaterationforindoorlocalizationneuralnetworkandcentroidbasedapproach AT amrutasjondhale improvedtrilaterationforindoorlocalizationneuralnetworkandcentroidbasedapproach AT pallavisdeshpande improvedtrilaterationforindoorlocalizationneuralnetworkandcentroidbasedapproach AT jaimelloret improvedtrilaterationforindoorlocalizationneuralnetworkandcentroidbasedapproach
_version_	1718428989064740864

Improved trilateration for indoor localization: Neural network and centroid-based approach

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