LAUSR

In this article, we present a multigas-sensor device whose structure is optimized for edge-computing capability under Internet of things’ (IoT) environments. Considering inherent sensor device characteristics susceptible to environmental factors, such as temperature and humidity, edge-computing capability for the on-site sensor calibration and pattern recognition (PR) is facilitated through a proposed analog-assisted continual learning scheme. An environment-adaptable continual learning (EACL) is proposed to combine multiple learning processes under different environments, including chamber and on-site. Its computational burden is much relieved to be integrated into the edge device by adopting the analog-assisted structure, where a designed readout integrated circuit (ROIC) for automatic calibration normalizes gas-sensor data. For functional feasibility, an edge-computing IoT device prototype is manufactured with a fabricated ROIC and an in-house semiconductor-type sensor array, supporting wireless on-site monitoring platform interfaces. The environment-adaptable edge-computing capability is functionally verified through EACL-PR experiments on hazardous gases, such as NO2 and CO, under environmental factor variations. The average PR accuracy of 97% is achieved on several kinds of mixture gas patterns. The analog-assisted operation is verified to reduce the training cycles by three times, while the EACL itself achieves 25% better efficiency.