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

arXiv: https://arxiv.org/abs/2008.06701 · HTML: https://mlsanity.com/paper/2008.06701/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 19 of 19)

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- [Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review](https://mlsanity.com/paper/2312.16174/) (2023)
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- [Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things](https://mlsanity.com/paper/2401.12914/) (2024)
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- [Rethinking industrial artificial intelligence: a unified foundation framework](https://mlsanity.com/paper/2504.01797/) (2025)
- [An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving](https://mlsanity.com/paper/2109.02629/) (2021)
- [SmartSplit: Latency-Energy-Memory Optimisation for CNN Splitting on Smartphone Environment](https://mlsanity.com/paper/2111.01077/) (2021)
- [RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks](https://mlsanity.com/paper/2301.10822/) (2023)
- [Edge Deep Learning Model Protection via Neuron Authorization](https://mlsanity.com/paper/2303.12397/) (2023)
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## Abstract (arXiv)

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.