Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for out...

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Autores principales:	Winfred Ingabire, Hadi Larijani, Ryan M. Gibson, Ayyaz-UI-Haq Qureshi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	IoT LoRaWAN RSSI localization RNN Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/39ee5805c9ce45449e0584a50a26808a
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spelling	oai:doaj.org-article:39ee5805c9ce45449e0584a50a26808a2021-11-25T16:12:59ZOutdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks10.3390/a141103071999-4893https://doaj.org/article/39ee5805c9ce45449e0584a50a26808a2021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/307https://doaj.org/toc/1999-4893Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.Winfred IngabireHadi LarijaniRyan M. GibsonAyyaz-UI-Haq QureshiMDPI AGarticleIoTLoRaWANRSSIlocalizationRNNIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 307, p 307 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IoT LoRaWAN RSSI localization RNN Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	IoT LoRaWAN RSSI localization RNN Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Winfred Ingabire Hadi Larijani Ryan M. Gibson Ayyaz-UI-Haq Qureshi Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
description	Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.
format	article
author	Winfred Ingabire Hadi Larijani Ryan M. Gibson Ayyaz-UI-Haq Qureshi
author_facet	Winfred Ingabire Hadi Larijani Ryan M. Gibson Ayyaz-UI-Haq Qureshi
author_sort	Winfred Ingabire
title	Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
title_short	Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
title_full	Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
title_fullStr	Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
title_full_unstemmed	Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
title_sort	outdoor node localization using random neural networks for large-scale urban iot lora networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/39ee5805c9ce45449e0584a50a26808a
work_keys_str_mv	AT winfredingabire outdoornodelocalizationusingrandomneuralnetworksforlargescaleurbaniotloranetworks AT hadilarijani outdoornodelocalizationusingrandomneuralnetworksforlargescaleurbaniotloranetworks AT ryanmgibson outdoornodelocalizationusingrandomneuralnetworksforlargescaleurbaniotloranetworks AT ayyazuihaqqureshi outdoornodelocalizationusingrandomneuralnetworksforlargescaleurbaniotloranetworks
_version_	1718413249576173568

Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

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