Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †

Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †

Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for l...

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Main Authors: Khan, Muhammad W., Salman, Naveed, Kemp, Andrew H., Mihaylova, Lyudmila
Format: Online
Language:English
Published: MDPI 2016
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970185/
id pubmed-4970185
recordtype oai_dc
spelling pubmed-49701852016-08-04 Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks † Khan, Muhammad W. Salman, Naveed Kemp, Andrew H. Mihaylova, Lyudmila Letter Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution. MDPI 2016-07-22 /pmc/articles/PMC4970185/ /pubmed/27455268 http://dx.doi.org/10.3390/s16071143 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Khan, Muhammad W.
Salman, Naveed
Kemp, Andrew H.
Mihaylova, Lyudmila
spellingShingle Khan, Muhammad W.
Salman, Naveed
Kemp, Andrew H.
Mihaylova, Lyudmila
Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
author_facet Khan, Muhammad W.
Salman, Naveed
Kemp, Andrew H.
Mihaylova, Lyudmila
author_sort Khan, Muhammad W.
title Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
title_short Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
title_full Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
title_fullStr Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
title_full_unstemmed Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks †
title_sort localisation of sensor nodes with hybrid measurements in wireless sensor networks †
description Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution.
publisher MDPI
publishDate 2016
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970185/
_version_ 1613619668048674816

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