工业物联网中的深度学习：潜力、挑战和新兴应用
Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications
摘要：
物联网（IoT）的最新进展正在引起互连设备的激增，从而实现各种智能应用。这些数量庞大的物联网设备产生了大量的数据，进一步需要智能数据分析和处理方法，如深度学习（DL）。值得注意的是，DL算法在应用于工业物联网（IIoT）时，可以实现各种应用，如智能装配，智能制造，高效联网和事故检测和预防。因此，受这些众多应用的激励，在本文中，我们提出了DL在IIoT中的关键潜力。首先，我们回顾了各种DL技术，包括卷积神经网络，自动编码器和递归神经网络，以及它们在不同行业中的应用。然后，我们概述了工业物联网系统的DL的众多用例，包括智能制造，智能计量，智能农业等，此外，我们还对DL-IIoT的有效设计和适当实施方面的几个研究挑战进行了分类。最后，我们提出了几个未来的研究方向，以启发和激励在这一领域的进一步研究。
英文摘要：
The recent advancements in the Internet of Things (IoT) are giving rise to the proliferation of interconnected devices, enabling various smart applications. These enormous number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, such as Deep Learning (DL). Notably, the DL algorithms, when applied in the Industrial Internet of Things (IIoT), can enable various applications such as smart assembling, smart manufacturing, efficient networking, and accident detection-and-prevention. Therefore, motivated by these numerous applications; in this paper, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, auto-encoders, and recurrent neural networks and there use in different industries. Then, we outline numerous use cases of DL for IIoT systems, including smart manufacturing, smart metering, smart agriculture, etc. Moreover, we categorize several research challenges regarding the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
