Development Of Edge Runtime Learning Systems For An Artificial Nose Classifying Drinks

Smart adaptive systems are evolving rapidly. In addition to high-performance artificial intelligence from the cloud, smart edge devices are increasingly developing. They are capable of handling constantly more complex classification or even learning tasks independently. After answering the question of how this knowledge can be learned or used at the edge, it must also be determined how this knowledge can be exchanged with a cloud. We would like to investigate this exchange of knowledge between several Edge Runtime Learning Artificial Noses and a cloud intelligence in further experiments. For this purpose, we describe an improvement of our former approaches for an artificial nose. Especially, we implemented a resource-efficient edge nose with lazy learning algorithms on a microcontroller and enabled learning at runtime. This new nose achieves classification rates of up to 92 %, almost as good as the resource-intensive previous version, and forms the basis for research into the knowledge exchange between different edge and cloud AI devices and the processes involved.