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Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

2023Journal article Open access
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dc.contributor.authorBrito, Thadeu
dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorMendes, João
dc.contributor.authorZorawski, Matheus
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorPereira, Ana I.
dc.contributor.authorRufino, José
dc.contributor.authorLima, José
dc.contributor.authorCosta, Paulo Gomes da
dc.date.accessioned2023-02-24T09:39:38Z
dc.date.available2023-02-24T09:39:38Z
dc.date.issued2023
dc.description.abstractDeveloping 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.pt_PT
dc.description.sponsorshipThis 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/07427/2021pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBrito, 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. Sensorspt_PT
dc.identifier.doi10.3390/s23031282pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27165
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectData transmission optimizationpt_PT
dc.subjectWireless sensor networkpt_PT
dc.subjectWildfirept_PT
dc.subjectLoRaWANpt_PT
dc.subjectInternet of Thingspt_PT
dc.subjectDigital filterpt_PT
dc.titleData acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring systempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.issue3pt_PT
oaire.citation.startPage1282pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume23pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBrito
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person.familyNameRufino
person.familyNameLima
person.givenNameThadeu
person.givenNameBeatriz Flamia
person.givenNameJoão
person.givenNameMatheus
person.givenNameFlorbela P.
person.givenNameAna I.
person.givenNameJosé
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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