AI-Driven Drones for Real-Time Network Performance Monitoring

Fallah, Dina; Abdul-Kareem, Bushra Jabbar; Sultanbek Sadyrbekovich, Kamalov; Falih Mahdi, Ammar; Janan, Ola

doi:10.22034/jipm.2025.728427

AI-Driven Drones for Real-Time Network Performance Monitoring

Document Type : Original Article

Authors

Dina Fallah ¹

Bushra Jabbar Abdul-Kareem ²

Kamalov Sultanbek Sadyrbekovich ³

Ammar Falih Mahdi ⁴

Ola Janan ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728427

Abstract

ABSTRACT
Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.

Keywords

KEYWORDS: AI-driven drones

network performance monitoring

UAV

real-time assessment

machine learning

telecommunications

latency

throughput

signal strength

remote monitoring

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