Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of safety, security, and low-latency communication in an environment of critical real-time operations. Under these circumstances, discriminative deep learning (DL) algorithms are unsuitable due to their need for large amounts of labeled and balanced training data, uncertainty of inputs, etc. To overcome these issues, researchers have started using deep generative models (DGMs), which combine the flexibility of DL with the inference power of probabilistic modeling. In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection, network traffic prediction, and platform monitoring. Following an analysis of existing IIoT DGM implementations, we identify challenges (i.e., weak discriminative capability, insufficient interpretability, lack of generalization ability, generated data vulnerability, privacy concern, and data complexity) that need to be investigated in order to accelerate the adoption of DGMs in IIoT and also propose some potential research directions.

中文翻译：

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工业物联网中的深度生成模型：调查


通信技术和人工智能的进步正在加速工业物联网 (IIoT) 的发展。随着工业物联网实现传感器和控制器与网络的持续集成，对生成的大数据进行智能分析是一项关键要求。尽管工业物联网被认为是物联网的一个子集，但它在关键实时操作环境中的更高级别的安全性、保密性和低延迟通信方面有其自身的特点。在这种情况下，判别式深度学习（DL）算法由于需要大量标记和平衡的训练数据、输入的不确定性等而不再适用。为了克服这些问题，研究人员开始使用深度生成模型（DGM），将深度学习的灵活性与概率建模的推理能力相结合。在本文中，我们回顾了 DGM 的最新技术及其在工业物联网中的适用性，将所审查的工作分为异常检测、信任边界保护、网络流量预测和平台监控等工业物联网应用领域。在对现有 IIoT DGM 实施进行分析之后，我们确定了需要调查的挑战（即判别能力弱、可解释性不足、缺乏泛化能力、生成的数据漏洞、隐私问题和数据复杂性），以加速采用IIoT 中的 DGM 还提出了一些潜在的研究方向。