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

Asghari, Parvaneh; Rahmani, Amir Masoud; Javadi, Hamid Haj Seyyed

doi:10.1002/ett.3637

Cited by 47 publications

(49 citation statements)

References 56 publications

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“…Also, a hybrid intelligent approach was proposed for chronic kidney disease prediction in cloud-based IoT environment, in [32]. Recently, a medical monitoring scheme for cloud-based IoT platforms was proposed in [7] which applied a variety of classification methods for predicting a combination of diabetes mellitus, renal disorder, hypertension, and heart disease. Furthermore, in this paper, a medical/health service composition model extended by the authors in [33] was provided for the required recommendation that was produced by the offered system.…”

Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

“…In the surveyed papers, the security issues were considered in [7,20,30,31], while in others they were not focused. Comparing to the analyzed papers, we aim to offer a secure remote health monitoring model in cloud-based IoT environment using data mining methods for early disease diagnosis which uses a lightweight block encryption method that is a proper solution for constrained medical IoT resources [42] which has not been focused in the studied papers.…”

Section: Security Solutions In Iot Medical Data Managementmentioning

confidence: 99%

“…In recent years, the growth of IoT and sensor technology related to wearable medical devices has enriched the patients’ care quality through smart remote health monitoring systems [ 6 ]. Nowadays, the cloud-based IoT platforms are applied widely in smart remote health and medical monitoring systems [ 7 , 8 ]. The combination of cloud and IoT has many benefits from resource management aspects such as resource distribution, powerful processing, avoiding from data fragmentation over various databases, and supporting user mobility in monitoring systems [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

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Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

Section: Security Solutions In Iot Medical Data Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the IoT is also necessary for executing the smart services along with the context‐awareness 17 . Moreover, the IoT data comprises of numerous sensed and collected data obtained from the sensors that employ in tracking the temperature, respiratory rate flow, and so on 18 . The combination of both health care system and IoT technologies is applied in several medical sectors such as managing the data of the patients, health care management, and real‐time monitoring 19 .…”

Section: Introductionmentioning

confidence: 99%

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

Kavitha

Ravikumar²

2020

Trans Emerging Tel Tech

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Recently, numerous context‐aware approaches are established to provide physiological information regarding the wellness and healthcare of each individual. While monitoring the health condition of the patient there occur delays in transferring data to the cloud. So to overcome such types of delays, numerous IoT sensors are developed to monitor, track, and sense the activities of the elder persons. This paper proposes four‐module architecture comprises of IoT module (IoT‐M), data pre‐processing module (DP‐M), context‐aware module (CA‐M) as well as decision‐making module (DM‐M) for storing and processing numerous cumulative sensor data. Here, an IoT comprises a concrete or substantial hardware ecological unit, that is, sensors and actuators. On the other hand, the context‐aware computational approach comprises of insubstantial software ecological unit in understanding and processing the context directly into an accomplishment through various IoT devices. The initial module or the IoT‐M comprises of sensors whereas in the DP‐M phase includes data collection phase, data storage phase, and data redundancy phase. The third phase or the CA‐M comprises two different types of layers namely fog layer, cloud layer. In addition to this, a context‐aware learning phase is also enumerated. In the final phase or the DM‐P phase, the feature extraction and classification is done by Back‐Propagation Neural Network along with the Adaptive grasshopper optimization algorithm so as to obtain a best optimal solution. Thus, an alarm or a notification is sent to the medical practitioner regarding the health condition of the patient with very less response time, high accuracy, and a high scalability rate. The evaluation results and discussions are made by comparing our proposed approach with several other approaches and the evaluation results reveal that the proposed framework provides better results with high accuracy, scalability, network latency, and low response time.

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“…With the promotion of Internet technology and the popularization of smart devices, the Internet of Thing (IoT) 1 has been developed prosperously. The IoT connects various information sensing devices and the Internet to form a unified network system that connects many smart devices, and some smart systems based on IoT (eg, smart cities, smart medical systems) 2 have greatly improved different aspects of human life. The development of advanced technologies (eg, 5G network) 3,4 in the IoT greatly expands the perception of communication networks, which provides the impetus for the generation of mobile crowdsensing (MCS) 5 .…”

Section: Introductionmentioning

confidence: 99%

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

Xia

Zhao

Tang

et al. 2020

Trans Emerging Tel Tech

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Recently, the development of Internet of Things and mobile sensor technology has facilitated mobile crowdsensing (MCS) as a popular data collection paradigm. In MCS, requesters publish tasks to the sensing platform, and then the sensing platform assigns tasks to workers who carry mobile sensing devices. In general, the sensing platform matches tasks and workers according to the location and time attributes of tasks and workers. However, most current studies rarely consider location and time attributes, simultaneously. In previous work, it is difficult to achieve real‐time task assignment while protecting worker's location privacy. To tackle the above problem, in this article, we propose a real‐time and privacy‐preserving online task assignment scheme, named Repot. In Repot, the location privacy of workers is protected by using geoindistinguishability. And we design a probabilistic method to quantify the reachability between the obfuscated worker and the task. In addition, the worker‐based distance comparison mechanism and the task‐based distance comparison mechanism are designed to reduce the overall distance of workers in Repot, respectively. The experimental evaluation is performed on a real‐world data set and the results show the feasibility and effectiveness of Repot.

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

Cited by 47 publications

References 56 publications

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

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