Cloud-centric IoT based student healthcare monitoring framework

Verma, Prabal; Sood, Sandeep K.; Kalra, Sheetal

doi:10.1007/s12652-017-0520-6

Cited by 112 publications

(56 citation statements)

References 39 publications

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“…The IoT healthcare system provides an efficient monitoring and tracking that helps to improve the resource management of people [4]. Cloud computing is used to handle the healthcare data and provides resource sharing facilities like, flexibility, data service integration with scalable data storage, parallel processing and security problems early [5].…”

Section: Introductionmentioning

confidence: 99%

Challenges and opportunities in IoT healthcare systems: a systematic review

Selvaraj¹,

2019

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In this study, the latest research articles which are involved in the Internet of Things (IoT) based healthcare system are analyzed as the IoT is growing enormously in the healthcare systems such as health monitoring, fitness programs, etc. Numerous research has been carried out in the IoT based healthcare system to improve monitoring efficiency. The architecture used in the IoT especially the cloud integrated systems are investigated in this work. The factors such as accuracy and power consumption are the important concern in the IoT, hence the research works which are involved in improving the performance of the IoT based healthcare systems are discussed. Data management methods in the IoT based healthcare system with cloud facilities are also systematically analyzed in this study. The performance of the IoT based healthcare system along with its advantages and limitations are reviewed. Most research works are efficient in detecting several symptoms and can accurately predict the diseases. The IoT based healthcare system designed especially for elders is an efficient solution in monitoring their healthcare issues. Major limitations in the existing systems are high power consumption, availability of fewer resources and security issues due to the utilization of many devices.

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Section: Introductionmentioning

confidence: 99%

Challenges and opportunities in IoT healthcare systems: a systematic review

Selvaraj¹,

2019

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“…If one considers the telecommunication industry in the evolution to the Fifth Generation (5G) technologies as an example, both network applications and infrastructure services are increasingly deployed as virtualized software instances running in the cloud (Ericsson 2017). Similar trend has been witnessed in various areas such as health care (Verma et al 2017), manufacturing (Molano et al 2017), and transportation systems (Xue et al 2016). A main advantage of this shift is that, in cloud computing, resource provision can be adapted dynamically and automatically depending on the status of the cloud at runtime, thus maintaining the satisfactory Quality of Service (QoS) with efficient use of resources.…”

Section: Introductionmentioning

confidence: 75%

Tool-supported design of data aggregation processes in cloud monitoring systems

Cai

Gallina

Nyström

et al. 2018

J Ambient Intell Human Comput

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Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce development cost. In this paper, we propose a systematic approach for designing data aggregation processes in cloud monitoring systems. Our approach applies a feature-oriented taxonomy called DAGGTAX (Data AGGregation TAXonomy) to systematically specify the features of the designed system, and SAT-based analysis to check the consistency of the specifications. Following our approach, designers first specify the data aggregation processes by selecting and composing the features from DAGGTAX. These specified features, as well as design constraints, are then formalized as propositional formulas, whose consistency is checked by the Z3 SAT solver. To support our approach, we propose a design tool called SAFARE (SAt-based Feature-oriented dAta aggREgation design), which implements DAG-GTAX-based specification of data aggregation processes and design constraints, and integrates the state-of-the-art solver Z3 for automated analysis. We also propose a set of general design constraints, which are integrated by default in SAFARE. The effectiveness of our approach is demonstrated via a case study provided by industry, which aims to design a cloud monitoring system for video streaming. The case study shows that DAGGTAX and SAFARE can help designers to identify reusable features, eliminate infeasible design decisions, and derive crucial system parameters.

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“…In recent years, the cloud‐based IoT as a powerful platform for modern smart healthcare systems, simplifies remotely patients health monitoring via providing continuous medical information to involved parties who provide health/medical recommendations and services for patients . In such platforms, there are many advantages including resource sharing, applying the same data in several services, parallel processing, preventing of information disintegration over numerous databases and also, supporting user mobility …”

Section: Introductionmentioning

confidence: 99%

“…For gaining analytical information and models for decision making and predicting the patients' health/medical anomalies, different data analytic approaches are applied in wide range of healthcare and digital medical monitoring systems such as neural network (NN), clustering, and classification methods based on effective algorithms …”

Section: Introductionmentioning

confidence: 99%

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

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Advanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions.

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Cloud-centric IoT based student healthcare monitoring framework

Cited by 112 publications

References 39 publications

Challenges and opportunities in IoT healthcare systems: a systematic review

Challenges and opportunities in IoT healthcare systems: a systematic review

Tool-supported design of data aggregation processes in cloud monitoring systems

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

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