Artificial Intelligence Driven Software-Defined Networking Technologies for Next Generation Networks
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: 31 March 2025 | Viewed by 512
Special Issue Editors
Interests: wireless networks; game theory; artificial intelligence; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: predictive maintenance; IoT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence-driven software-defined networking (AI-SDN) technologies are transforming next-generation networks by greatly enhancing their agility, efficiency and intelligence. The integration of AI with SDN allows for dynamic network management, real-time optimization and predictive analytics, creating more responsive and resilient network infrastructures. AI algorithms can process vast amounts of network traffic data, foresee potential issues and automatically adjust network configurations to maintain optimal performance. This integration of AI and SDN not only simplifies network operations, but also meets the increasing demands of emerging applications such as IoT, 5G and edge computing, ensuring scalable and robust connectivity for future digital ecosystems. In the future, AI-SDN will play a crucial role in the development and deployment of 6G networks, which promise even higher speeds, lower latency and more reliable connections. The intelligent orchestration of resources through AI will be essential in managing the complex, high-bandwidth applications anticipated in 6G, such as immersive communications, immersive augmented reality and advanced autonomous systems, establishing AI-SDN as the backbone of next-generation connectivity.
Dr. Evangelos Spyrou
Dr. Vassilios Kappatos
Guest Editors
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Keywords
- 5G AI-SDN metrhods and optimization
- use of AI-SDN in 6G communications and applications
- AI-SDN for edge computing and mobile networks
- AI-SDN IoT solutions
- delay, throughput and reliability in communications
- generative AI for SDN
- diffusion models for a time series in SDN
- AI methods for the stadarisation and xerticipation of SDN methods
- AI-SDN applications and developments
- AI-SDN test cases and potential testbeds
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