Cloud Center Research Articles

Methods for Validating HRRR Simulated Cloud Properties for Different Weather Phenomena Using Satellite and Radar Observations

Abstract In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, Kentucky, tornado on 11 December, a heavy snow event in Minnesota from 10 to 11 December, and the Midwest derecho on 15 December. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely, the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the object-based threat score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers. Significance Statement The purpose of this study is to assess the accuracy of forecast cloud and radar objects, defined using simulated satellite brightness temperatures and radar reflectivity, from the High-Resolution Rapid Refresh (HRRR) model. This assessment was conducted for a tornado, snow, and derecho event from December 2021. Results from these three events indicate that the HRRR model accurately represents the observed displacement between the center of cloud and radar objects for the tornado and derecho events, and is the most accurate overall for the snow event.

Federated Learning Enabled Credit Priority Task Processing for Transportation Big Data

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.

Dynamic Optimization for Mobile Edge Computing: A Comparative Study

Edge computing EC is an advanced technology where a server at the edge resource is allocated at the network’s edge, closely to end users, mobile devices, sensors, and the developing Internet of Things (IoT). To date, a large number application using edge computing concept has developed and equipped in the market. However, “Edge” refer to continuum network resources as well as computing; through cloud center of data and the sources of data. The Edge is not limited to request service or contents; it is extended to perform tasks like computing from the cloud. One main reason to get well acceptance to the research community is; the edge is able to offload computing, store the data, processing and caching, request distribution and service delivery to the user from the cloud. In this paper, extensive experiments are conducted which leads to a comparison of several methods in order to solve two problems e.g. Power Allocation problem using Non-Cooperative Game model based on sub-Gradient NCGG and Nertia-Weighted Particle Swarm Optimization PSO methods. Similarly, Joint Request Offloading and Computing Resource Scheduling JRORS using MO-NSGA and Binary article Swarm Optimization BPSO will explored to check their effectiveness and efficiency to our day to day computing operation.

Intelligent Resource Allocation and Task Offloading Model for IoT Applications in Fog Networks: A Game-Theoretic Approach

Fog computing is an emerging paradigm that allows IoT devices and applications to process their data at the network’s edge. However, fog servers have limited computational resources compared to the cloud system; therefore, they cannot accommodate the ever-increasing computational demand from IoT devices. In such a challenging environment, IoT users’ decision to offload their tasks to the fog node or the central cloud server is affected by the environment’s current dynamics as other IoT users are also competing to maximize their resource utilization. In this paper, we propose a computational model that considers energy consumption and transmission latency as decision parameters for task offloading of IoT applications. Second, we model the competition as a game where IoT devices’ decision for the optimal distribution of tasks is captured in a joint optimization problem for energy consumption and latency. Third, we propose a decentralized task distribution algorithm where the players learn to update their strategy based on other players’ actions. We also prove that the solution of the proposed algorithm converges to Nash equilibrium (NE). Finally, we carry out extensive evaluations and compare our computational model and results with existing studies.

Blockchain Empowering Internet of Things: A Comprehensive Analysis of Applications and Security Challenges

An accessible, dependable, and secure infrastructure is necessary to manage the ever-increasing amount of processed data, which is being driven by the proliferation of Internet of Things (IoT) devices in society. There is a risk of single-point failures in the current IoT model because it depends on a central cloud server. One solution to this problem is the increasing popularity of combining Blockchain Technology (BCT) with the IoT. This takes advantage of the decentralized structure of Blockchain (BC) networks. IoT security has been greatly enhanced by this merger. A comprehensive analysis of Blockchain-based Internet of Things (BC-IoT) applications and the security risks they pose is provided in this study. It examines the IoT and BCT side by side, using well-structured and efficient scholarly literature to classify blockchain-based IoT applications in many different industries, such as manufacturing, healthcare, transportation, supply chain management, and ad hoc Vehicle Networks (VANET). In addition, the article discusses the shortcomings of BCT as it pertains to the IoT and the security concerns surrounding applications built on the blockchain. Academics and researchers can use the given insights to further develop BC applications based on the IoT.

Local Energy Market Design for Power- and Hydrogen-Based Microgrids Considering a Hybrid Uncertainty Controlling Approach

Despite the significant merits of plugin electric vehicles (PEVs) and fuel cell electric vehicles (FCEVs), their enormous rise has posed serious challenges in terms of energy supply. This research proposes a sustainable energy platform to integrate PEVs and FCEVs under the concept of microgrid (MG) with power and green hydrogen as energy carriers, so-called integrated power, and hydrogen MG (IPHMG). In addition, a decentralized market clearing model is developed to enable the nearby IPHMG to interact in a local energy market (LEM). The LEM is run by a central manager for both power and hydrogen. To do so, the mobile edge computing (MEC) system is proposed to move the computation from a central cloud server to decentralized edge servers, causing to reducing the computational burden and increasing data security and privacy. Moreover, the fast alternating direction method of multipliers (fast-ADMM) is employed to decompose the problem. Further, this work advances the state-of-the-art in uncertainty management by introducing a tri-level uncertainty controlling approach to fully consider the uncertain parameters. According to the obtained results, the community of IPHMGs could reduce 5.86% of total cost by trading energy in the LEM and the presented hybrid uncertainty management model could protect the community against uncertainties by reducing the total cost by 8.08%.

Power Aware Energy Efficient based Virtual Machine Migration Using Enhanced Pelican Remora Optimization in Cloud Center

Power Aware Energy Efficient based Virtual Machine Migration Using Enhanced Pelican Remora Optimization in Cloud Center

Design and Implementation of a Fog Computing Architecture for IoT Data Analytics

The number of Internet of Things (IoT) devices has exponentially increased, creating an explosion of data that requires sophisticated processing and analysis techniques. When it comes to meeting the demands of long duration and narrow band of the things' Internet applications, traditional cloud computing solutions may encounter difficulties. To overcome these issues, fog computing has developed into a workable concept for extending nube services all the way to the system's edge. The development of a fog computing architecture for the analysis of data from the internet of things is the topic of this study. Our system's three primary parts are fog nodes, edge devices, and a central cloud server. Sensors and edge devices of the Internet of Things (IoT) are in charge of local preprocessing and data collection. In between network edge devices and the cloud server, fog nodes act as intermediaries. Their actions have reduced the volume of raw data sent to the cloud for processing and archiving. One cloud server manages all aspects of data analysis, storage, and archiving. In order to show how effective and efficient our architecture is, Our approach was supported by data gathered from a variety of Internet-connected devices, and by lowering the amount of data transferred to the cloud, we were able to considerably lower lag and the use of black band. The network's core fog nodes also offered the processing capacity required to carry out analysis relatively instantly. The advantages of the board devices, given that a large number of Internet of Things applications require real-time or almost real-time data processing, this architecture stands out because to its capacity to lower latency, save bandwidth, and improve system efficiency.

A Brief Introduction to Infrastructure Planning for Next-Generation Smart Computing Data Centers

Globally, digital technology and the digital economy have propelled technological revolution and industrial change, and it has become one of the main grounds of international industrial competition. It was estimated that the scale of China’s digital economy would reach 50 trillion yuan in 2022, accounting for more than 40% of GDP, presenting great market potential and room for the growth of the digital economy. With the rapid development of the digital economy, the state attaches great importance to the construction of digital infrastructure and has introduced a series of policies to promote the systematic development and large-scale deployment of digital infrastructure. In 2022 the Chinese government planned to build 8 arithmetic hubs and 10 national data center clusters nationwide. To proactively address the future demand for AI across various scenarios, there is a need for a well-structured computing power infrastructure. The data center, serving as the pivotal hub for computing power, has evolved from the conventional cloud center to a more intelligent computing center, allowing for a diversified convergence of computing power supply. Besides, the data center accommodates a diverse array of arithmetic business forms from customers, reflecting the multi-industry developmental trend. The arithmetic service platform is consistently broadening its scope, with ongoing optimization and innovation in the design scheme of machine room processes. The widespread application of submerged phase-change liquid cooling technology and cold plate cooling technology introduces a series of new challenges to the construction of digital infrastructure. This paper delves into the design objectives, industry considerations, layout, and other dimensions of a smart computing center and proposes a new-generation data center solution that is “flexible, resilient, green, and low-carbon.”

Optimizing Cloud-Fog-Edge Job Scheduling Using Catastrophic Genetic Algorithm and Block Chain-Based Trust: A Collaborative Approach

Collaborative edge-cloud features improve job scheduling. Cloud job scheduling is crucial. Pending delay completion. A cloud-edge mixed system replaced centralized cloud computing. Combining resource levels reduces terminal user service call latency. Decentralization, regionalization, and node dispersal autonomy increase ambiguity, unreliability, and instability. This paper will plan cloud-migrating tasks on edge devices or the cloud to achieve a global optimum. The objective of this research is to enhance the efficiency of job scheduling in cloud-fog edge environments through the integration of the Catastrophic Genetic Algorithm (CGA), a genetic algorithm inspired by natural evolution. Additionally, Berger's theory will be employed to develop a trust-enabled interaction framework based on blockchain technology. The CGA fitness function incorporates load balancing and reasonability in the coordination of services and scheduling of tasks, with the goal of maximizing performance. This article presents proposed improvements to the CGA, which involve the incorporation of mutation and crossover operators, roulette selection, and cataclysm. These changes aim to expand the search area and potentially discover schedules that are more optimal. The approach also effectively deals with the problem of premature convergence, guaranteeing ample time for the algorithm to comprehensively explore the solution space prior to reaching a final solution. The experimental findings indicate that the strategy put forward in this study yields a substantial reduction in task completion time, surpassing 97%. Furthermore, it effectively addresses the best local problem, hence showcasing competing options.

A sensitive, high-resolution, wide-field IRAM NOEMA CO(1–0) survey of the very nearby spiral galaxy IC 342

We present a new wide-field 10.75 × 10.75 arcmin2 (≈11 × 11 kpc2), high-resolution (θ = 3.6″ ≈ 60 pc) NOEMA CO(1–0) survey of the very nearby (d = 3.45 Mpc) spiral galaxy IC 342. The survey spans out to about 1.5 effective radii and covers most of the region where molecular gas dominates the cold interstellar medium. We resolved the CO emission into > 600 individual giant molecular clouds and associations. We assessed their properties and found that overall the clouds show approximate virial balance, with typical virial parameters of αvir = 1 − 2. The typical surface density and line width of molecular gas increase from the inter-arm region to the arm and bar region, and they reach their highest values in the inner kiloparsec of the galaxy (median Σmol ≈ 80, 140, 160, and 1100 M⊙ pc−2, σCO ≈ 6.6, 7.6, 9.7, and 18.4 km s−1 for inter-arm, arm, bar, and center clouds, respectively). Clouds in the central part of the galaxy show an enhanced line width relative to their surface densities and evidence of additional sources of dynamical broadening. All of these results agree well with studies of clouds in more distant galaxies at a similar physical resolution. Leveraging our measurements to estimate the density and gravitational free-fall time at 90 pc resolution, averaged on 1.5 kpc hexagonal apertures, we estimate a typical star formation efficiency per free-fall time of 0.45% with a 16 − 84% variation of 0.33 − 0.71% among such 1.5 kpc regions. We speculate that bar-driven gas inflow could explain the large gas concentration in the central kiloparsec and the buildup of the massive nuclear star cluster. This wide-area CO map of the closest face-on massive spiral galaxy demonstrates the current mapping power of NOEMA and has many potential applications. The data and products are publicly available.

Chemo-dynamical tagging in the outskirts: The origins of stellar substructures in the Magellanic Clouds

We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds extending to 20° from the Large Magellanic Cloud (LMC) center. We also present, for the first time, the metallicity and α-abundance radial gradients of the LMC and the Small Magellanic Cloud (SMC) out to 11° and 6°, respectively. Our chemical tagging includes 13 species including light, α-, and Fe-peak elements. We find that the abundances of all of these chemical elements in stars populating two regions in the northern periphery, along the northern “stream-like” feature, show good agreement with the chemical patterns of the LMC, and thus likely have an LMC origin. For substructures located in the southern periphery of the LMC we find more complex chemical and kinematical signatures, indicative of a mix of LMC-like and SMC-like populations. The southern region closest to the LMC shows better agreement with the LMC, whereas that closest to the SMC shows a much better agreement with the SMC chemical pattern. When combining this information with 3D kinematical information for these stars, we conclude that the southern region closest to the LMC likely has an LMC origin, whereas that closest to the SMC has an SMC origin and the other two southern regions have a mix of LMC and SMC origins. Our results add to the evidence that the southern substructures of the LMC periphery are the product of close interactions between the LMC and SMC, and thus likely hold important clues that can constrain models of their detailed dynamical histories.

Edge-centric energy efficient LSTM with federated learning model based computation offloading for IoT applications

A more recent innovation to support cloud computing is edge computing that can address the deficiency of the existing centralised cloud computing paradigm and bring compute and storage resources closer to devices. Edge computing isn’t always the same as conventional cloud computing. Edge computing isn’t always the same as conventional cloud computing. It is a new computational model that performs computation at the network edge. Its crucial idea is to bring computing in the way of the information’s source. Task scheduling is the process allocating incoming requests (tasks) using a specific method to make the best use of the resources for the needed process. Users of services must submit their requests online because cloud computing is the technology used to deliver services through the internet. This paper aims to provide an Edge centric LSTM model with Federated learning for Data storage and model training that will take place on powerful edge servers in distributed ML methods. a massively distributed tasks requiring Processing on-site, coordinating the task remotely and execution are carried out in collaboration with number of multiple edge nodes and the distant cloud infrastructure. As a result, improved computation offloading and networking tradeoffs are possible to achieve low latency, large bandwidth take place on powerful edge servers.

JWST Reveals Widespread CO Ice and Gas Absorption in the Galactic Center Cloud G0.253+0.016

We report JWST NIRCam observations of G0.253+0.016, the molecular cloud in the Central Molecular Zone known as “The Brick,” with the F182M, F187N, F212N, F410M, F405N, and F466N filters. We catalog 56,146 stars detected in all six filters using the crowdsource package. Stars within and behind The Brick exhibit prodigious absorption in the F466N filter that is produced by a combination of CO ice and gas. In support of this conclusion, and as a general resource, we present models of CO gas and ice and CO2 ice in the F466N, F470N, and F410M filters. Both CO gas and ice contribute to the observed stellar colors. We show, however, that CO gas does not absorb the Pfβ and Huϵ lines in F466N, but that these lines show excess absorption, indicating that CO ice is present and contributes to observed F466N absorption. The most strongly absorbed stars in F466N are extincted by ∼2 mag, corresponding to >80% flux loss. This high observed absorption requires very high column densities of CO, and thus a total CO column that is in tension with standard CO abundance and/or gas-to-dust ratios. This result suggests the CO/H2 ratio and dust-to-gas ratio are greater in the Galactic Center than in the Galactic disk. Ice and/or gas absorption is observed even in the cloud outskirts, implying that additional caution is needed when interpreting stellar photometry in filters that overlap with ice bands throughout galactic centers.

A privacy-preserving federated learning with a secure collaborative for malware detection models using Internet of Things resources

The rise in malware attacks presents a growing security threat in today’s digital landscape, emphasizing the need for advanced malware detection methodology. While numerous detection systems utilizing methodologies like machine learning (ML) have been developed, they often fail to detect new and unknown malware in real time. This is because their malware datasets are generated with a very limited source and are not dynamic for up-to-date malware. Thus, with of the growth of the Internet of Things, malware have also increasingly grown, which makes current malware detection systems outdated in a short time. Therefore, IoT device resources can be exploited for building malware datasets having various types of malware families. In addition, the approach of collecting data from IoT devices and training them in a centralized cloud (CC) server is no longer acceptable due to this approach incurs significant security and privacy issues. This paper proposes PPFL-SC, an efficient privacy-preserving federated learning with a secure collaborative supporting verification. A group-oblivious signcryption cryptography is designed to be used as a privacy-preserving technique for federated learning. The CC server aggregates data without compromising individual data privacy. Moreover, the Stackelberg incentive model is designed as an incentive mechanism for encouraging IoT devices to collaborate through their heterogeneous dataset in a malware detection model. Thus, the proposed incentive mechanism is capable of increasing the participation of IoT devices and decreasing their dropping out during model updating. A security analysis confirms that PPFL-SC fulfills all security requirements for privacy-preserving federated learning. Real-world dataset experiments validate the efficiency and practicality of the proposed PPFL-SC.

Self-excited converging shock structure in a complex plasma medium.

We report the study of a self-excited converging shock structure observed in a complex plasma medium. A high-density dust cloud of melamine formaldehyde particles is created and horizontally confined by a circular ring in a dc glow discharge plasma at a particular discharge voltage and pressure. Later, as the discharge voltage is increased, a circular density crest is spontaneously generated around the outer boundary of the dust cloud. This nonlinear density structure is seen to propagate inward towards the center of the dust cloud. The properties of the excited structure are analyzed and found to follow the characteristics of a converging shock structure. A three-dimensional molecular dynamics simulation has also been performed in which a stable dust cloud is formed and levitated by the balance of forces due to gravity and an external electric field mimicking the cathode sheath electric field in the experiment. Particles are also horizontally confined by an external electric field, representing the sheath electric field of the circular ring present in the experiment. A circular shock structure has been excited by applying an external perturbation in the horizontal electric field around the outer boundary of the dust cloud. The characteristic properties of the shock are analyzed in the simulation and qualitatively compared with the experimental findings. This paper is not only of fundamental interest but has many implications concerning the study of converging shock waves excited in other media for various potential applications.

Real-time Health Monitoring and Speedy Personal Alert Support System using Medical IoT-Cloud Infrastructures

Health care monitoring technology is evolving day by day with the help of computational techniques and intelligent diagnosis models. Compared to conventional approaches and clinical observations, recent health care methodologies are heavily depending on more accurate computational platforms to diagnose various diseases. On the stream, computational principles and intelligent diagnosis model are proposed for monitoring and classifying the biosensor signal data. The existing diagnosis procedures that are using crucial computational models have been trained to observe the medical symptoms of particular disease. The contributions of recent medical support systems and computational models are closely made of Internet of Medical Things (IoMT) environment. However, the existing techniques are applied to obtain accurate decisions only rather than speedy response properties. Against the issue, the proposed Real-time Gated Recurrent Unit (GRU) based Health Monitoring (RGHM) system has been initiated to build more reliable, fast and accurate health assistance solutions. Most of the recent works are taking more complex data processing techniques that are producing higher delay and resources. The proposed RGHM works against these issues and attains effective results compared to current techniques such as Smart Artificial Intelligence based Monitoring Technique (SAIMT), Patient Monitoring with IoT model (PM-IoT) and Patient Centric Cloud Health Monitoring system (PCCHM). The performance of proposed RGHM is illustrated in result section that shows the RGHM has attained 11% to 14% of better performance than existing techniques.

Heart Disease Detection by Machine Learning System

Heart disease is a prevalent global health issue that impacts a substantial number of individuals worldwide. It is characterized by symptoms such as shortness of breath, muscle weakness, and swollen feet. However, the current diagnostic methods for heart disease have limitations in terms of accuracy and efficiency, making early detection challenging. Consequently, researchers are striving to develop an effective approach for early detection of heart disease. The lack of advanced medical equipment and qualified healthcare professionals further complicates the diagnosis and management of cardiac conditions., there have been approximately 26 million reported cases of heart disease, with an additional 3.6 million new cases identified annually. In the United States, a significant proportion of the population is affected by heart disease. Typically, doctors diagnose heart disease by considering the patient's medical history, conducting a physical examination, and assessing any concerning symptoms. However, this diagnostic method does not consistently provide accurate identification of individuals with heart disease. The importance of employing. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. “Patient 1,patient 2,patient 3 and patient 4.” Dyspnea can be described as a sensation of breathlessness and inadequate breathing, where one feels unable to take in enough air or breathe deeply. It involves the interplay of mechanoreceptors in the upper airways, lungs, and chest wall, along with peripheral receptors, chemoreceptors, and other sensory receptors. Edema refers to the accumulation of excessive fluid in the body tissues, leading to swelling. While edema can occur in any part of the body, it is more commonly observed in the lower extremities Ascites - The pathological buildup of fluid in the abdominal cavity is known as ascites. It is the most frequent cirrhosis consequence and happens in 50% of patients with decompensated cirrhosis within 10 years. Ascites formation marks the change from stressed to decompensated cirrhosis. Patent 1 is in rank 1 and patient 5 is ranked 5. In weighted table every value is equally split by 1,so that each value is equal. In the study, the researchers compared the sensitivity levels of two classifiers: the Relief FS method with a linear SVM classifier and the NB classifier with specific features from the LASSO FS algorithm. The findings revealed that the NB classifier, utilizing LASSO FS features, exhibited the highest performance in terms of sensitivity. Additionally, the Logistic Regression MCC classifier, employing the FCMIM FS method, achieved a classification accuracy of 91%.

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).