Enhancing Remote Oversight of Plants with a Compact IIoT System

It is impossible to overestimate the importance of remotely monitoring industrial facilities in the context of the Industrial Internet of Things. The goal of this line of research is to improve remote monitoring by using real-time data analytics, predictive maintenance, and energy optimization with a tiny Industrial Internet of Things (IIoT) system. The three most important algorithms are an energy optimization method using a proportional-integral-derivative (PID) controller, a predictive maintenance strategy based on machine learning models, and a real-time data streaming and analysis tool. The proposed method is said to be able to use these algorithms in real time, which enables it to schedule preventive maintenance, identify anomalies, and use energy effectively. Effective, sustainable, and data-driven plant management support should be expected to result in minimal downtime, lower operating costs, and ecologically conscious operations.