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5G-Powered Remote Sensing for Real-Time Infrastructure Monitoring

Shinki Katyayani PandeyKalinga University,Department of Management,Raipur,IndiaMohsen FallahCollege of Technical Engineering, Islamic University of Najaf,Department of Computers Techniques Engineering,Najaf,IraqOtamirzaev Muzaffar Bakhodir UgliFaculty of Humanities & Pedagogy, Turan International University,NamanganP. K. ChidambaramNew Prince Shri Bhavani College of Engineering and Technology,Department of Mech,ChennaiS. KannimuthuKarpagam College of Engineering,Department of Information Technology,Coimbatore,641032Arun Sekar RSR University,Dept.of ECE,Warangal,Telangana,506371A. KarthikayenSaveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Department of Electronics and Communication Engineering,Chennai,Tamilandu,India,602105M. O PallaviSapthagiri NPS University,Department of Artificial Intelligence & Data Science,Bangalore,India
2025
ABI

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For urban infrastructure expansion, real-time, intelligent, and automated monitoring is needed for it to be able to ensure structural integrity and operational efficiency at the same time. This research envisages an inventive 5G friendly remote sensing technique that combines edgeAI, digital twins, and blockchain to empower and maintain that a kinetic infrastructure is being well attended to. The ultra-low latency 5G networks are used coupled with ultra high resolution sensor information to feed to an edge computing AI system for anomaly detection and predictive maintenance in real time. The digital twin model forms a virtual twin of the infrastructure assets to simulate structural degradation with AI-driven simulations and to optimize the maintenance schedule. Blockchain technology is employed to ensure data security and integrity and to create a tamper-proof distributed ledger for sensor data. To make the monitoring more accurate, the proposed system combines multiple sensors, i.e., LiDAR, thermal imaging, IoT sensors, and drone surveillance. The framework is tailored for critical infrastructure such as bridges, highways, railways, power grids, and smart buildings and enhances maintenance efficiency, decreases operational cost, and decreases failure risk. This shows through experimental results that conventional monitoring systems are not capable of achieving improved anomaly detection accuracy, lesser latency, and enhanced data security, as is the case with this new system. Through this, the research helps in developing the resilient, AI-powered, and secure infrastructure management solutions for making intelligent urban ecosystems and leveraging 5G connectivity for real-time, predictive, and autonomous infrastructure monitoring.

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