پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک) پژوهشنامه پردازش و مدیریت اطلاعات 2251-8223 40 ویژه نامه انگلیسی 4 2025 07 23 AI-Driven Drones for Real-Time Network Performance Monitoring AI-Driven Drones for Real-Time Network Performance Monitoring 1281 1308 728427 10.22034/jipm.2025.728427 FA Dina Fallah Al-Turath University, Baghdad 10013, Iraq 0009-0003-1922-0652 Bushra Jabbar Abdul-Kareem Al-Mansour University College, Baghdad 10067, Iraq 0000-0003-2928-4716 Kamalov Sultanbek Sadyrbekovich Osh State University, Osh City 723500, Kyrgyzstan 0009-0002-9944-9782 Ammar Falih Mahdi Al-Rafidain University College Baghdad 10064, Iraq 0000-0003-0209-3477 Ola Janan Madenat Alelem University College, Baghdad 10006, Iraq 0009-0000-2608-5012 Journal Article 2025 08 17 <strong>ABSTRACT</strong><br /><strong>Background:</strong> The growing complexity of telecommunications networks, fueled by advancements like the Internet of Things (IoT) and 5G, necessitates dynamic and real-time network performance monitoring. Traditional static systems often fail to address challenges related to scalability, adaptability, and response speed in high-demand environments. Integrating artificial intelligence (AI) with unmanned aerial vehicles (UAVs) presents a transformative approach to overcoming these limitations.<br /><strong>Objective:</strong> This study aims to evaluate the effectiveness of AI-driven drones for real-time network performance monitoring, focusing on key metrics such as latency, signal strength, throughput, and anomaly detection.<br /><strong>Methods:</strong> A comprehensive framework was developed, employing reinforcement learning (RL) for path planning and a hybrid temporal-spectral anomaly detection (HTS-AD) algorithm. Experimental validation was conducted using 10 UAVs across simulated and real-world environments, collecting over 3.2 million data points. Statistical analyses, including MANOVA and Bayesian regression, were used to evaluate performance.<br /><strong>Results:</strong> The proposed system demonstrated significant improvements over traditional methods, including a 24.6% increase in anomaly detection accuracy, a 30% reduction in energy consumption, and 99.9% network coverage in high-density UAV deployments.<br /><strong>Conclusion:</strong> AI-driven drones offer a scalable, efficient, and reliable solution for network monitoring. By addressing limitations of traditional systems, this study establishes a foundation for next-generation telecommunications infrastructure. Future research should focus on real-world deployment and hybrid security models. <strong>ABSTRACT</strong><br /><strong>Background:</strong> The growing complexity of telecommunications networks, fueled by advancements like the Internet of Things (IoT) and 5G, necessitates dynamic and real-time network performance monitoring. Traditional static systems often fail to address challenges related to scalability, adaptability, and response speed in high-demand environments. Integrating artificial intelligence (AI) with unmanned aerial vehicles (UAVs) presents a transformative approach to overcoming these limitations.<br /><strong>Objective:</strong> This study aims to evaluate the effectiveness of AI-driven drones for real-time network performance monitoring, focusing on key metrics such as latency, signal strength, throughput, and anomaly detection.<br /><strong>Methods:</strong> A comprehensive framework was developed, employing reinforcement learning (RL) for path planning and a hybrid temporal-spectral anomaly detection (HTS-AD) algorithm. Experimental validation was conducted using 10 UAVs across simulated and real-world environments, collecting over 3.2 million data points. Statistical analyses, including MANOVA and Bayesian regression, were used to evaluate performance.<br /><strong>Results:</strong> The proposed system demonstrated significant improvements over traditional methods, including a 24.6% increase in anomaly detection accuracy, a 30% reduction in energy consumption, and 99.9% network coverage in high-density UAV deployments.<br /><strong>Conclusion:</strong> AI-driven drones offer a scalable, efficient, and reliable solution for network monitoring. By addressing limitations of traditional systems, this study establishes a foundation for next-generation telecommunications infrastructure. Future research should focus on real-world deployment and hybrid security models. https://jipm.irandoc.ac.ir/article_728427_262253ea1c5f04527c26f073301a5913.pdf