Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Quy, Vu Khanh; Nguyen, Van-Hau; Anh, Dang Van; Ngoc, Le Anh

doi:10.1007/s40747-021-00582-9

Cited by 96 publications

(42 citation statements)

References 60 publications

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“…To resolve this issue, interoperability can be achieved by either pushing standards or integration approaches. For example, in industry, one of the well-known Machine to Machine (M2M) standards offering interoperability at different layers is the European Telecommunication Standard Institute (ETSI) [15,16]. Also, interoperability in Industrial IoT is performed using diverse integration approaches such as gateway and middleware [17].…”

Section: Methodsmentioning

confidence: 99%

Development of automatic medical internet of things system (MIoT) for robotic surgery with multi-protocol approach using COAP and MQTT protocols

Deshpande¹,

Jogdand²

2022

ijhs

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Modern medical devices serving as blessings to patients worldwide as IoT systems are becoming pillar for healthcare professionals. IoT research is growing in the direction of automation of patient care systems to automation of robotic surgeries. Industrial IoT is serving numerous sectors like medical, agriculture, manufacturing and many more sectors. Hence, this paper presents the architecture for medical device sensors and devices which can be automated for fast response to incoming IoT protocol. We developed the multiprotocol communication approach where CoAP and MQTT protocols can respond to medical system control panel request by extracting the common message parameters. This can be helpful for several medical systems which need automation and quick response systems can be developed using proposed approach.

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Section: Methodsmentioning

confidence: 99%

Development of automatic medical internet of things system (MIoT) for robotic surgery with multi-protocol approach using COAP and MQTT protocols

Deshpande¹,

Jogdand²

2022

ijhs

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“…Scientists say that the platform acts as an alert system using early warning indicators (some forms of edge devices) [4]. Another group of researchers consider the architecture of fog-based applications [5]. They note that such an architecture consists of three layers: Thing Layer, Fog Layer and Cloud Layer.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Those are Arduino motherboards, IoT devices, sensors, body data collection tools (e.g. blood pressure, heart rate, glucose) and so on [5]. The authors of [6] suggest architecture for edge devices for diagnostics of students' physical condition.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Mathematical model of the base unit of the biotechnical system as a type of edge devices

Нікітчук

Vakaliuk

Andreiev

et al. 2022

J. Phys.: Conf. Ser.

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The rapid development of computer biometrics over the past 2-3 decades is largely due to the development and widespread introduction into clinical practice of new methods of studying the human body health, including pulse methods. It is possible to judge changes in hemodynamic characteristics, heart rate and blood flow rate in the studied part of the body based on the parameters of the pulse wave signal. At the same time, the physical processes of formation of the pulse wave shape have not been fully studied, although the number of biophysical models of blood circulation is quite significant. The development of such a model will allow to effectively apply modern developments in digital signal processing to the pulse wave and increase its diagnostic value. Qualitative model of pulse signal can be entrusted to the development of the base unit of the biotechnical system as a type of edge devices. The work is devoted to the improvement of methods of rapid diagnosis of the cardiovascular system based on the analysis of model pulsegrams. An adequate mathematical model of the pulse wave, which corresponds to real pulse signals in different states of the human body and contains mathematical relationships between the main parameters of pulsegrams, has been refined. The algorithm of express diagnostics with the established criteria of the analysis of pulsograms is offered.

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“…The increase in human population coupled with the decrease in natural resources has raised concerns for food security [1]. Efforts were made to improve productivity in agriculture to…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

et al. 2022

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An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, timeconsuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.INDEX TERMS Internet of Things (IoT), machine learning, soil fertility mapping, fertilizer recommendation, support vector machine (SVM), Gaussian Naïve Bayes (GNB), logistic regression (LR), k-nearest neighbor (KNN).

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Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Cited by 96 publications

References 60 publications

Development of automatic medical internet of things system (MIoT) for robotic surgery with multi-protocol approach using COAP and MQTT protocols

Development of automatic medical internet of things system (MIoT) for robotic surgery with multi-protocol approach using COAP and MQTT protocols

Mathematical model of the base unit of the biotechnical system as a type of edge devices

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

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