# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### System Architecture  
The main goal of this work is to introduce a secure machine learning monitoring system for IoT healthcare data in the cloud, which integrates the benefits delivered by a  
Figure 1: The primary goals of the proposed system. machine learning scenario and the immediacy of the data produced by the IoMT with the computation power offered from a cloud computing server, operated through a wireless network. This system could produce, manage, and control IoMT data produced by air condition-quality sensors and thermal-IR cameras, in addition to various mobile devices. The XMPP (Extensible Messaging and Presence Protocol) has been used to communicate the data generated by various devices, such as sensors and cameras. This protocol has undergone extensive use and testing and is an open standard. It is based on an efficient model, the publish/subscribe model presented in Figure 2[23]. In this paradigm, data is sent from a publisher (an IoT device) to a broker device in a safe and effective manner. Any subscriber device that requests a certain \"topic\" is then forwarded the updated data by the broker device. As an illustration, the temperature sensor will publish temperature information to the broker under the \"temperature\" subject. Those requesting the temperature topic will have access to these data. The requirements of storage and cloud systems are met by the XMPP protocol. Additionally, a framework that provided an abstraction of the network devices was employed to create the system and facilitate communication across all devices, servers, and databases. The interface between the IoT devices [XMPP] and the API (Application Programming Interface), which is hosted in the user's device, is made simpler by this framework. The system architecture for our suggested model implementation is depicted in Figure 3. Moreover, the operation of the proposed system in Figure 3 is analyzed below. Each surveillance infrastructure is under the direction of the cloud server. An installed infrastructure that is not directly connected to a cloud server may also provide data to it. To export the appropriate conclusions for each monitoring region, the cloud server managed and organized all the data that the system had acquired. Additionally, the cloud offers a safe environment for gathering, managing, and processing the data generated by the IoMT. The peripheral equipment of the system is connected either to an autonomous network and through it to the internet and from there to the proposed system or is directly connected to the network of the proposed system. Thermal-IR cameras measure the ambient temperature and, more precisely, the temperature of people arriving or moving through the surveillance area. Each camera sends the pictures it has taken to a cloud server over a wireless network. A server in the cloud runs a data analysis algorithm that determines whether the temperature reading of each camera is greater than 37 Celsius. Then, to stop the spread and isolate that person in another,  
Figure 2: Publish/subscribe communication model. separated space, a notification signal appears in the monitoring system of that area if a temperature over that particular threshold is observed. The area's environment has a high level of air pollution, according to air quality sensors. The cloud server received the detection data via the wireless network. The data analysis algorithm established on the cloud server inspects if the rate of air pollution in the area exceeds normal levels. If it exceeds, then a notification signal appears in the monitoring system of that area, and then the local surveillance and control personnel will be notified. The entire proposed system is based on the algorithm proposed below, and it involves all existing encryption/decryption models to create a new model that will provide direct data transmission and management in a secure cloud environment.