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• W3197568502 abstract "Internet of things (IoT) and 5G network are fundamental building blocks for industrial IoT (IIoT). IoT has enabled real-time monitoring and actuation in industrial floors and machinery, aimed at improving the efficiency and safety of industrial activities and processes. On the other hand, 5G networks will provide ultra-reliable and low-latency communication for the wireless integration of autonomous industrial machinery, mobile vehicles, and robots, and management systems, aimed at the real-time control and management of industrial machinery toward smart factories. In IIoT, machine learning (ML) will also play a fundamental role in handling complex tasks at industrial machinery and 5G networks management, configuration, and control. However, ML suffers from the cold-start problem and needs a large amount of highly accurate data samples for model training, which is costly and difficult to obtain in IIoT applications. In this article, we shed light on the design of transfer learning (TL)-based systems for IIoT. We discuss how TL can overcome the demand for high-quality large data samples required to training ML models in IIoT. We also highlight the work principles and daunting challenges faced during the TL systems for IIoT. Furthermore, we categorize the TL systems for IIoT into TL for IIoT machinery level and for IIoT networking level and provide an in-depth discussion of the design building blocks and challenges of TL systems in each proposed class. Finally, we point out some future research directions for the design of novel TL-based systems for envisioned 5G-enabled IIoT applications." @default.
• W3197568502 created "2021-09-13" @default.
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• W3197568502 date "2022-06-01" @default.
• W3197568502 modified "2023-09-24" @default.
• W3197568502 title "Transfer Learning for Disruptive 5G-Enabled Industrial Internet of Things" @default.
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• W3197568502 doi "https://doi.org/10.1109/tii.2021.3107781" @default.
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