Submission Deadline: 01 July 2025 View: 810 Submit to Special Issue
Prof. Dr. Jehad Ali
Email: jehadali@ajou.ac.kr
Affiliation: Department of AI Convergence Network, Ajou University, South Korea
Research Interests: SDN, AI, ML, 5G, 6G, IoT
Prof. Khalil Khan
Email: Khalil.khan@nu.edu.kz
Affiliation: Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Kazakhstan
Research Interests: AI, ML, Deep Learning, Networks, Image processing
Artificial Intelligence (AI) and Machine Learning (ML) are powerful technologies driving innovations in intelligent programmable networks. These networks are essential for adapting to the increasing demand for high-quality services, particularly in modern, complex network environments such as 5G and beyond. AI and ML can process vast amounts of data in real time, allowing intelligent programmable networks to optimize traffic flows dynamically. ML algorithms can predict traffic patterns by analyzing historical data. This enables proactive network adjustments to avoid congestion and ensure smooth data transmission, which is critical for maintaining high levels of QoS. AI-based systems can dynamically allocate network resources, such as bandwidth, computing power, or storage, based on current demand. This ensures that high-priority traffic receives the necessary resources, improving overall QoS. AI enables networks to self-optimize by continuously learning from network behavior and making adjustments without human intervention. AI models can monitor network performance and automatically fine-tune parameters such as latency, jitter, and throughput, ensuring that service quality remains optimal, even under fluctuating network conditions. AI can assist in network slicing, where virtual networks are dynamically created to cater to specific services. Each slice can be optimized for QoS and tailored to the unique requirements of applications like IoT, AR/VR, or autonomous systems.
This special issue invites high-quality research articles leveraging AI and ML in Intelligent programmable networks for management and improvement of QoS. The topics of interest include but are not limited to:
• Traffic Prediction in Intelligent Networks leveraging AI and ML
• QoS Improvement leveraging AI and ML in Intelligent Networks
• Modelling the Intelligent Networks using AI, ML and programmable networks
• Dynamic Resource Allocation with AI and ML
• Automated QoS Adjustments leveraging AI and ML
• End to end QoS Management utilizing AI and ML
• Fault detection and prediction leveraging AI and ML
• Performance evaluation of Intelligent networks with AI, ML
• Resource Efficiency leveraging AI and ML
• Leveraging AI and ML in AR/VR
• Massive IoT management leveraging AI and ML
• Placement of hardware and software services with AI and ML
• Intrusions detection and prevention leveraging AI and ML
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