Abstract:
Yearly, more than 200 million malaria cases are recorded worldwide.
Most of these cases are witnessed in less developed countries as
the environments are not well-maintained, which forms breeding
places for mosquitoes. Female mosquito-anopheles is responsible
for malaria infection, dengue, chikungunya, and zika. Developing
countries struggle to fight diseases; malaria still claims more than
400,000 lives annually. One current way to keep away anopheles
mosquitoes is using commercially available electric liquid mosquito
repellents, which can adversely affect the human body when used
for extended periods. Furthermore, energy and sprays are wasted
as they constantly work even without the presence of anopheles
mosquitoes. We propose a low-cost IoT-based TinyML model that
intelligently discharges the mosquito repellent when an anopheles
mosquito is in the room. First, we prove the concept by exploring
two lightweight deep learners with a 1D Convolution Neural Net work (1D-CNN) and 2D Convolution Neural Network (2D-CNN) to
classify raw sounds from mosquito wingbeats. We adopted a Leaky
ReLU in building the 1D-CNN to speed up training and improve
classification performance. Furthermore, we adopted batch normal ization to avoid degradation and vanishing gradient problems. We
implemented the experiments in an Edge impulse platform. Each
of the CNN models recorded stable classification performance dur ing the proof of concept study, while the 1D-CNN took less time
and computing resources in training, validation, and testing. As
we aimed to propose a low-cost solution, we evaluated the perfor mance of the 1D-CNN-based prototype in the actual deployment
by playing mosquito wingbeat sounds on a laptop which we placed
next to it in intervals of 0.5, 1.0, 1.5, 2.0, 2.5, and 3 meters. The model
showed promising results across distances and thus could be used
to chase away mosquitoes in a room of small to medium size.