Call for Papers: Machine Learning-Enabled Zero Touch Networks
IEEE Communications Magazine · 2022
BackgroundWith 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.