fog computing
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Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.
Infrastruktura jako služba, tedy infrastruktura poskytovaná zákazníkovi formou služby poskytovatele, je jedním z modelu nasazení tzv. cloud computingu, který umožňuje využít datovou a výpočetní kapacitu v cloudu jako množinu fyzických či virtuálních zařízení. Infrastruktura jako služba může být poskytována zvlášť každému výzkumnému projektu a přitom sdílet stejné fyzické kapacity zapojených počítačů a zařízení. V současné době je testováno poskytování infrastruktury jako služby několika projektům v rámci aktivit sdružení CESNET, 1. lékařské fakulty Univerzity Karlovy v Praze (1. LF UK) a Hudební a taneční fakulty Akademie múzických umění v Praze (HAMU). Současný výzkum v oblasti výpočetní fyziologie je náročný na výpočetní kapacitu. Výpočetní úlohy jsou distribuovány počítačům, které jsou poskytovány infrastrukturou. Projekt v oblasti analýzy lidského hlasu je náročný na propustnost počítačové sítě mezi akustickým a video zařízením na lokální straně a analytickou aplikací na straně výkonného serveru. Tento příspěvek popisuje hlavní vlastnosti a výzvy pro infrastruktury určené pro takovýto typ aplikací. Infrastruktura jako model nasazení v rámci cloud computingu může být vhodná pro mezioborové týmy a pro spolupráci a integraci vysoce specializovaných softwarových aplikací.
Infrastructure as a service (infrastructure which is offered to a customer in the form of service of the provider) is a deployment model which allows utilize data and computing capacity of a cloud as a set of virtual devices and virtualized machines. Infrastructure as a service can be offered separately to each project. The same capacity of connected physical machines and devices can be shared. Currently, the concept of an Infrastructure as a service is tested on several projects within activity of the CESNET association, First Faculty of Medicine, Charles University, Prague and Musical and Dance Faculty of Academy of Performing Arts in Prague. The current research in the field of computation physiology is demanding on a high computation capacity. The computation tasks are distributed to computers, which are provided by the infrastructure. The project in the field of the analysis of a human voice is demanding on high throughput of a computer network between an acoustic or video device on the local side and an analytic application on the remote high performance server side. This paper describes features and main challenges for infrastructure dedicated for such a type of an application. Infrastructure as a deployment model of cloud computing might be beneficial for a multi domain team and for collaboration and integration of a high specialized software application.
- Klíčová slova
- infrastruktura jako služba, virtualizace, virtualizace, výpočetní fyziologie, identifikace fyziologických systémů, validace fyziologických systémů, protokol vzdálené plochy, grid computing, hlasové pole,
- MeSH
- biomedicínské technologie MeSH
- cloud computing MeSH
- financování organizované MeSH
- počítačové komunikační sítě organizace a řízení trendy využití MeSH
- počítačové systémy trendy využití MeSH
- využití lékařské informatiky MeSH
Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients' biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.
- MeSH
- algoritmy MeSH
- ambulantní monitorování přístrojové vybavení MeSH
- biometrie MeSH
- chorobopisy MeSH
- cloud computing * MeSH
- důvěrnost informací MeSH
- elektronické zdravotní záznamy MeSH
- internet MeSH
- lékařská informatika přístrojové vybavení MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- programovací jazyk MeSH
- sběr dat MeSH
- ukládání a vyhledávání informací metody MeSH
- zabezpečení počítačových systémů * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.
- MeSH
- algoritmy MeSH
- COVID-19 * MeSH
- internet věcí * MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
- MeSH
- Bayesova věta MeSH
- big data MeSH
- časná diagnóza MeSH
- internet věcí * MeSH
- Publikační typ
- časopisecké články MeSH
