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Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area

The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an...

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Autores principales: Cruz, Hugo A. O., Ferreira, Sidnir C. B., Araújo, Jasmine P. L., Barros, Fabrício J. B., Farias, Fabrício S., Neto, Miércio C. A., Tostes, Maria E. L., Nascimento, Andréia A., Cavalcante, Gervásio P. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460370/
https://www.ncbi.nlm.nih.gov/pubmed/36080959
http://dx.doi.org/10.3390/s22176492
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author Cruz, Hugo A. O.
Ferreira, Sidnir C. B.
Araújo, Jasmine P. L.
Barros, Fabrício J. B.
Farias, Fabrício S.
Neto, Miércio C. A.
Tostes, Maria E. L.
Nascimento, Andréia A.
Cavalcante, Gervásio P. S.
author_facet Cruz, Hugo A. O.
Ferreira, Sidnir C. B.
Araújo, Jasmine P. L.
Barros, Fabrício J. B.
Farias, Fabrício S.
Neto, Miércio C. A.
Tostes, Maria E. L.
Nascimento, Andréia A.
Cavalcante, Gervásio P. S.
author_sort Cruz, Hugo A. O.
collection PubMed
description The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with −108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area.
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spelling pubmed-94603702022-09-10 Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area Cruz, Hugo A. O. Ferreira, Sidnir C. B. Araújo, Jasmine P. L. Barros, Fabrício J. B. Farias, Fabrício S. Neto, Miércio C. A. Tostes, Maria E. L. Nascimento, Andréia A. Cavalcante, Gervásio P. S. Sensors (Basel) Article The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with −108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area. MDPI 2022-08-29 /pmc/articles/PMC9460370/ /pubmed/36080959 http://dx.doi.org/10.3390/s22176492 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cruz, Hugo A. O.
Ferreira, Sidnir C. B.
Araújo, Jasmine P. L.
Barros, Fabrício J. B.
Farias, Fabrício S.
Neto, Miércio C. A.
Tostes, Maria E. L.
Nascimento, Andréia A.
Cavalcante, Gervásio P. S.
Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title_full Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title_fullStr Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title_full_unstemmed Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title_short Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
title_sort methodology for lora gateway placement based on bio-inspired algorithmsfor a smart campus in wooded area
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460370/
https://www.ncbi.nlm.nih.gov/pubmed/36080959
http://dx.doi.org/10.3390/s22176492
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