I-Health: SDN-Based Fog Architecture for IIoT Applications in Healthcare

Sarkar, Joy Lal; Ramasamy, V.; Majumder, Abhishek; Pati, Bibudhendu; Panigrahi, Chhabi Rani; Wang, Weizheng; Qureshi, Nawab Muhammad Faseeh; Su, Chunhua; Dev, Kapal

doi:10.1109/tcbb.2022.3193918

Cited by 26 publications

(16 citation statements)

References 37 publications

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“…The MCS platform is the most important component of MCS. For one thing, it accepts task requests from task requesters [7] , [8] , [9] . For another, tasks are posted on the platform, and the platform recruits low-priced, high-quality workers to complete them through auctions or other means.…”

Section: Related Workmentioning

confidence: 99%

“…The MCS typically consists of three organic components [4] , [5] , [6] : task requesters, data platform and workers. The requesters order their tasks to the platform [7] , [8] , and the platform publishes those tasks and recruits workers to complete them [9] , [10] , [11] . The workers are people who carry smart devices embedded with rich sensors, such as smartphones, smartwatches or others [12] , [13] , [14] .…”

Section: Introductionmentioning

confidence: 99%

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A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Tang

Fan

Xie

et al. 2023

Computer Communications

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Tang

Fan

Xie

et al. 2023

Computer Communications

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“…Sending a large amount of data to the cloud would slow down the network and generate congestion, which can further be overcome by adding a fog computing (FC) layer in a hierarchical design between user-side IoT devices and the cloud. 3,4 The cloud layer's processing and storing operations can be partly performed by network devices like servers and routers in the fog layer. New study avenues have opened up as a result of automated disease identification and prediction based on the IoT-fog-cloud (IFC) integrated concept.…”

Section: Introductionmentioning

confidence: 99%

“…It is a terrific approach to managing enormous amounts of data and providing stakeholders with resources on demand using IoT sensors in real‐time applications. Sending a large amount of data to the cloud would slow down the network and generate congestion, which can further be overcome by adding a fog computing (FC) layer in a hierarchical design between user‐side IoT devices and the cloud 3,4 . The cloud layer's processing and storing operations can be partly performed by network devices like servers and routers in the fog layer.…”

Section: Introductionmentioning

confidence: 99%

IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

et al. 2023

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Performing a coronary disease diagnosis remotely is challenging now‐a‐days. COVID‐19 is a worldwide pandemic, and methods for detecting COVID‐19 are hampered by insufficient data and a lack of validation testing. Internet of Things (IoT) applications that rely on cloud computing (CC) are being studied in an effort to improve e‐Healthcare systems, even though CC presents substantial latency, bandwidth, energy consumption, security and privacy issues and so forth. The extension to CC, fog computing (FC), can overcome these said limitations. This study aims to diagnose COVID‐19 patients to fight the outbreak remotely. This study proposes IFCnCov, which enables remote users to diagnose COVID‐19 disease in real‐time using integrated IoT, FC, and CC principles, as well as ensemble learning (EL) and deep learning (DL). The proposed system is a two‐layered architecture, trained with DL approaches to two different datasets: a symptom‐based dataset and a chest x‐rays imaging dataset obtained from the Kaggle repository employing several evaluative measures. From various experiments, this proposed IFCnCov achieves comparatively enhanced accuracies of 97.71% and 98.64%, precision of 97.38% and 98.52%, sensitivity of 98.19% and 98.92%, specificity of 97.21% and 98.34%, and F1‐scores of 97.79% and 98.72% in the first and second stages respectively, which also outperforms some other considered state‐of‐the‐art works. Additionally, this work is validated in terms of several network parameters, including scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.

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“…he leakage of these data will threaten the information security of citizens and thus the national information security. To solve this problem, data ought to be encrypted before outsourcing to a cloud server and have strict access control, such as an e-health system [3] , [4] , [5] , [6] , which usually encrypts electronic health record data locally before uploading it to the cloud for storage and use. Although encrypted data can protect privacy, it creates difficulties for users to access and share data later.…”

Section: Introductionmentioning

confidence: 99%