Guest Editors
Dr. Jehad Ali, Ajou University, South Korea.
Dr. Sahib Khan, University of Engineering and Technology, Pakistan.
Dr. Ikram Syed, National University of Science and Technology, Pakistan.
Summary
By using controller intelligence and programmability, software-defined networking (SDN) may dynamically and efficiently manage resources in order to supply a wide range of different services. SDN enables network systems to coordinate and estimate available resources, as well as dynamically adapt to the environment, to optimize resource consumption.
Artificial Intelligence and Machine learning are proving to be an effective method of increasing the intelligence of SDN controllers. Techniques such as machine learning and artificial intelligence (AI) are useful in network communication because they allow for rapid adaptability. It is possible to make efficient use of this increase in intelligence of the SDN controller in the context of the Software-Defined Internet-of-Vehicles (SD-IoV). The controller that has been trained with powerful AI and Machine Learning algorithms may improve the supply of End-to-End (E2E) services, security, and resource management in the SD-IoV environment.
This special issue look forward to provide the most up-to-date information on state-of-the-art SDN technologies that are based on machine learning and AI methods, as well as new research findings involving a broad variety of features within the intelligent SDN technology for SD-IoV.
Keywords
Potential topics include but are not limited to the following:
1. Load balancing in SD-IoV
2. Controller placement in SD-IoV
3. Ultra-reliable low latency-based SD-IoV
4. Artificial intelligence enabled SD-IoV
5. Security and reliability in SD-IoV
6. Leveraging NFV in SD-IoV
7. MEC based efficient solutions in SD-IOV
8. Machine learning based SD-IoV
9. Energy efficient SD-IoV based solutions
10. Massive machine type communication leveraging 5G in SD-IOV
11 Network slicing in SD-IoV leveraging 5G
12. Named data leveraging SD-IoV
13. DDoS attacks detection and prevention mechanisms in SD-IoV
Published Papers
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