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Erschienen in:
01.10.2023
Empowering an IoT platform with advance quantum computing and a Customized deep residual technique
Erschienen in: Optical and Quantum Electronics | Ausgabe 10/2023
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Abstract
All over the world, millions of devices are wirelessly connected and exchanging data as part of the Internet of Things (IoT). As more and more information is hoped to be monitored by means of a single platform, the importance of accuracy assessment in the pursuit of the perfect IoT platform has grown. To keep up with the ever-increasing data analysis needs for crucial, real-time decision making, IoT data collection is becoming increasingly crucial. In this study, we utilized the "IoT Sensor Data" dataset, consisting of sensor readings collected from various IoT devices. The proposed R-QCNN model, which combines a quantum neural network with a deep residual learning technique, was trained and evaluated on this dataset. Experimental results show that the R-QCNN achieved an accuracy of 95% in classifying the IoT sensor data, outperforming existing methods. Thus, our approach demonstrates promising results in optimizing the cost function routine for IoT platforms.