Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/27165 |
Resumo: | Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears. |
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Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring systemData transmission optimizationWireless sensor networkWildfireLoRaWANInternet of ThingsDigital filterDeveloping innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021MDPIBiblioteca Digital do IPBBrito, ThadeuAzevedo, Beatriz FlamiaMendes, JoãoZorawski, MatheusFernandes, Florbela P.Pereira, Ana I.Rufino, JoséLima, JoséCosta, Paulo Gomes da2023-02-24T09:39:38Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/27165engBrito, Thadeu; Azevedo, Beatriz; Flamia Mendes, João; Zorawski, Matheus; Fernandes, Florbela P.; Pereira, Ana I. Rufino, José; Lima, José; Costa, Paulo (2023). Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system. Sensors10.3390/s23031282info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-06T01:21:35Zoai:bibliotecadigital.ipb.pt:10198/27165Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:17:31.697089Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
title |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
spellingShingle |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system Brito, Thadeu Data transmission optimization Wireless sensor network Wildfire LoRaWAN Internet of Things Digital filter |
title_short |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
title_full |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
title_fullStr |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
title_full_unstemmed |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
title_sort |
Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system |
author |
Brito, Thadeu |
author_facet |
Brito, Thadeu Azevedo, Beatriz Flamia Mendes, João Zorawski, Matheus Fernandes, Florbela P. Pereira, Ana I. Rufino, José Lima, José Costa, Paulo Gomes da |
author_role |
author |
author2 |
Azevedo, Beatriz Flamia Mendes, João Zorawski, Matheus Fernandes, Florbela P. Pereira, Ana I. Rufino, José Lima, José Costa, Paulo Gomes da |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Brito, Thadeu Azevedo, Beatriz Flamia Mendes, João Zorawski, Matheus Fernandes, Florbela P. Pereira, Ana I. Rufino, José Lima, José Costa, Paulo Gomes da |
dc.subject.por.fl_str_mv |
Data transmission optimization Wireless sensor network Wildfire LoRaWAN Internet of Things Digital filter |
topic |
Data transmission optimization Wireless sensor network Wildfire LoRaWAN Internet of Things Digital filter |
description |
Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-24T09:39:38Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/27165 |
url |
http://hdl.handle.net/10198/27165 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Brito, Thadeu; Azevedo, Beatriz; Flamia Mendes, João; Zorawski, Matheus; Fernandes, Florbela P.; Pereira, Ana I. Rufino, José; Lima, José; Costa, Paulo (2023). Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system. Sensors 10.3390/s23031282 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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