Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

• Klíčová slova
• CLIP, JTOS, SDN, dynamic environment, framework, task scheduling,
• MeSH
• cloud computing * MeSH
• internet věcí * MeSH
• poskytování zdravotní péče MeSH
• Publikační typ
• časopisecké články MeSH

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels.

• Klíčová slova
• GSM, IoT, complex systems, edge computing, energy, metaverse, smart grids, sustainability, triplexer,
• Publikační typ
• časopisecké články MeSH

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.

• Klíčová slova
• application in biomedicine, cloud computing use, infrastructure solution,
• MeSH
• biomedicínský výzkum * ekonomika   přístrojové vybavení   metody   MeSH
• cloud computing * MeSH
• lékařská informatika * ekonomika   přístrojové vybavení   metody   MeSH
• lékařství * metody   MeSH
• objevování léků metody   MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

• Klíčová slova
• Cardiovascular Disease, ECG sensors, fog computing, health monitoring system, internet of medical things, low-latency, scheduling algorithms, task scheduling,
• MeSH
• algoritmy * MeSH
• cloud computing * MeSH
• elektrokardiografie MeSH
• internet MeSH
• počítačová simulace MeSH
• Publikační typ
• časopisecké články MeSH

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

• Klíčová slova
• Edge computing, Firefly algorithm, Genetic operator, Quasi-reflection-based learning, Swarm intelligence, Workflow scheduling,
• Publikační typ
• časopisecké články MeSH

Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

• Klíčová slova
• IoT, edge computing, energy harvesting, geothermal energy, geothermal sensors,
• MeSH
• cloud computing MeSH
• geotermální energie * MeSH
• informační technologie MeSH
• internet věcí * MeSH
• technologie MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Here, we show results on X-ray absorption near edge structure spectroscopy in both transmission and X-ray fluorescence full-field mode (FF-XANES) at the calcium K-edge on human bone tissue in healthy and diseased conditions and for different tissue maturation stages. We observe that the dominating spectral differences originating from different tissue regions, which are well pronounced in the white line and postedge structures are associated with polarization effects. These polarization effects dominate the spectral variance and must be well understood and modeled before analyzing the very subtle spectral variations related to the bone tissue variations itself. However, these modulations in the fine structure of the spectra can potentially be of high interest to quantify orientations of the apatite crystals in highly structured tissue matrices such as bone. Due to the extremely short wavelengths of X-rays, FF-XANES overcomes the limited spatial resolution of other optical and spectroscopic techniques exploiting visible light. Since the field of view in FF-XANES is rather large the acquisition times for analyzing the same region are short compared to, for example, X-ray diffraction techniques. Our results on the angular absorption dependence were verified by both site-matched polarized Raman spectroscopy, which has been shown to be sensitive to the orientation of bone building blocks and by mathematical simulations of the angular absorbance dependence. As an outlook we further demonstrate the polarization based assessment of calcium-containing crystal orientation and specification of calcium in a beta-tricalcium phosphate (β-Ca3(PO4)2 scaffold implanted into ovine bone. Regarding the use of XANES to assess chemical properties of Ca in human bone tissue our data suggest that neither the anatomical site (tibia vs jaw) nor pathology (healthy vs necrotic jaw bone tissue) affected the averaged spectral shape of the XANES spectra.

• MeSH
• fluorescence MeSH
• fosforečnany vápenaté chemie   MeSH
• kortikální kost chemie   MeSH
• lidé MeSH
• minerály chemie   MeSH
• počítačová simulace MeSH
• Ramanova spektroskopie MeSH
• rentgenová absorpční spektroskopie * MeSH
• rentgenové záření MeSH
• vápník chemie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• beta-tricalcium phosphate MeSH Prohlížeč
• fosforečnany vápenaté MeSH
• minerály MeSH
• vápník 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

Molybdenum disulfide, in particular its edges, has attracted considerable attention as possible substitute for platinum catalysts in the hydrogen evolution reaction (HER). The complex nature of the reaction complicates its detailed experimental investigations, which are mostly indirect and sample dependent. Therefore, density functional theory calculations were employed to study how the properties of the MoS2 Mo-edge influence the thermodynamics of hydrogen adsorption onto the edge. The effect of the computational model (one-dimensional nanostripe), border symmetry imposed by its length, sulfur saturation of the edge, and dimensionality of the material are discussed. Hydrogen adsorption was found to depend critically on the coverage of extra sulfur at the Mo edge. The bare Mo-edge and fully sulfur-covered Mo-edge are catalytically inactive. The most favorable hydrogen binding towards HER was found for the Mo-edge covered by sulfur monomers. This edge provides hydrogen adsorption free energies positioned around -0.25 eV at up to 50 % hydrogen coverage, close to the experimental values of overpotential needed for the HER reaction.

• Klíčová slova
• ab initio calculations, adsorption, edge effects, two-dimensional materials,
• Publikační typ
• časopisecké články MeSH

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

• Klíčová slova
• Fault Tree Analysis, Methane Sensing, Random Forest, Sensing Edge Device, edge fault detection, sensor faults,
• Publikační typ
• časopisecké články MeSH