Preventive Detection of Mosquito Populations using Embedded Machine Learning on Low Power IoT Platforms

Abstract

We can accurately detect mosquito species with 80% accuracy using frequency spectrum analysis of insect wing-beat patterns when mapped to low-power embedded/IoT hardware. We combine energy-efficient hardware acceleration optimizations with algorithmic tuning of signal processing and machine-learning routines to deliver a platform for insect classification. The use of low power accelerator blocks in cheap embedded boards such as the Raspberry Pi 3 and Intel Edison, along with performance tuning of the software implementations enable a competitive implementation of mosquito classification task on standard datasets. Our approach demonstrates a concrete application of embedding intelligence in edge devices for reducing system-level energy needs instead of simply uploading sensory data directly to the cloud for post-processing. For the mosquito classification task, we are able to deliver classification accuracies as high as 80% with Intel Edison processing times as low as 5 ms per set of 8K audio samples and an energy use of 5 mJ per sample (2 months of continuous non-stop use on an AA battery with 2000 mAh capacity or longer depending on insect activity). We envision a network of connected sensors and embedded/IoT platforms deployed in vulnerable such as construction sites, mines, areas of known mosquito activity, ponds, riverfronts, or other areas with standing water bodies. In our experiments, targeting a 20% packet loss rate, we observed the ad-hoc WiFi range for mesh networks using the Raspberry Pi 3 boards to be 14 m while the Photon board connecting to infrastructure WiFi router nodes can stretch this to 35 m.