Reducing Energy Consumption and Health Hazards of Electric Liquid Mosquito Repellents through TinyML

Abstract

Two problems arise when using commercially available electric liquid mosquito repellents. First, prallethrine, the main component of the liquid repellent, can have an adverse effect on the human body with extended exposure. Second, electricity is wasted when no mosquitoes are present. To solve these problems, a TinyML-oriented mosquito sound classification model is developed and integrated with a commercial electric liquid repellent device. Based on a convolutional neural network (CNN), the classification model can control the prallethrine vaporizer to turn on only when there are mosquitoes. As a consequence, the repellent user can avoid inhaling unnecessarily large amounts of the chemical, with the added benefit of dramatically reduced energy consumption by the repellent device.

Keywords

• TinyML
• electric liquid mosquito repellent
• prallethrine
• convolutional neural network (CNN)
• embedded AI
• health