论文标题
工业物联网的零接触网络：端到端的机器学习方法
Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning Approach
论文作者
论文摘要
预计以行业为4.0的智能工厂将实现制造商的下一场革命。尽管人工智能（AI）技术提高了生产率，但当前用例属于小规模和单任务操作。为了取消智能工厂的潜力，本文开发了用于智能制造的零接触网络系统，并以大规模的方式在培训和推断阶段中促进了分布的AI应用程序。首先为零接触平台引入了开放无线接入网络（O-RAN）体系结构，以启用该领域中全球控制的通信和计算基础架构能力。设计的无服务器框架允许智能有效的学习任务和资源分配。因此，可以将要求的学习任务分配给适当的机器人，并且基础基础架构可用于在没有专家知识的情况下支持学习任务。此外，由于提出的网络系统的灵活性，可以利用强大的AI-ENI-NETWORKENT算法来确保服务级别的协议和出色的工厂工作负载性能。最后，讨论了零接触智能工厂的三个开放研究方向，端到端的兼容性，端到端增强功能和网络安全。
Industry 4.0-enabled smart factory is expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong to small-scale and single-task operations. To unbound the potential of smart factory, this paper develops zero-touch network systems for intelligent manufacturing and facilitates distributed AI applications in both training and inferring stages in a large-scale manner. The open radio access network (O-RAN) architecture is first introduced for the zero-touch platform to enable globally controlling communications and computation infrastructure capability in the field. The designed serverless framework allows intelligent and efficient learning assignments and resource allocations. Hence, requested learning tasks can be assigned to appropriate robots, and the underlying infrastructure can be used to support the learning tasks without expert knowledge. Moreover, due to the proposed network system's flexibility, powerful AI-enabled networking algorithms can be utilized to ensure service-level agreements and superior performances for factory workloads. Finally, three open research directions of backward compatibility, end-to-end enhancements, and cybersecurity are discussed for zero-touch smart factory.
