# MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)

arXiv: https://arxiv.org/abs/2401.16258 · HTML: https://mlsanity.com/paper/2401.16258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [TinyML for Ubiquitous Edge AI](https://mlsanity.com/paper/2102.01255/) (2021)
- [Flying Insect Classification with Inexpensive Sensors](https://mlsanity.com/paper/1403.2654/) (2014)
- [Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs](https://mlsanity.com/paper/2008.08639/) (2020)

## Abstract (arXiv)

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.