Implementation of E-Health Cloud System Using Timing Enabled Proxy Re-encryption function (Re-dtPECK)

To meet the needs of Body Sensor Network (BSN) intelligent terminal remote communication ,the scheme of network communication design in the Android intelligent terminals is provided .And combined with the characteristics of Android ,the method of Android network communication’s development is discussed .Firstly ,the architecture of intelligent health-cloud system and Android’s framework are introduced .And then the HTTP ,Web Service and other means of communication in Android are analyzed. Finally, a model of network communication in the intelligent medical is constructed and network communication in the intelligent terminal is carried out .The results show that Android can effectively take control of network communication in the BSN’s intelligent terminal and it has practical value.

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Cost-effective secure E-health cloud system using identity based cryptographic techniques

Future Generation Computer Systems ◽

10.1016/j.future.2016.08.008 ◽

2017 ◽

Vol 67 ◽

pp. 242-254 ◽

Cited By ~ 43

Author(s):

Xu An Wang ◽

Jianfeng Ma ◽

Fatos Xhafa ◽

Mingwu Zhang ◽

Xiaoshuang Luo

Keyword(s):

Cost Effective ◽

Cloud System ◽

Identity Based ◽

Cryptographic Techniques ◽

Health Cloud

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An Efficient Approach for Secured E-Health Cloud System Using Identity Based Cryptography Techniques in Cloud Computing Environment

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES ◽

10.26782/jmcms.2019.06.00010 ◽

2019 ◽

Vol 14 (3) ◽

Author(s):

Shikha Mittal

Keyword(s):

Cloud Computing ◽

Cloud System ◽

Computing Environment ◽

Cloud Computing Environment ◽

Efficient Approach ◽

Identity Based ◽

Identity Based Cryptography ◽

Health Cloud

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Security of Cloud System by IDS-AM-Clust

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v13i1.15644 ◽

2018 ◽

Vol 13 (1) ◽

pp. 1

Author(s):

Chaimae Saadi ◽

Habiba Chaoui

Keyword(s):

Cloud System

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Design and Development of a Web-based Delirium Preventive Application for Long-term Care Facilities: A Methodological Study (Preprint)

10.2196/preprints.26092 ◽

2020 ◽

Author(s):

Kyoung Ja Moon ◽

Chang-Sik Son ◽

Jong-Ha Lee ◽

Mina Park

Keyword(s):

Risk Factors ◽

Long Term Care ◽

Cloud System ◽

Evidence Based ◽

Methodological Study ◽

Term Care ◽

Web Based ◽

Care Facilities ◽

Delirium Prevention

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none

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A novel approach for IoT tasks offloading in edge-cloud environments

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00243-9 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Jaber Almutairi ◽

Mohammad Aldossary

Keyword(s):

Service Time ◽

Edge Computing ◽

Cloud System ◽

Novel Approach ◽

Delay Sensitivity ◽

Cloud Environments ◽

Application Characteristics ◽

Iot Devices ◽

Computational Resources ◽

And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

Life ◽

10.3390/life11040310 ◽

2021 ◽

Vol 11 (4) ◽

pp. 310

Author(s):

Shih-Chia Chang ◽

Ming-Tsang Lu ◽

Tzu-Hui Pan ◽

Chiao-Shan Chen

Keyword(s):

Decision Making ◽

Cloud Computing ◽

Multiple Criteria Decision Making ◽

Cloud Service ◽

Computing System ◽

Healthcare Industry ◽

Computing Systems ◽

Factors Affecting ◽

Cloud Computing System ◽

Health Cloud

Although the electronic health (e-health) cloud computing system is a promising innovation, its adoption in the healthcare industry has been slow. This study investigated the adoption of e-health cloud computing systems in the healthcare industry and considered security functions, management, cloud service delivery, and cloud software for e-health cloud computing systems. Although numerous studies have determined factors affecting e-health cloud computing systems, few comprehensive reviews of factors and their relations have been conducted. Therefore, this study investigated the relations between the factors affecting e-health cloud computing systems by using a multiple criteria decision-making technique, in which decision-making trial and evaluation laboratory (DEMATEL), DANP (DEMATEL-based Analytic Network Process), and modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches were combined. The intended level of adoption of an e-health cloud computing system could be determined by using the proposed approach. The results of a case study performed on the Taiwanese healthcare industry indicated that the cloud management function must be primarily enhanced and that cost effectiveness is the most significant factor in the adoption of e-health cloud computing. This result is valuable for allocating resources to decrease performance gaps in the Taiwanese healthcare industry.

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