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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.
Harsh industrial environments such as oilfields present unique challenges to electronic systems, including extreme temperatures, limited connectivity, power constraints, and operational unpredictability. Traditional Internet of Things (IoT) deployments often fail to adapt in real-time, exposing systems to risks such as data loss, late anomaly detection, or critical failure. This paper proposes a lightweight, Artificial Intelligence (AI)-driven eSystem architecture tailored for such conditions, integrating edge intelligence, secure communication, and self-adaptive mechanisms. We demonstrate the framework's viability through simulating a case study of real-time sensor data from pipeline infrastructure, applying a Long Short-Term Memory (LSTM)-based anomaly detection model deployed at the edge. Results show significant improvements in detection latency, bandwidth efficiency, and system resilience. The framework offers a modular blueprint for deploying AI-enhanced eSystems across energy, mining, and remote critical infrastructure domains.