Special Issue "Artificial Intelligence Convergence Networks Leveraging Software-Defined Networking"

Submission Deadline: 01 September 2021 (closed)
Guest Editors
Dr. Jehad Ali, Ajou University, South Korea.
Dr. Khalil Khan, University of Azad Jammu and Kashmir, Pakistan.
Mr. Muhammad Adil, Virtual Univesity, Pakistan.

Summary

Software-Defined Networking (SDN) dynamically and efficiently manage resources to provide diverse services leveraging controller intelligence and programmability. The centralized control in SDN has a global view of the underlying network that enables the network systems to orchestrate and estimate the available resources and dynamically adapt to the environment for maximizing resource utilization. Considering these pros, the SDN is regarded as an exquisite choice for future generation Networks. 
The introduction of 5G technology brought unprecedented growth in the ubiquitous traffic generation. The Internet of Things, the Internet of Vehicles (IoV), and Vehicle to Everything (V2X) creates a huge volume of data, resulting in the scalability of the networks as well as frequent dynamic changes Consequently, the optimal configuration policy for the underlying network changes accordingly. Hence, the manual configuration of the controller is a daunting task owing to these changes. Deep learning (DL) is becoming a successful way to boost SDN controller intelligence as a promising machine learning solution. Machine learning and Artificial intelligence (AI) techniques provide effectiveness for adaptation in network communication. The controller trained with AI and Machine learning sophisticated algorithms can enhance the provision of resource allocation, End-to-End (E2E) Services, and Security. 
This Special Issue looks forward to novel technologies using Artificial Intelligence and machine learning techniques, covering new research findings with a broad range of elements leveraging the intelligent SDN technology for Artificial Intelligence Convergence Networks. The potential topics are not limited to methodologies and challenges of AI-enabled SDN in the emerging fields. We welcome original high-quality research and review articles.


Keywords
Potential topics include but are not limited to the following:
• Leveraging Artificial Intelligence and Software-defined Networks for 5G resource management
• Load balancing in energy-constrained networks using Machine learning with Software-defined networks
• E2E delay reduction leveraging AI and Machine learning based Software-defined networks
• Machine Learning and Artificial Intelligence based solutions for boosting the availability of the controllers
• Artificial Intelligence and Machine learning for Controller placement problem in Software-defined
• Machine learning/AI-based Software-defined networks
• Utilizing Artificial Intelligence for enhancing Privacy and Security for Software-defined networks
• Machine learning and Artificial Intelligence for Effective Fault management in Software-defined networks
• Artificial Intelligence and Machine learning-based SDN solutions for mission-critical applications
• Artificial Intelligence and Machine Learning based Software-defined networks for efficient edge computing, Resource management, network slicing, and Fog computing
• Other related issues

Published Papers
  • Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm
  • Abstract Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for… More
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