Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

Bandopadhaya, Shuvabrata; Dey, Rajiv; Suhag, Ashok Kumar

doi:10.1016/j.suscom.2020.100378

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…The framework, called FogCEPCare, used a set of custom services that work proactively using the complex event processing architecture of cloud computing. Bandopadhaya et al [38] presented an integrated health monitoring system based on IoT and distributed computing for deployed soldiers. The proposed IoT architecture was service-oriented in three layers, where the computing functionalities were distributed among all the layers in order to improve the security of the soldier and to obtain a fast response to any unexpected event Cost reduction and communication efficiency are also an extremely important purpose of many monitoring systems.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon

Guerra-Ojeda

Carrascosa

et al. 2020

Sensors

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Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.

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Section: State Of the Artmentioning

confidence: 99%

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon

Guerra-Ojeda

Carrascosa

et al. 2020

Sensors

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“…As shown in the results, cell (10) is ranked as the most suspicious cell in region 1 with a suspicion score of 0.8333 followed by cells (2,18,9,11,17,25,3). On the other hand, cell ( 23) is ranked as the top suspicious cell in region 2 with a suspicion score of 0.75 followed by cells (30,31,4,12,13,14,15,32,22). The regional manager then records the maximum reached suspicion rank (Ω) within its region (see Algorithm 1 (line 4)).…”

Section: Re-organization Within the Regionmentioning

confidence: 73%

“…The rapid developments in the area of the Internet of Things (IoT) and their integration with fog, edge, and cloud computing technologies provide an ideal platform for a new generation of advanced surveillance systems. To this end, several IoT-based surveillance systems were proposed in recent years (e.g., [7,11,12,46,49,50,53,58]). In these systems, IoT based sensors and other edge devices collect and initially process raw data.…”

Section: Iot-based Surveillance Systemsmentioning

confidence: 99%

A re-organizing biosurveillance framework based on fog and mobile edge computing

Al-Zinati

Al-Rashdan

Al-Duwairi

et al. 2020

Multimed Tools Appl

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Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate responses to biological threats and consequently save human lives. Nevertheless, biosurveillance systems are costly to implement and hard to operate. Furthermore, they rely on static infrastructures that might not cope with the evolving dynamics of the monitored environment. In this paper, we present a reorganizing biosurveillance framework for the detection and localization of biological threats with fog and mobile edge computing support. In the proposed framework, a hierarchy of fog nodes are responsible for aggregating monitoring data within their regions and detecting potential threats. Although fog nodes are deployed on a fixed base station infrastructure, the framework provides an innovative technique for reorganizing the monitored environment structure to adapt to the evolving environmental conditions and to overcome the limitations of the static base station infrastructure. Evaluation results illustrate the ability of the framework to localize biological threats and detect infected areas. Moreover, the results show the effectiveness of the reorganization mechanisms in adjusting the environment structure to cope with the highly dynamic environment. KeywordsMobile edge computing • Fog computing • Biosurveillance systems • Edge cloud data management IntroductionWith the emergence of the recent COVID-19 outbreak, several contact-tracing applications have been proposed to alert users if they have come in close contact with someone who

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“…Bandopadhaya et al [27] suggested a structured medical care checking response using the IoT with transmitted figures for warriors expressed in antagonistic ecological conditions. The e-care boundaries of each individual should be regularly monitored by these officers and the subsequent investigation of the dataset to be rendered with the lowest possible delay to start proper clinical assistance.…”

Section: Fog Computing-based Iot For Health Monitoringmentioning

confidence: 99%

An Overview on Analyzing Deep Learning and Transfer Learning Approaches for Health Monitoring

Wang

Nazir

Shafiq

2021

Computational and Mathematical Methods in Medicine

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With the rise and advancement of technology, early detection and involvement in health-associated monitoring through home control are growing with population aging. The expansion of healthy life expectations is progressively significant due to the speedy aging of the world population. The patient requires early and home-based treatment to detect and prevent disease on time and with less effort. Home-based health monitoring has been considered the need of a smart home. The services of health monitoring can facilitate the patient by collecting and analyzing the data of health for tackling diverse complex issues of health at a large scale. Health monitoring is a sustainable progression of clinical trials for ensuring that health is monitored according to the defined protocol and standard operating procedures. Various scenarios can be considered for monitoring health and are performed through experts of the field. Healthcare systems are having large-scale infrastructure of electronic devices, medical information systems, wearable and smart devices, medical records, and handheld devices. The growth in medical infrastructure, combined with the development of computational approaches in healthcare, has empowered practitioners and researchers to devise a novel solution in the innovative spectra. A detailed report of the existing literature in terms of deep learning and transfer learning is the dire need and facilitating of modern healthcare. To overcome these limitations, therefore, the proposed study presents a comprehensive review of the existing approaches, techniques, and methods associated with deep learning and transfer learning for health monitoring. This review will help researchers to formulate new ideas for facilitating healthcare based on the existing evidence.

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Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

Cited by 17 publications

References 14 publications

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

A re-organizing biosurveillance framework based on fog and mobile edge computing

An Overview on Analyzing Deep Learning and Transfer Learning Approaches for Health Monitoring

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