A federated content distribution system to build health data synchronization services

In organizational environments, such as in hospitals, data have to be processed, preserved, and shared with other organizations in a cost-efficient manner. Moreover, organizations have to accomplish different mandatory non-functional requirements imposed by the laws, protocols, and norms of each country. In this context, this paper presents a Federated Content Distribution System to build infrastructure-agnostic health data synchronization services. In this federation, each hospital manages local and federated services based on a pub/sub model. The local services manage users and contents (i.e., medical imagery) inside the hospital, whereas federated services allow the cooperation of different hospitals sharing resources and data. Data preparation schemes were implemented to add non-functional requirements to data. Moreover, data published in the content distribution system are automatically synchronized to all users subscribed to the catalog where the content was published.This work has been partially supported by the grant “CABAHLA-CM: Convergencia Big data-Hpc: de Los sensores a las Aplicaciones” (Ref: S2018/TCS-4423) of Madrid Regional Government; the Spanish Ministry of Science and Innovation Project ” New Data Intensive Computing Methods for High-End and Edge Computing Platforms (DECIDE)”. Ref. PID2019-107858GB-I00; and by the project 41756 “Plataforma tecnológica para la gestión, aseguramiento, intercambio y preservación de grandes volúmenes de datos en salud y construcción de un repositorio nacional de servicios de análisis de datos de salud” by the FORDECYT-PRONACES

On the efficient delivery and storage of IoT data in edge-fog-cloud environments

This article belongs to the Special Issue Internet of Things, Sensing and Cloud ComputingCloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge-fog-cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge-fog-cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.This research was funded by the project 41756 "Plataforma tecnológica para la gestión, aseguramiento, intercambio y preservación de grandes volúmenes de datos en salud y construcción de un repositorio nacional de servicios de análisis de datos de salud" by the PRONACES-CONACYT

On the continuous processing of health data in edge-fog-cloud computing by using micro/nanoservice composition

The edge, the fog, the cloud, and even the end-user's devices play a key role in the management of the health sensitive content/data lifecycle. However, the creation and management of solutions including multiple applications executed by multiple users in multiple environments (edge, the fog, and the cloud) to process multiple health repositories that, at the same time, fulfilling non-functional requirements (NFRs) represents a complex challenge for health care organizations. This paper presents the design, development, and implementation of an architectural model to create, on-demand, edge-fog-cloud processing structures to continuously handle big health data and, at the same time, to execute services for fulfilling NFRs. In this model, constructive and modular $blocks$ , implemented as microservices and nanoservices, are recursively interconnected to create edge-fog-cloud processing structures as ¿This work was supported in part by the Council for Science and Technology of Mexico (CONACYT) through the Basic Scientific Research under Grant 2016-01-285276, and in part by the Project CABAHLA-CM: Convergencia Big data-Hpc: de los sensores a las Aplicaciones from Madrid Regional Government under Grant S2018/TCS-4423

From the edge to the cloud: A continuous delivery and preparation model for processing big IoT data

This research was partially supported by "Fondo Sectorial de Investigación para la Educación", SEP-CONACyT Mexico, under grantnumbers 281565 and 285276, and by Madrid Regional Government (Spain) under the grant ”Convergencia Big data-Hpc: de los sensores a las Aplicaciones. (CABAHLA-CM)”, ref: S2018/TCS-4423