An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Shukla, Saurabh; Hassan, Mohd Fadzil; Khan, Muhammad Khalid; Jung, Low Tang; Awang, Azlan

doi:10.1371/journal.pone.0224934

Cited by 86 publications

(40 citation statements)

References 56 publications

(68 reference statements)

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“…Fog computing extends the cloud by migrating data processing closer to the production site, thereby accelerating the system’s responsiveness to events. The Fog infrastructure allows the management of multiple data from IoMT devices that send the information to the Fog node reducing the latency, response time or data delay [ 32 , 33 , 34 ]. Several low-cost applications using IoT and Fog devices can be found in the literature in order to streamline diagnostics.…”

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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“…Fog nodes are connected with the cloud and web application through a proxy server. We created sensors in the simulation environment according to the policies of [ 1 ]. Scenarios are simulated with an increasing number of sensor nodes per fog device to calculate the latency and network consumption.…”

Section: Simulation Setup and Resultsmentioning

confidence: 99%

“…There are to be about 237.1 million wearable body devices available on the market by 2020 with an estimated reach of market share related to the healthcare industry of USD 117 billion by 2020 [ 1 ]. The data flow by healthcare applications based on such a large number of bio-sensors is approximated to be 507.5 zettabytes [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

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“…e-Health applications are time-critical ones demanding instant data processing. Factors such as heart rate, sugar level, and blood pressure are monitored by FC [3,4,11,69,70,85,86,99,109,122,130,136,139].…”

Section: Applications Of Fog Computingmentioning

confidence: 99%

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Yadav

Mishra²,

Jayanthu

et al. 2021

IET Wireless Sensor Systems

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Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end-devices. This enhances the quality of service (QoS) to the proximity of end-users as latency-free processing is obtained in cloud-IoT-based environs. Mining industry will encounter a level-up due to these technological advancements like fog-IoT-based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time-sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state-of-art of fog, IoT, and cloud architectures are provided, and a Fog-IoT mines monitoring (FIoTMM) system for real-time monitoring of open-cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Cited by 86 publications

References 56 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

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

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