Call for Papers

With the continued growth of IoT devices and their deployment, manually managing and connecting them is impractical and presents multiple challenges. To that end, Zero Touch Networks that rely on software-based modules instead of dedicated propriety hardware become a viable potential solution. The overall aim of zero-touch networks is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them. Thus, Zero Touch Networks are able to monitor networks and services and act on faults with minimal (if any) human intervention, including in the early detection of emerging problems, autonomous learning, autonomous remediation, decision making, and support of various optimization objectives. As a result, Zero Touch Networks are able to offer self-serving, self-fulfilling, and self-assuring operations.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies have continued to develop and evolve in recent years in parallel with the advancement of ICT technologies. AI/ML/DL are viewed as foundational pillars for Zero Touch Networks. This is because they allow systems to be more autonomous and efficient. Moreover, they simultaneously help reduce human intervention. Having systems that are automated, intelligent, flexible, scalable, easily configurable, dynamic, secure, and privacy-preserving is “extremely” desired.

Given that Zero Touch Networks are considered to be in their infancy at this stage, it is expected that they will develop from merely detecting anomalies to being able to fully heal without the need for human intervention. To achieve this, AI/ML/DL technologies will be vital as part of the transformation from traditional to automated and intelligent network and service management. As a result, by integrating these technologies, efficient network management that is automated and considered to be intelligent can be achieved.

This Feature Topic (FT) aims to bring together researchers, industry practitioners, and individuals working on the related areas of Zero-Touch Networks. Prospective authors are invited to submit articles on topics including, but not limited to:

• AI/ML/DL for Zero Touch Networks
• Big Data Analytics for Zero Touch Networks
• Federated Learning for Zero Touch Networks
• Reinforcement learning for Zero Touch Networks
• Data-driven Service Monitoring for Zero Touch Networks
• ML-enabled Channel Estimation and Network Behavior Analysis of Zero Touch networks
• ML-aided Traffic Prediction and Classification in Zero Touch Networks
• ML-aided congestion control in Zero Touch Networks
• ML-supported Dynamic Resource Allocation Techniques for Zero Touch Networks
• ML-aided Network Slicing & Orchestration of Zero Touch Networks
• ML-based Energy Efficient Zero Touch Networks
• ML-based intrusion detection and security frameworks for Zero Touch Networks

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Manuscript Submission Guidelines. Please, check these guidelines carefully before submitting since submissions not complying with them will be administratively rejected without review.

All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select the “FT-2214/Machine Learning-Enabled Zero Touch Networks” topic from the drop-down menu of Topic/Series titles. Please observe the dates specified here below noting that there will be no extension of submission deadline.

Important Dates

Manuscript Submission Deadline: 15 August 2022
Decision Notification: 15 November 2022
Final Manuscript Due: 1 December 2022
Tentative Publication Date: February 2023

Guest Editors

Abdallah Shami (Lead Editor)
The University of Western Ontario, Ontario, Canada

Lyndon Ong
Ciena, CTO Office, California, USA