2020/08/15 by Ruhul Amin Khalil, Nasir Saeed, Khalil, Ruhul Amin +7 · 1 citation
Engineering · #Digital Transformation in Industry #FOS: Electrical engineering #Industrial Vision Systems and Defect Detection #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.06701
openalex publication_date 2020/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The recent advancements in the Internet of Things (IoT) are giving rise to\nthe proliferation of interconnected devices, enabling various smart\napplications. These enormous number of IoT devices generates a large capacity\nof data that further require intelligent data analysis and processing methods,\nsuch as Deep Learning (DL). Notably, the DL algorithms, when applied in the\nIndustrial Internet of Things (IIoT), can enable various applications such as\nsmart assembling, smart manufacturing, efficient networking, and accident\ndetection-and-prevention. Therefore, motivated by these numerous applications;\nin this paper, we present the key potentials of DL in IIoT. First, we review\nvarious DL techniques, including convolutional neural networks, auto-encoders,\nand recurrent neural networks and there use in different industries. Then, we\noutline numerous use cases of DL for IIoT systems, including smart\nmanufacturing, smart metering, smart agriculture, etc. Moreover, we categorize\nseveral research challenges regarding the effective design and appropriate\nimplementation of DL-IIoT. Finally, we present several future research\ndirections to inspire and motivate further research in this area.\n