Subject: [nmrg] Call for Papers - Special Issue of IEEE JSAC on Artificial Intelligence and Machine Learning for Networking and Communications

From: <[email-masked]>
Date: Wed, 16 May 2018 10:54:11 +0200

IEEE JSAC Special issue

Impact Factor: 8.08

Call for Papers on Artificial Intelligence and Machine Learning for
Networking and Communications

<https://www.comsoc.org/jsac/cfp/artificial-intelligence-and-machine-learnin
g-networking-and-communications>
https://www.comsoc.org/jsac/cfp/artificial-intelligence-and-machine-learning
-networking-and-communications 

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 challenges 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

Submission Guidelines

To submit a paper, please go to https://edas.info/N24637

All submissions have to be prepared according to the Guide for Authors as
published in the Journal website at
http://www.comsoc.org/jsac/paper-submission-guidelines.

Important Dates

*	Manuscript Due: July 15, 2018
*	Acceptance notification: October 19, 2018
*	Revised Paper Acceptance notification: December 7, 2018
*	Final manuscript due: January 1, 2019
*	Expected Publication of the Special Issue: First quarter 2019

Guest Editors

Prosper Chemouil, Orange Labs, France, [email-masked]
<mailto:[email-masked]>  

Pan Hui, University of Helsinki, Finland & Hong Kong University of Science
and Technology, Hong Kong, [email-masked] <mailto:[email-masked]>  

Wolfgang Kellerer, Technical University of Munich, Germany,
[email-masked] <mailto:[email-masked]>  

Yong Li, Tsinghua University, China, [email-masked]
<mailto:[email-masked]> 

Rolf Stadler, KTH Royal Institute of Technology, Sweden, [email-masked]
<mailto:[email-masked]>  

Dacheng Tao, The University of Sydney, Australia, [email-masked]
<mailto:[email-masked]>  

Yonggang Wen, Nanyang Technological University, Singapore, [email-masked]
<mailto:[email-masked]>  

Ying Zhang, Facebook, USA, [email-masked] <mailto:[email-masked]> 

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