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Machine Learning-Enabled Zero Touch Networks
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DOI：10.1109/MCOM.2023.10047848.png)
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Abstract
En 中文
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 (ZTNs), which rely on software-based modules instead of dedicated proprietary hardware, become a viable potential solution. The overall aim of ZTNs is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them. Thus, ZTNs are able to monitor networks and services and act on faults with minimal (if any) human intervention, including the early detection of emerging problems, autonomous learning, autonomous remediation, decision making, and support of various optimization objectives. As a result, ZTNs are able to offer self-serving, self-fulfilling, and self-assuring operations.
Keywords:
Special issues and sections
Machine learning
Internet of Things
Decision making
Optimization
Autonomous networks
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