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| Title | Machine Learning Techniques For Industrial Internet Of Things |
|---|---|
| ID_Doc | 36024 |
| Authors | Sharma M.; Hazra A.; Tomar A. |
| Year | 2024 |
| Published | Learning Techniques for the Internet of Things |
| DOI | http://dx.doi.org/10.1007/978-3-031-50514-0_4 |
| Abstract | Industrial Internet of Things (IIoT), which connects millions of smart devices, will allow for industrial use cases like smart cities and supply chain management with minimal human involvement in the future. The IIoT has revolutionized production by making data faster, more accurate, and more accessible to stakeholders at all levels. In the IIoT, machine learning (ML) techniques are frequently utilized to add intelligence to the industrial environment and manufacturing operations. For instance, timely and accurate data analysis is essential, and ML techniques are used to examine and comprehend the enormous amounts of data created by IoT devices. Organizations use ML algorithms to promote innovation, make smart decisions, and create autonomous industrial environments. IoT and ML are employed in manufacturing to enhance quality control, streamline production, and cut waste. For instance, producers can spot areas for improvement and carry out preventative maintenance before equipment faults occur by applying ML algorithms to analyze data from IoT sensors on factory equipment. Learning techniques in IIoT are critical to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Motivated by the abovementioned learning technology, in this chapter, we discuss the significance of ML and its benefits toward IIoT for processing real-time applications. We shed light on several key ML technologies for IIoT. Finally, we highlight several research challenges and outstanding concerns that need further addressing to realize the IIoT scenario. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | Artificial intelligence; Computational offloading; Industrial Internet of Things; Machine learning |
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