## 2 Related Works  
In recent years, research in the field of healthcare systems has provided independent solutions that present a variety of issues. Particularly, these different healthcare system initiatives present little or no interoperability, scalability, and extensibility. Our work is motivated by some previous research and complements it in many ways. A healthcare monitoring system based on an architecture focused on grouping and providing interoperability between healthcare sensing devices was proposed in [12]. Data on health conditions are reported through a web application and mobile application. However, the architecture lacks the flexibility to adapt and scale to the demands of the number of users of a smart city. A framework for the design of a smart health monitoring system was presented in [13]. The main objective of this framework was to enable ubiquitous monitoring of various population groups, including elderly people. The framework includes basic components such as real-time data extraction, wireless communication controllers, and wireless communication controllers. However, the framework is not validated by any prototype, and strategies for managing scalability or a big data subsystem are not proposed. A specific solution to monitor the heart rate for hypertensive patients combining the benefits of some technologies such as ZigBee, Wi-Fi, and a web application was proposed in [14]. Although the system had some reactive features through the use of messaging subsystems, the high scalability and availability of the system were not taken into consideration. A system that captures data from wearable sensors at home and sends them to a cloud-based web server was proposed in [15]. The applications were based on REST web services. However, considerations for real-time monitoring in a smart city were not considered. Additionally, the architecture did not cover the aspects related to the management of a large amount of data. A system for continuous monitoring of patient respiration was developed in [16]. An Arduino controller transmitted the data to a web server that stored the data in a MySQL database, and a web page was used to view the data from a monolithic web server. However, the system did not incorporate reactive capabilities, such as messaging systems, component replication, or the use of microservices as opposed to the use of monolithic web servers. A fusion between the IoT and cloud computing was used to implement a patient health monitoring system in [17]. This framework provided continuous on-demand monitoring. Although mechanisms were proposed for the analytics of the data collected, the architecture did not incorporate specific solutions for the treatment of big data and real-time analytics. The architecture of the software applications was not explicitly discussed. The authors in [18] proposed a specific model for the creation of sense electronic health records. This solution used Bluetooth to connect to sensors, and a smartphone sent the data to the EHR server using a RESTful API. However, the coupling between smartphones and the EHR server affects the scalability of the system. A smart health system assisted by cloud computing and big data was presented in [19]. The system included a data collection layer with a unified standard, a data management layer for distributed storage, and a data-oriented service layer. On the other hand, it presented a weak reuse, and the system did not guarantee the integrity and interoperability of the data in the environment of its operation. The authors in [20] proposed a prototype design of a health monitoring system for patients in healthcare. Some vital signs were retrieved by low-cost hardware, transferred to the cloud computing environment, and processed with big data technologies. In the prototype, the mechanisms and products used to transfer the data from the sensors to the cloud computing environment were not fully explained, especially the interaction between the messaging brokers.