A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems

Parto, Mahmoud; Saldana, Christopher; Kurfess, Thomas

Abstract Due to the recent movements in Industry 4.0 and Internet of Things (IoT), accessing or generating data in the Smart Manufacturing (SM) domain has become more attainable; communication protocols such as MTConnect and OPC-UA provide access to a majority of raw data generated from machine tools while retrofit sensor packs facilitate high-frequency data acquisitions from legacy and modern equipment. These technologies have led to the generation of quantities of raw data, known as Big Data (BD), that are complex to be analyzed. This study proposes a novel and event-driven IoT architecture that considers automated and scalable machine learning techniques with the focus sense-making from manufacturing process and systems performance in the Cyber-Physical Systems (CPS) domain. In this manuscript, a novel generalized three-layer IoT architecture utilizing Edge Computing (EC), Fog Computing (FC), Cloud Computing (CC), and Federated Learning (FL) is presented, where data are preprocessed in the Edge layer, Machine Learning (ML) models are incrementally trained in the Fog layer and the trained models are aggregated in centralized cloud models. The results show that the methods discussed in the proposed architecture will lead to the development of higher performance and more scalable IoT frameworks that require minimal storage and computing power.