  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.
