کنفرانس مهندسی برق ایران

صفحه اصلی / سی و چهارمین کنفرانس بین المللی مهندسی برق

AI-Driven Network Management System: Integrated Monitoring and Management for Network and Surveillance Racks

نویسندگان :

Parsa Khosravi¹ Sina Sayardoost Tabrizi²

1- University of Tehran 2- University of Tehran

کلمات کلیدی :

Network Monitoring،Rack Management،Anomaly Detection،Predictive Maintenance،Artificial Intelligence،Smart Surveillance Systems،Edge Computing،IoT

چکیده :

Reliable supervision of network and surveillance racks increasingly depends on the ability to observe environmental, energy, hardware, and software conditions as a coherent whole. Existing solutions typically separate these layers, which limits their ability to detect emerging faults or understand how issues propagate across subsystems. This study presents an integrated monitoring and management platform that brings these domains together within a single, AI-enabled architecture. The system operates autonomously during power or network interruptions and uses combined sensor data with machine-learning models to identify abnormal patterns, anticipate failures, and support faster intervention. Experimental evaluation—supported by a deployment on real rack equipment—shows that the proposed design improves fault-detection speed by roughly 40%, increases average equipment lifetime by 20–30%, and reduces operational losses by up to 42% and overall energy consumption by 16–28% compared with conventional tools. These results demonstrate the value of cross-domain data fusion combined with lightweight predictive analytics for enhancing the resilience and efficiency of modern rack infrastructures.