An Efficient Machine Learning Software Architecture for Internet of Things

Internet of Things (IoT) software is becoming a critical infrastructure for many domains. In IoT, sensors monitor their environment and transfer readings to cloud, where Machine Learning (ML) provides insights to decision-makers. In the healthcare domain, the IoT software designers have to consider privacy, real-time performance and cost in addition to ML accuracy. We propose an architecture that decomposes the ML lifecycle into components for deployment on a two-tier cloud, edge-core. It enables IoT time-series data to be consumed by ML models on edge-core infrastructure, with pipeline elements deployed on any tier, dynamically. The architecture feasibility and ML accuracy are validated with three brain-computer interfaces (BCI) based use-cases. The contributions are two-fold: first, we propose a novel ML-IoT pipeline software architecture that encompasses essential components from data ingestion to runtime use of ML models; second, we assess the software on cognitive applications and achieve promising results in comparison to literature.