###### Abstract
In recent years, mobile crowd sensing (or simply crowd sensing) has appeared as an IoT solution that relies on smartphone sensors to implement large-scale detection services [5], which requires incremental data storage to save sensed data in big data repositories [6]. These types of solutions are inexpensive compared to wireless sensor and actuator networks (WSANs). The IoT enables the use of sensorization technologies to collect massive amounts of data from urban areas and offer community services to citizens, which makes it possible to offer intelligent city systems and services. The installation of different types of sensors in various environments and the emergence of new communication technologies, such as 4G/5G, allow the creation of systems with innovative functionalities to monitor and manage services such as electrical energy, water, household waste, public transport and traffic, the health of citizens, and public libraries. However, real-time analytics in connection with scalability and massive data transmission are research challenges [7]. The implementation of these distributed applications for a smart city requires high computing power provided by highly scalable hardware and software resources. In addition, distributed applications must concurrently provide interactive features for a wide variety of users and devices, provide batch and near-real-time processing and big data analytics and must be rapidly designed and developed. The principles of the Reactive Manifesto can be used to address these challenges and address the design and implementation of the system architecture [8]. The Reactive Manifesto describes a set of principles (responsive, resilient, elastic, and message driven) for designing reactive architectures and achieving computer systems that work in distributed, dynamic, and highly scalable environments. It is vital to take into account that specific e-health applications require handling critical scenarios such as in the case of emergency management and obtaining medical reports in near real time, especially in chronic diseases. Due to these considerations, there is a need to use streaming data through near-real-time applications; therefore, this article promotes the use of emerging fast data architectures. This type of architecture allows ingesting, and continuous processing, and provides components for the design of a big data subsystem [9]. An architectural base of this type makes it possible to create data analytics services for batch and near-real-time processing. Additionally, this paper also promotes cloud computing as an infrastructure as a service (IaaS) and encourages automation tools for deployments, such as Kubernetes (K8s) which helps to manage containers under the concept of \"container as a service\"(CaaS). K8s is a portable and extensible platform for managing workloads and services. K8s automatically orchestrates useful computation, network, and storage infrastructure in DevOps processes [10]. Thus, it is possible to create and manage flexible DevOps processes with short development cycles and rapid deployment of distributed applications (microservices) that have high computational requirements. The main idea behind the approach of this paper is to propose a set of methods, principles, and architectural decisions to enable the design and implementation of e-health reactive services that can be used for elderly people in smart city homes (Figure 1). The software components of the services were based on microservices, and some of the services incorporated crowd sensing as an IoT sensing solution to meet the pervasive system requirements. Moreover, a set of architectural decisions was proposed to have continuous integration of the services code in version repositories and continuous deployment of services in a public cloud. Furthermore, the use of a fast data architecture and the incorporation of a big data subsystem for supporting batch and near-real-time data analytics were considered. This article makes the following main contributions:  
1.