A Real-Time Fog Computing Approach for Healthcare Environment

Gomes, Eliza; Dantas, Mário A. R.; Plentz, Patricia Della Mea

doi:10.1007/978-3-030-02607-3_8

Cited by 6 publications

(9 citation statements)

References 19 publications

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“…For this, the Earliest Deadline First approach is applied. A similar approach is provided by Gomes et al [41], aiming at healthcare environments. A conceptual approach to enable real-time fog computing is presented by Kopetz and Poledna [42], where the authors discuss the basic concept of time-triggered VMs.…”

Section: Resource Managementmentioning

confidence: 98%

The FORA Fog Computing Platform for Industrial IoT

Pop

Zarrin

Barzegaran

et al. 2021

Information Systems

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Section: Resource Managementmentioning

confidence: 98%

The FORA Fog Computing Platform for Industrial IoT

Pop

Zarrin

Barzegaran

et al. 2021

Information Systems

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“…This section presents a review of existing work, focusing on scheduling policies used for allocating services in healthcare monitoring systems, as well as the characteristics of servers used. The EDF scheduling scheme, which assigns priorities to requests based on their absolute deadlines, was proposed in [4]. Data packets with short deadlines are assigned higher priority, while those with long deadlines are assigned lower priority.…”

Section: Related Workmentioning

confidence: 99%

Delay and size‐dependent priority‐aware scheduling for IoT‐based healthcare traffic using heterogeneous multi‐server priority queueing system

Asingwire,

Sibomana,

Ngenzi

et al. 2023

IET Communications

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Internet of Things (IoT) based healthcare applications are time‐sensitive and any delay can cause alarming situations, including death of patients. The Earliest Deadline First (EDF) scheduling scheme has been proposed for use in IoT‐based healthcare applications. However, the EDF scheme performs poorly under overloaded conditions due to giving highest priority to packets that are close to missing their deadlines. Some studies have proposed the use of Priority EDF to overcome the challenges of EDF; however, Priority EDF still favours higher priority queues which increases the waiting times of lower priority queues. In order to overcome the limitation of EDF and its variants, this paper proposes a system model for a prioritized scheduling (PS) scheme. The PS scheme is an improvement of the Earliest Deadline First (EDF) scheme and its variants for IoT‐based healthcare applications. The PS scheme uses a heterogeneous multi‐server priority queuing system to provide service differentiation by prioritizing short packets over large packets and delay sensitive packets are serviced before delay tolerant packets. Numerical results demonstrate that the PS scheme minimizes the mean slowdown for both delay sensitive short and large packets at low and high load values. Additionally, the PS scheme performs better than the EDF and Priority EDF schemes in terms of reducing mean slowdown of packets and the PS scheme performs better than the EDF in terms of throughput for all packet sizes at both low and high load values. The performance improvement in terms of throughput is more pronounced at high load values. This addresses the challenge of the EDF scheme which performs poorly under overloaded conditions and the challenge of the Priority EDF scheme which favours higher priority queues at the expense of low priority queues.

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“…Operational management may be realized by automatic health checking systems discussed in the many works of literature (Moore and Van Pham, 2018). The e-health services help monitor and assess the users' health, including elders with disabilities, children or patients (Gomes et al, 2018). Using smart objects, they can be combined with health care to deliver smart amenities for remote checking of the individuals.…”

Section: Framework and Modelsmentioning

confidence: 99%

“…However, owing to the limit of the edge-to-edge connectivity of the network to transfer the ECG signal to the centralized analysis of the cloud, saving overall energy has been decreased to 22 per cent when the patient's processing reaches 200. Gomes et al (2018) have provided FC-based hardware and software infrastructure as an immediate checking mechanism in accordance with the needs of the health environment. The infrastructure can store, process and provide real-time monitoring data based on the FC parameter.…”

Section: Framework and Modelsmentioning

confidence: 99%

Fog computing in health management processing systems

Badrnejad³

2020

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Purpose Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works. Design/methodology/approach The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms. Findings The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing. Research limitations/implications This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English. Practical implications The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future. Originality/value Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.

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A Real-Time Fog Computing Approach for Healthcare Environment

Cited by 6 publications

References 19 publications

The FORA Fog Computing Platform for Industrial IoT

The FORA Fog Computing Platform for Industrial IoT

Delay and size‐dependent priority‐aware scheduling for IoT‐based healthcare traffic using heterogeneous multi‐server priority queueing system

Fog computing in health management processing systems

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