Machine Learning Frameworks for Industrial Internet of Things (IIoT): A Comprehensive Analysis

Abstract

Many industrial processes have been transformed by ict infrastructure. Artificial intelligence and machine learning algorithms have been needed by companies of all sizes, whether minor or major, to handle the terabytes of data created by sensing, actuation, production control systems, and web services. These data are large (terabytes) and diverse (picture, audio, video, graphics), necessitating the use of specific models and approaches for analysis and administration. Intelligent technologies create enormous quantities of data in massive industrial networks, hence IIoT frameworks demand clever, resilient big data analysis tools. Because of their cognitive acquisition and processing capabilities, artificial intelligence (AI) and machine learning (ML) techniques generate impressive outcomes in IIoT networks. The possibility of machine learning in IIoT applications is assessed in this research study, which also gives a comprehensive topology of IIoT and major enabling technologies. The theoretical underpinnings of numerous well-known ML algorithms, as well as several software and hardware frameworks for ML implementations, are then presented. The use of machine learning techniques in IIoT applications is briefly covered. Finally, this study identifies key problems as well as potential research directions.