Abstract
Many researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.
Highlights
Cloud computing is one of the most valuable and promising research paths [1]
This research presents the simulation of a remote patient monitoring system based on Internet of things (IoT) and cloud computing in two static and dynamic environments to increase the effectiveness of health services and improve the healthcare system using MATLAB simulator
The Long-Short Term Memory (LSTM) Deep Neural Network is based on trained input data and produces a model based on hidden layers and neural structure
Summary
Cloud computing is one of the most valuable and promising research paths [1]. This computing method provides infrastructure and software services for users as well as the services requested by users through the Internet. This research presents the simulation of a remote patient monitoring system based on IoT and cloud computing in two static (smart homes) and dynamic (mobile health) environments to increase the effectiveness of health services (in any place and any time) and improve the healthcare system using MATLAB simulator. The LSTM Deep Neural Network is based on trained input data and produces a model based on hidden layers and neural structure It receives information about patients ’status for classification and evaluation and uses the generated model to classify and monitor patients’ health status remotely. We provide the advantages and disadvantages of each method. The information read from the brains of these patients is continuously
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