Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are generated at the edge of the infrastructure and transmitted to the cloud, where traditional data analytics techniques are applied. Currently, developers are forced to resort to ad hoc solutions specifically tailored for the available infrastructure (for example, edge devices) when designing, developing, and operating the data analytics applications. Here, a novel approach implements cloud-supported, real-time data analytics in edge-computing applications. The authors introduce their serverless edge-data analytics platform and application model and discuss their main design requirements and challenges, based on real-life healthcare use case scenarios.

Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

In this paper, a deviceless edge computing solution is analysed in contrast to the “traditional” edge server solution. We compare centralized with the distributed deviceless approaches for horizontal offloading of data and computations, and analyze the requirements of protocols to realize such solutions. The proposed deviceless solutions are more energy-efficient (IoT and edge devices may work for a longer period without recharging), provide a scalable and elastic environment and extended fault tolerance.

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.

The national e-health systems implemented in Serbia as ”MojDoktor” (MyDoctor) and in Macedonia as ”MojTermin” (MyAppointment) are based on the same integrated health information platform. Its architecture was designed to allow for process oriented development with agile methodologies. This methodology allowed for fast deployment and adoption, but a change in the architecture to a more formal approach is required to assure its extensibility, soundness, interoperability and standardization. In this paper, we propose a formalization of the system design and its implementation as a Model Driven Architecture®. We develop a set of formal models on several abstraction levels and explain how different layers of the MDA framework are covered using them. We also propose a model for data transformations that will provide interfaces for interoperability between an external and our system.

This paper presents a novel quality model, which provides a quantitative assessment of the attributes evaluated at each stage of development of scientific applications. This model is defined by selecting a set of attributes and metrics that affect the quality of applications. It is based on the established quality standards. The practical application and verification of the quality model is confirmed by two case studies. The first is an application for solving one-dimensional and two-dimensional Schrödinger equations, using the discrete variables representation method. The second is an application for calculating an ECG-derived heart rate and respiratory rate. The first application follows a development model for scientific applications, which includes some software engineering practices. The second application does not use a specific development model, rather, it is developed ad hoc. The quality of the applications is evaluated through comparative analyses using the proposed model. Based on software quality metrics, the results of this study indicate that the application for solving one-dimensional and two-dimensional Schrödinger equations produces more desirable results.