An IoT Measurement System Based on LoRaWAN for Additive Manufacturing.

Industrial IoT IoT measurement systems LoRa battery lifetime smart monitoring smart sensors

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
22 Jul 2022
Historique:
received: 21 06 2022
revised: 14 07 2022
accepted: 18 07 2022
entrez: 28 7 2022
pubmed: 29 7 2022
medline: 30 7 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 35897970
pii: s22155466
doi: 10.3390/s22155466
pmc: PMC9331730
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2021 Jan 26;21(3):
pubmed: 33530518
Sensors (Basel). 2016 Sep 09;16(9):
pubmed: 27618064
Sensors (Basel). 2021 Mar 10;21(6):
pubmed: 33801852
Sensors (Basel). 2020 Aug 25;20(17):
pubmed: 32854350
Sensors (Basel). 2019 Nov 05;19(21):
pubmed: 31694254
Sensors (Basel). 2019 Sep 02;19(17):
pubmed: 31480709

Auteurs

Tommaso Fedullo (T)

Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy.
Department of Management and Engineering, University of Padova, S. S. Nicola 3, 36100 Vicenza, Italy.

Alberto Morato (A)

National Research Council of Italy, CNR-IEIIT, Via Gradenigo 6/B, 35131 Padova, Italy.

Giovanni Peserico (G)

Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35100 Padova, Italy.
Autec s.r.l., Via dei Pomari 65, 36030 Caldogno, Italy.

Luca Trevisan (L)

Consorzio RFX, Corso Stati Uniti 4, 35127 Padova, Italy.

Federico Tramarin (F)

Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy.

Stefano Vitturi (S)

National Research Council of Italy, CNR-IEIIT, Via Gradenigo 6/B, 35131 Padova, Italy.

Luigi Rovati (L)

Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy.

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Classifications MeSH