MosquIoT: A System Based on IoT and Machine Learning for the Monitoring
  of Aedes aegypti (Diptera: Culicidae)
Javier Aira Teresa Olivares Montes Francisco M. Delicado Darìo Vezzani
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 paper
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.