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An IoT Measurement System Based on LoRaWAN for Additive Manufacturing
The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331730/ https://www.ncbi.nlm.nih.gov/pubmed/35897970 http://dx.doi.org/10.3390/s22155466 |
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author | Fedullo, Tommaso Morato, Alberto Peserico, Giovanni Trevisan, Luca Tramarin, Federico Vitturi, Stefano Rovati, Luigi |
author_facet | Fedullo, Tommaso Morato, Alberto Peserico, Giovanni Trevisan, Luca Tramarin, Federico Vitturi, Stefano Rovati, Luigi |
author_sort | Fedullo, Tommaso |
collection | PubMed |
description | The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible. |
format | Online Article Text |
id | pubmed-9331730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93317302022-07-29 An IoT Measurement System Based on LoRaWAN for Additive Manufacturing Fedullo, Tommaso Morato, Alberto Peserico, Giovanni Trevisan, Luca Tramarin, Federico Vitturi, Stefano Rovati, Luigi Sensors (Basel) Article The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible. MDPI 2022-07-22 /pmc/articles/PMC9331730/ /pubmed/35897970 http://dx.doi.org/10.3390/s22155466 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 Fedullo, Tommaso Morato, Alberto Peserico, Giovanni Trevisan, Luca Tramarin, Federico Vitturi, Stefano Rovati, Luigi An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title | An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title_full | An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title_fullStr | An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title_full_unstemmed | An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title_short | An IoT Measurement System Based on LoRaWAN for Additive Manufacturing |
title_sort | iot measurement system based on lorawan for additive manufacturing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331730/ https://www.ncbi.nlm.nih.gov/pubmed/35897970 http://dx.doi.org/10.3390/s22155466 |
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