Sensor cloud virtualisation systems for improving performance of IoT-based WSN

Abstract

A cloud is a new paradigm for IoT-based WSN that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD), a novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. IoT, cloud computing and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platforms and is capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real-data set from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.