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- W3126796721 abstract "Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with 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." @default.
- W3126796721 created "2021-02-15" @default.
- W3126796721 creator A5000165797 @default.
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- W3126796721 creator A5066119580 @default.
- W3126796721 creator A5079244922 @default.
- W3126796721 creator A5083193286 @default.
- W3126796721 date "2021-07-15" @default.
- W3126796721 modified "2023-10-18" @default.
- W3126796721 title "Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications" @default.
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