IoT-Enabled Industrial Automation Framework for Real-Time Monitoring, Predictive Control, and Operational Optimization in Industry 4.0 Environments

Abstract

The collective movement of the Internet of Things (IoT), industrial automation, and advanced analytics is regulated by its importance in the progress of Industry 4.0. The current paper provides the concept, design and implementation of an IoT based industrial automation framework offering real time monitoring, predictive control and operational optimization to the manufacturing and process industries. The main goal is to increase the level of situational awareness, minimize unpredicted downtime and increase production effectiveness due to transparent combination of edge-cloud communication and smart analytics. The recommended system interconnects industrial heterogeneous and diverse sensors with programmable logic controllers (PLCs) and actuators through a communication layer that is based on MQTT to provide low-latency and secure communication of information. The data of real-time streams is processed at the edge with a split-second analytics pipeline that consists of both time-series anomaly detection and predictive maintenance models. The working prototype was installed on a CNC machining line upon which performance was compared to a conventional system controlled by a PLC. A 60 days trial period results showed a 23 percent saving in unplanned downtime, 15 percent increase in production throughput and 21 percent energy consumption. The mentioned improvements outline the efficacy of the predetermined control based on IoT in the optimization of operational performance. The results affirm that fusing industrial automation with the IoT effectively raises the responsiveness and adaptive capability and imparts actionable intelligence that incurs cost-effectiveness and sustainable use in manufacturing. It will have future extensions that integrate 5G connectivity, federated learning, and digital twin in multi-factory optimization and resilience in Industry 4.0 environments.