Role of machine learning approach for industrial internet of things (IIoT) in cloud environment-a systematic review

Abstract

The industrial internet of things (IIoT) is recognized as the extended use of the internet of things (IoT) and integrated technologies in the manufacturing industry.It involves the implementation of innovative techniques such as machine learning (ML), robotics, big data, machine-to-machine (M2M), and industrial control systems (ICS) by companies in their working processes [1].IIoT also includes sensors and actuators that have calculating and interactive capacities.Such devices change the entire course of a gathering, exchanging, examining, and transforming data that facilitates the decision-making process.It innovates Industry 4.0 practices and contributes towards industrial development through improved productivity by industries such as power, mining, agriculture, healthcare, and transportation [2].While focusing on ML, it forms an integral part of the IIoT and contributes to Industry 4.0 growth by providing innovative computing and communication technologies.ML is based on a predictive analysis aspect that helps analyse the large volumes of data produced by IIoT effectively [3].