MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)
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 av...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universidad de Castilla-La Mancha |
| Repositório: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/33025 |
| Acesso em linha: | https://doi.org/10.1109/TIM.2023.3265119 https://hdl.handle.net/10578/33025 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Aedes aegypti Entomological surveillance Internet of Things (IoT) Low-power wide-area network (LPWAN) Machine learning Ovitraps Smart cities Tiny machine learning (TinyML) |
| Resumo: | 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. |
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