Real-Time Cloud-Based Health Tracking and Monitoring System in Designed Boundary for Cardiology Patients

Shahzad, Aamir; Lee, Yang Sun; Lee, Malrey; Kim, Young-Gab; Xiong, Naixue

doi:10.1155/2018/3202787

Cited by 32 publications

(13 citation statements)

References 29 publications

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“…After extracting and processing the sensor data to decide the patient's condition, it must be stored for further analysis. For this purpose, cloud assistive technology [36] will bring a better outcome. Assume that there is a geo-dispersed data center DC n , where {n = 1, 2, 3, .…”

Section: Step 3: Storage and Accessmentioning

confidence: 99%

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

Jayalakshmi¹,

Garg²,

Maharajan³

et al. 2021

Computers, Materials &Amp; Continua

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In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID'19 patient's conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID'19 patients and quarantined people. It identi es the patient's medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identi cation. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID'19 patients based on the current context.

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Section: Step 3: Storage and Accessmentioning

confidence: 99%

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

Jayalakshmi¹,

Garg²,

Maharajan³

et al. 2021

Computers, Materials &Amp; Continua

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“…In this section, the literature related to the proposed privacy model is studied from various aspects. The data collected through the various IoT enabled devices [1][2][3][4][5][6]20,21] must be anonymized before publishing because of the private information contained in it. Anonymized data are published for the sake of its maximum utility without disclosing the private information of an individual.…”

Section: Related Workmentioning

confidence: 99%

θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

et al. 2020

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The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%).

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“…For obtaining medium-and long-term predictions, we need long records of traffic traces [20]. This The values of MSE in ARIMA model, as applied to long records of traffic traces with medium-and long-term predictions do not significantly change, being equal to 0.035.…”

Section: моделирование систем и процессовmentioning

confidence: 99%

Storage scaling management model

Sovetov

Tatarnikova

Poymanova

2020

ICS

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Introduction: The implementation of data storage process requires timely scaling of the infrastructure to accommodate the data received for storage. Given the rapid accumulation of data, new models of storage capacity management are needed, which should take into account the hierarchical structure of the data storage, various requirements for file storage and restrictions on the storage media size. Purpose: To propose a model for timely scaling of the storage infrastructure based on predictive estimates of the moment when the data storage media is fully filled. Results: A model of storage capacity management is presented, based on the analysis of storage system state patterns. A pattern is a matrix each cell of which reflects the filling state of the storage medium at an appropriate level in the hierarchical structure of the storage system. A matrix cell is characterized by the real, limit, and maximum values of its carrier capacity. To solve the scaling problem for a data storage system means to predict the moments when the limit capacity and maximum capacity of the data carrier are reached. The difference between the predictive estimatesis the time which the administrator has to connect extra media. It is proposed to calculate the values of the predictive estimates programmatically, using machine learning methods. It is shown that when making a short-term prediction, machine learning methods have lower accuracy than ARIMA, an integrated model of autoregression and moving average. However, when making a long-term forecast, machine learning methods provide results commensurate with those from ARIMA. Practical relevance: The proposed model is necessary for timely allocation of storage capacity for incoming data. The implementation of this model at the storage input allows you to automate the process of connecting media, which helps prevent the loss of data entering the system.

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Real-Time Cloud-Based Health Tracking and Monitoring System in Designed Boundary for Cardiology Patients

Cited by 32 publications

References 29 publications

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

Storage scaling management model

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