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Go to Editorial ManagerKeeping IoT networks running reliably in harsh environments is still a tough problem. Sensors wear out, communication links are unreliable, and maintenance quickly becomes expensive. These issues make traditional monitoring approaches fragile and slow to react. This work presents a self-adaptive, AI-driven Digital Twin framework that continuously tracks the real state of an IoT network and flags failures before they actually happen. The system mirrors the physical network in real time by combining edge-level data preprocessing, physics-aware Digital Twin simulations, and well-chosen deep learning models for anomaly detection and remaining useful life estimation. To test the idea, we simulated a network of 50 IoT nodes operating under realistic harsh conditions, including thermal stress, high humidity, and signal interference. The results are hard to ignore. The proposed framework reached 91% prediction accuracy, detected problems 27 seconds earlier on average, and improved overall network reliability from 84% to 96% compared to standard threshold-based monitoring. The takeaway is straightforward: pairing AI analytics with Digital Twin technology enables proactive and resilient IoT operation in environments where conventional monitoring quickly falls apart. This work lays a practical foundation for deploying AI-enhanced Digital Twins in real-world, next-generation IoT systems, where reliability actually matters.
The rapid development of the Internet of Things (IoT) has drawn significant attention from both industry and academia, driven by the integration of cloud computing, big data analytics, machine learning, and cyber-physical systems in manufacturing. Programmable Logic Controllers (PLCs), long central to industrial control systems, have evolved from basic feedback control devices to advanced components capable of networking and data exchange through IoT technologies. The Industrial Internet of Things (IIoT) refers to intelligent automation systems that continuously monitor critical parameters and respond to changes in real time. The integration of IoT with PLCs is transforming industrial automation by enabling remote real-time monitoring, data-driven decision-making, and predictive maintenance through advanced analytics. IIoT technologies enhance manufacturing performance and offer strategic value across sectors. Understanding their impact involves examining current research, including technology assessments and application-based case studies. This study provides an overview of PLC systems evolving into IIoT frameworks, with a focus on implementing proportional-integral (PI) control using the Siemens S7-300. Designed for precise and consistent temperature regulation, this approach enhances process efficiency and product quality, making it highly suitable for industrial and manufacturing environments.