A deep learning-driven strategy to minimise outage for industrial IoT networks
The Industrial Internet of Things (IIoT) refers to an interconnected network of devices, in an industrial setting, used to improve the efficiency of processes. This network of devices is the cornerstone to transmit data within and outside of the network, characterised by ultra-low latency and outage...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175310 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The Industrial Internet of Things (IIoT) refers to an interconnected network of devices, in an industrial setting, used to improve the efficiency of processes. This network of devices is the cornerstone to transmit data within and outside of the network, characterised by ultra-low latency and outage, to maximise the performance of IIOT devices with minimal downtime. In this study, we leverage on the Multi Access -Edge Computing (MEC) capabilities in Sixth Generation Wireless (6G) and build a Deep Learning Model to optimise static and dynamic parameters at wireless transmitting base stations. Compared to traditional methods, this neural network model is capable of achieving near optimal values with the benefit of a negligible compute time, providing a framework for future works for Deep Learning in wireless communication. |
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