FogDLearner: A Deep Learning-based Cardiac Health Diagnosis Framework using Fog Computing

Iftikhar, Sundas; Golec, Muhammed; Chowdhury, Deepraj; Gill, Sukhpal Singh; Uhlig, Steve

doi:10.1145/3511616.3513108

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…The accuracy of the model will improve in classifying COVID‐19 and other X‐ray images as more people use it. In the future, the paper can be expanded upon and integrated into various IoT architectures, such as IoTpi (Shao et al, 2022 ), as well as various fog and cloud computing architectures, such as FogDLearner (Iftikhar et al, 2022 ) and HealthCloud (Desai et al, 2022 ). Additionally, a smart embedded device like a smartwatch can be incorporated with this work (Saleem et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Federated learning based Covid‐19 detection

et al. 2022

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The world is affected by COVID‐19, an infectious disease caused by the SARS‐CoV‐2 virus. Tests are necessary for everyone as the number of COVID‐19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID‐19 in a few seconds by uploading a single chest X‐ray image. A deep learning‐aided architecture that can handle client and server sides efficiently has been proposed in this work. The front‐end part has been developed using StreamLit, and the back‐end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID‐19 pandemic and will help to push the envelope of this work to a different extent.

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Section: Discussionmentioning

confidence: 99%

Federated learning based Covid‐19 detection

et al. 2022

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“…However, due to the centralized approach of the Cloud, this has lacked various important features such as contextual and location awareness [47]. If Edge Computing processes data at the edge network, then context and location awareness are much more readily obtainable and achievable [54]. Low latency: Reducing the time required when a packet travels from a node to the destination is critical in high processing applications such as augmented reality and gaming where mobile users expect uninterrupted services from the content provider [55].…”

Section: Edge Computingmentioning

confidence: 99%

“…Edge AI has a wide range of applications in various industries [54]. Some examples of how Edge AI is being used today.…”

Section: Edge Aimentioning

confidence: 99%

Edge AI: A survey

Singh

Gill

2023

Internet of Things and Cyber-Physical Systems

100

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“…The authors considered application‐level context in terms of networking requirements as contextual information for better utilization of resources. Iftikhar et al 31 presented FogDLearner, a framework for detecting heart health based on Fog computing that utilizes a deep learning classifier. It maintains accuracy and desired QoS in terms of latency and energy efficiency while diagnosing a person's heart status.…”

Section: Background and Related Workmentioning

confidence: 99%

CaPTS scheduler: A context‐aware priority tuple scheduling for Fog computing paradigm

Islam

Kumar

2022

Trans Emerging Tel Tech

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With the increase in real‐time latency‐sensitive Internet of Things (IoT) applications, a huge amount of data is generated in the Fog‐IoT paradigm. There is a need to schedule and execute this huge workload over Fog devices efficiently to support the increasing demand of these applications. But, Fog devices are resource‐constrained in terms of processing/computing power, bandwidth as well as storage capacity which makes tuple scheduling a challenging problem. Moreover, due to the rise in IoT devices per application, a sharp increase in service response time, network congestion, and inefficiency in terms of energy consumption, and execution cost has been observed. Consequently, an efficient tuple scheduling algorithm is desirable that can reduce latency and network usage and optimize energy consumption and cost. Therefore, in this work, CaPTS scheduler: A Context‐aware Priority Tuple Scheduling for Fog computing paradigm is designed and proposed. It takes into consideration various context‐aware parameters such as task load of application, networking requirement, and data flow rate to set the priority of tuples and schedule them across Fog computing nodes while ensuring quick service response time and satisfying quality of service requirements of end‐users. The CaPTS scheduler is implemented and evaluated using iFogSim toolkit on various performance metrics such as latency, network usage, energy consumption, and cost. Its performance is validated through a case study on the smart mining industry system. The results show that on an average the latency and network usage are minimized by 35.93% and 44.20%, while energy consumption and cost are optimized by 4.55% and 30.92%, respectively, in comparison with baseline techniques.

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FogDLearner: A Deep Learning-based Cardiac Health Diagnosis Framework using Fog Computing

Cited by 12 publications

References 20 publications

Federated learning based Covid‐19 detection

Federated learning based Covid‐19 detection

Edge AI: A survey

CaPTS scheduler: A context‐aware priority tuple scheduling for Fog computing paradigm

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