Khan, MW, Salman, N, Kemp, AH et al. (1 more author) (2016) Localisation of sensor nodes with hybrid measurements in wireless sensor networks. Sensors, 16 (7). 1143. ISSN 1424-8220
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 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/). |
Keywords: | hybrid localisation; received signal strength; angle of arrival; generalised pattern search |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Sep 2016 14:00 |
Last Modified: | 23 Jun 2023 22:13 |
Published Version: | http://doi.org/10.3390/s16071143 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/s16071143 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104565 |