Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

TL;DR

This paper reviews the potentials, challenges, and emerging applications of deep learning in the Industrial Internet of Things, highlighting key techniques, use cases, and future research directions to advance intelligent industrial systems.

Contribution

It provides a comprehensive overview of deep learning techniques and applications in IIoT, identifying research challenges and proposing future directions for the field.

Findings

Deep learning enables smart manufacturing and industrial automation.

Various DL techniques like CNNs and auto-encoders are applied in IIoT.

Research challenges include effective design and implementation of DL in IIoT.

Abstract

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.