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The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

The microservice architecture (MSA) is a widely used and researched approach in industry and academia. However, designing the migration to MSA is a complex and challenging task, and there is a lack of clear guidelines on how to address both business and technical issues during the process. This paper presents a step-by-step method for determining the boundaries of microservices, which is a critical activity in MSA migration for both practitioners and academics.We conducted a case study of SHAMIM, a nationwide project serving over 120,000 students, to demonstrate the proposed method. The microservice boundaries were determined by utilizing both business and technical input types, including domain-driven design (DDD), business processes, service call numbers, and data access patterns in databases.Our findings suggest that MSA migration can be conducted more reliably by using change documents maintained by the DevOps team. The proposed method leads to clear improvements in the determination of each microservice boundary, and it can be useful for practitioners and academics involved in MSA migration.

Microservices are compact, independent services that work together with other microservices to support a single application function. Organizations may quickly deliver high-quality applications using the effective design pattern of the application function. Microservices allow for the alteration of one service in an application without affecting the other services. Containers and serverless functions, two cloud-native technologies, are frequently used to create microservices applications. A distributed, multi-component program has a number of advantages, but it also introduces new security risks that are not present in more conventional monolithic applications. The objective is to propose a method for access control that ensures the enhanced security of microservices. The proposed method was experimentally tested and validated in comparison to the centralized and decentralized architectures of the microservices. The obtained results showed that the proposed method enhanced the security of decentralized microservices by distributing the access control responsibility across multiple microservices within the external authentication and internal authorization processes. This allows for easy management of permissions between microservices and can help prevent unauthorized access to sensitive data and resources, as well as reduce the risk of attacks on microservices.

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features—scalability, portability, interoperability, and usability—that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.

The ubiquitous Internet of Things (IoT) devices nowadays are generating various and numerous data from everywhere at any time. Since it is not always necessary to centralize and analyze IoT data cumulatively (e.g., the Monte Carlo analytics and Convergence analytics demonstrated in this article), the traditional implementations of big data analytics (BDA) will suffer from unnecessary and expensive data transmissions as a result of the tight coupling between computing resource management and data processing logics. Inspired by software-defined infrastructure (SDI), we propose the “microservice-oriented platform” to break the environmental monolith and further decouple data processing logics from their underlying resource management in order to facilitate BDA implementations in the IoT environment (which we name “IoBDA”). Given predesigned standard microservices with respect to specific data processing logics, the proposed platform is expected to largely reduce the complexity in and relieve inexperienced practices of IoBDA implementations. The potential contributions to the relevant communities include (1) new theories of a microservice-oriented platform on top of SDI and (2) a functional microservice-oriented platform for IoBDA with a group of predesigned microservices.

Information and decision support systems are essential to conducting scientific field campaigns in the atmospheric sciences. However, their development is costly and time-consuming since each field campaign has its own research goals, which result in using a unique set of sensors and various analysis procedures. To reduce development costs, we present a software framework that is based on the Industrial Internet of Things (IIoT) and an implementation using well-established and newly developed open-source components. This framework architecture and these components allow developers to customize the software to a campaign’s specific needs while keeping the coding to a minimum. The framework’s applicability was tested in two scientific field campaigns that dealt with questions regarding air quality by developing specialized IIoT applications for each one. Each application provided the online monitoring of the acquired data and an intuitive interface for the scientific team to perform the analysis. The framework presented in this study is sufficiently robust and adaptable to meet the diverse requirements of field campaigns.

The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets ( ), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets . Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems.

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user’s software platforms.

As monolithic applications grow in size and complexity, they tend to show symptoms of monolithic hell, such as scalability and maintainability problems. To help suppressing these problems, the microservices architectural style is applied. However, identifying the services within the monolith is not an easy task, and current research approaches emphasize different qualities of microservices. In this paper we present an approach for the automatic identification of microservices, which minimizes the cost of the monolith’s functionalities redesign. The decompositions are generated based on similarity measures between the persistent domain entities of the monolith. An extensive analysis of the decompositions generated for 121 monolith systems is done. As result of the analysis we conclude that there is not a similarity measure, neither a combination of similarity measures, that provides better decomposition results in terms of complexity associated with the functionalities migration. However, we prove that it is possible to follow an incremental migration process of monoliths. Additionally, we conclude that there is a positive correlation between coupling and complexity, and that it is not possible to conclude on the existence of a correlation between cohesion and complexity.