## 7 Conclusions and Future Work  
Currently, the growth in the number of elderly individuals is a concern of their families and health centers, which will increase in the coming years. One solution to address these needs is the use of e-health systems to provide healthcare services that monitor the activities of elderly people in their homes. However, currently, e-health solutions for healthcare are built with different technologies and without a common or explicit approach for the development of health services. Additionally, these solutions often have scalability, interoperability, and extensibility issues. The uses of ICTs can help meet the requirements for the care of elderly people by building system architectures that combine technologies to enable the implementation of health applications. By exploiting the opportunities of a widely connected world, it is feasible to build pervasive, fast, and reliable services, which also contribute to the establishment of smart cities. This paper presents an approach to build cloud-based IoT reactive services in the area of e-health for elderly care at home. In summary, the approach used the software architecture process by [PERSON] and [PERSON] to design certain architectural views and obtain the architecture description. Some architectural patterns were applied to design a system architecture whose components, deployed in a public cloud, met the requirements related to the IoT subsystem and all software services around the care of elderly people. To address some of the issues, we took into consideration providing reactive features to the system: responsive, resilient, elastic, and message driven. Furthermore, the design has the characteristics of an emerging fast data architecture with a big data subsystem to meet the needs related to the IoT subsystem and the data analytics subsystem. In addition, the software service development process followed the DDD as a foundation for the structuring of microservices. Finally, the system components were deployed on a public cloud with a CaaS model, and the deployment of microservices was made more flexible through DevOps practices with a CI/CD pipeline to obtain a dynamic workflow. EMQ and Confluent are elements of great importance to configure an emerging fast data architecture that provides a data ingestion channel and allows the interconnectivity of some other system components. EMQ and Confluent can have high availability and can scale to achieve a system capable of ingesting, processing, and serving data in near real time. The big data subsystem was built with MongoDB and Apache Spark, where MongoDB helps store the big data and guarantees dynamic operations to users, and Apache Sparkpermits near-real-time analysis. For example, an alert manager, as a Spark application, collects data from Confluent and manages emergencies with a predefined data analysis logic applied in near real time. CaaS is a cloud service model that was used to manage containers through Kubernetes, which helped to leave behind concerns related to operating system configuration, considering only tasks such as container deployments or scalability. In this way, complex on-premise infrastructures, which are difficult to scale, can be taken to a public cloud to scale and increase their availability. From the developer's point of view, a unified agile development environment is offered to build IoT services in the e-health area. The architecture is extensible since the use of Apache Kafka, as a distributed streaming platform complemented with Kafka Connect, helps to quickly build new data streams using existing connectors for other types of common data sources and sinks. Moreover, dynamic workflows were established that reduced the deployment time of services in the cloud computing infrastructure through the inclusion of DevOps practices with CI/CD pipelines. Thus, new subapplications implemented as microservices can be deployed flexibly to extend system services.