Artificial Intelligence and Machine Learning for Networking and Communications

  in Special Issue   Posted on June 6, 2018

Information for the Special Issue

Submission Deadline: Sun 15 Jul 2018
Journal Impact Factor : 8.085
Journal Name : IEEE Journal on Selected Areas in Communications
Journal Publisher:
Website for the Special Issue: https://www.comsoc.org/jsac/cfp/artificial-intelligence-and-machine-learning-networking-and-communications
Journal & Submission Website: http://www.comsoc.org/jsac

Special Issue Call for Papers:

Artificial Intelligence (AI) and Machine Learning (ML) approaches, well known from IT disciplines, are beginning to emerge in the networking domain. These approaches can be clustered into AI/ML techniques for network management; network design for AI/ML applications and system aspects. AI/ML techniques for network management, operations & automation address the design and application of AI/ML techniques to improve the way we address networking today. Recently, networking has become the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and more recently, edge cloud and fog. This development towards enhanced design opportunities along with increased complexity in networking as well as in networked applications has fueled the need for improved network automation in agile infrastructures. This new networking environment calls for even more automation, as exemplified by recent initiatives to set-up network automation platforms. This can be combined with Artificial Intelligence techniques to execute efficient, rapid, trustworthy management operations. Network design and optimization for AI/ML applications addresses a complementing topic namely the support of AI/ML-based systems through novel networking techniques including new architectures as well as performance models. A third topic area is system implementation and open-source software development

This evolution has drawn particular attention to inter-disciplinary approaches from communication networks and the AI/ML research community. On the one hand, researchers in communication networks are tapping into machine learning and AI techniques to optimize network architecture, control and management, leading to more automation in network operations. On the other hand, researchers in the AI community are working with networking researchers to optimize network architecture and design.

In this special issue, we invite submissions of high-quality original technical and survey papers, which have not been published previously, on artificial intelligence and machine learning techniques and their applications for computer and communications networks, including the following non-exhaustive list of topics, falling into four sub-categories:

Theoretic approaches and methodologies
Usable theory of networks inspired by machine learning
Transfer learning and reinforcement learning for networking and communications
Big data analytics frameworks for networking data
Network analytics
Machine learning, data mining and big data analytics in networking
Data mining, statistical modeling, and machine learning for network management
Network problem diagnosis through machine learning
Network applications
Network architecture and optimization for AI/ML applications at scale
Machine learning for multimedia networking
Resource allocation for shared/virtualized networks using machine learning
Protocol design and optimization using machine learning
AI/ML for wireless network resource management and medium access control
Energy-efficient network operations via AI/ML algorithms
Reliability, robustness and safety for networks optimized and operated based on AI techniques
Security concepts for networks optimized and operated based on AI/ML concepts
AI/ML Algorithms for network security
Network automation
Deep learning and reinforcement learning in network control & management
Proactive network monitoring architecture
Self-learning and adaptive networking protocols and algorithms
Predictive and self-aware networking maintenance
Open-source AI algorithms and software for networking
Open-source networking optimization software for AI/ML applications

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