Machine Learning Techniques for Industrial Internet of Things: A Survey

Industrial Internet of Things (IIoT), which connects millions of smart devices, will allow for industrial use cases like smart cities and supply chain management with minimal human involvement in the future. The IIoT has revolutionised production by making data faster, more accurate, and more accessible to stakeholders at all levels. In the IIoT, machine learning (ML) techniques are frequently utilised to add intelligence to the industrial environment and manufacturing operations. For instance, timely and accurate data analysis is essential, and ML techniques are used to examine and comprehend the enormous amounts of data created by IoT devices. Organisations use ML algorithms to promote innovation, make smart decisions, and create autonomous industrial environments. IoT and ML are employed in manufacturing to enhance quality control, streamline production, and cut waste. For instance, producers can spot areas for improvement and carry out preventative maintenance before equipment faults occur by applying ML algorithms to analyse data from IoT sensors on factory equipment. Learning techniques in IIoT are critical to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Motivated by the above-mentioned learning technology, in this chapter, we discuss the significance of ML and its benefits towards IIoT for processing real-time applications. We shed light on several key ML technologies for IIoT. Finally, we highlight several research challenges and outstanding concerns that need further addressing to realise the IIoT scenario.