Litoiu, MarinChaudhary, Mahima2021-07-062021-07-062021-042021-07-06http://hdl.handle.net/10315/38477Internet 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.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.NeurosciencesElectronic Thesis or Dissertation2021-07-06machine learningbrain-computer interfacewearable devicesinternet of thingssoftware architecture