On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

Abstract

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 infrastructureagnostic services. Continuity schemes create dataflows through the blocks of edge-fog-cloud structures and enforce, in an implicit manner, the fulfillment of NFRs for data arriving and departing to/from each block of each edge-fog-cloud structure. To show the feasibility of this model, a prototype was built using this model, which was evaluated in a case study based on the processing of health data for supporting critical decision-making procedures in remote patient monitoring. This study considered scenarios where end-users and medical staff received insights discovered when processing electrocardiograms (ECGs) produced by sensors in wireless IoT devices as well as where physicians received patient records (spirometry studies, ECGs and tomography images) and warnings raised when online analyzing and identifying anomalies in the analyzed ECG data. A scenario where organizations manage multiple simultaneous each edge-fog-cloud structure for processing of health data and contents delivered to internal and external staff was also studied. The evaluation of these scenarios showed the feasibility of applying this model to the building of solutions interconnecting multiple services/applications managing big health data through different environments. INDEX TERMS Big health data, edge-fog-cloud, health-IoT processing, Internet of Things, microservice architecture.

A WoT-Based Method for Creating Digital Sentinel Twins of IoT Devices

“…To conduct the evaluation of the prototype, a containerized application ( ) was deployed on the previously described infrastructure, one instance of the running on one virtual container . This extracts data from real traces produced by ECG medical devices (IoT devices for acquiring electrocardiogram (ECG) signals) [ 11 ], and builds workloads at a given rate time, following a synthetic distribution. An input parameter defines the amount of data to be included in the workload.…”

“…In this lifecycle, data are acquired (mainly at the edge [ 9 ]), prepared and analyzed (typically at the fog or the cloud [ 10 ]), and finally converted into information for human consumption to use it in decision-making processes (mainly at the cloud [ 8 ] through end-user devices). In these types of infrastructures (any combination of edge, fog, or cloud), the virtual containers ( ) are key to deploy services on each infrastructure [ 11 , 12 , 13 ]. These services provide dataflows from the IoT to the cloud that produce different types of data and information, which proves to be key for organizations to conduct critical decision-making processes [ 14 , 15 , 16 ].…”

Dynamic load balancing assisted optimized access control mechanism for Edge‐Fog‐Cloud network in Internet of Things environment

“…Similarly, the authors discuss the fog/edge computing based IoT architecture, applications, and open research issues in Reference 25. The continuous processing of health data is discussed in Reference 26 using micro/nano service composition in an Edge‐Fog‐Cloud computing environment. In addition, an Edge‐Fog‐Cloud platform is detailed in Reference 27 for the anticipatory learning process, specifically designed for the internet of mobile things.…”

Evaluation and Quality Assurance of Fog Computing‐Based IoT for Health Monitoring System

“…In view of the interesting properties of haze, such as low jitter, low idleness, enhanced protection, and so on, it is argued that the philosophy of fog extraction has extraordinary potential for high embedded platforms for data and information. Sanchez-Gallegos et al [25] presented a study on the plan creation, as well as implementation of an engineering model to build on request edge-mist cloud handling frameworks to deal consistently with enormous data and simultaneously execute NFR filling administration. Effective and calculated squares, revised as microservices and nanoservices, are recursively interconnected in this model to construct edge-haze cloud planning systems as a rationalist administrative framework.…”