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dc.contributor.author
Aira, Javier  
dc.contributor.author
Olivares, Teresa  
dc.contributor.author
Delicado, Francisco M.  
dc.contributor.author
Vezzani, Dario  
dc.date.available
2024-01-30T10:50:37Z  
dc.date.issued
2023-04  
dc.identifier.citation
Aira, Javier; Olivares, Teresa; Delicado, Francisco M.; Vezzani, Dario; MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae); Institute of Electrical and Electronics Engineers; Ieee Transactions on Instrumentation and Measurement; 72; 4-2023; 1-13  
dc.identifier.issn
0018-9456  
dc.identifier.uri
http://hdl.handle.net/11336/225134  
dc.description.abstract
Millions of people around the world are infected with mosquito-borne diseases each year. One of the most dangerous species is Aedes aegypti, the main vector of viruses such as dengue, yellow fever, chikungunya, and Zika, among others. Mosquito prevention and eradication campaigns are essential to avoid major public health consequences. In this respect, entomological surveillance is an important tool. At present, this traditional monitoring tool is executed manually and requires digital transformation to help authorities make better decisions, improve their planning efforts, speed up execution, and better manage available resources. Therefore, new technological tools based on proven techniques need to be designed and developed. However, such tools should also be cost-effective, autonomous, reliable, and easy to implement, and should be enabled by connectivity and multi-platform software applications. This article presents the design, development, and testing of an innovative system named 'MosquIoT. ' It is based on traditional ovitraps with embedded Internet of Things (IoT) and tiny machine learning (TinyML) technologies, which enable the detection and quantification of Ae. aegypti eggs. This innovative and promising solution may help dynamically understand the behavior of Ae. aegypti populations in cities, shifting from the current reactive entomological monitoring model to a proactive and predictive digital one.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Electrical and Electronics Engineers  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
AEDES AEGYPTI  
dc.subject
ENTOMOLOGICAL SURVEILLANCE  
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INTERNET OF THINGS (IOT)  
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LOW-POWER WIDE-AREA NETWORK (LPWAN)  
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MACHINE LEARNING  
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OVITRAPS  
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SMART CITIES  
dc.subject
TINY MACHINE LEARNING (TINYML)  
dc.subject.classification
Zoología, Ornitología, Entomología, Etología  
dc.subject.classification
Ciencias Biológicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2024-01-25T14:09:31Z  
dc.journal.volume
72  
dc.journal.pagination
1-13  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
New York  
dc.description.fil
Fil: Aira, Javier. Universidad de Castilla-La Mancha; España  
dc.description.fil
Fil: Olivares, Teresa. Universidad de Castilla-La Mancha; España  
dc.description.fil
Fil: Delicado, Francisco M.. Universidad de Castilla-La Mancha; España  
dc.description.fil
Fil: Vezzani, Dario. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto Multidisciplinario de Ecosistemas y Desarrollo Sustentable; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina  
dc.journal.title
Ieee Transactions on Instrumentation and Measurement  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/10093891/  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TIM.2023.3265119