External Cloud Research Articles

An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI

Abstract. The retrieval of cloud parameters from the atmospheric Sentinel missions requires Earth reflectance measurements from a set of spectral bands. The ground pixels of the involved spectral bands should be fully aligned, but when they are not, special treatment is required within the operational algorithms. This so-called inter-band spatial misregistration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV(ultraviolet)–VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from OCRA (Optical Cloud Recognition Algorithm). In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a coregistration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV–VIS to NIR and vice versa is applied. In this paper we present a new scheme for the coregistration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. The new coregistration scheme based on the VIIRS data improves the TROPOMI cloud product quality and allows the addition of cloud information for the first (westernmost) TROPOMI UVIS ground pixel. In practice, the latter means that a significant number of valid data points are included in the TROPOMI cloud, total ozone, SO2 and HCHO product since 26 November 2023 (orbit 31705), when the new coregistration scheme became operational. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track direction, we found that the old coregistration scheme tends to smooth out cloud structures along the scan line, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial coregistration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation – Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite – Second Generation) missions. In the case of the Sentinel-4 instrument, the external cloud information will originate from collocated data captured by the FCI (Flexible Combined Imager) on board the MTG-I (Meteosat Third Generation – Imager) satellite.

Half a step behind – bulk disclosure of confidential data in third-party GenAI solutions under the Swedish Public Access to Information and Secrecy Act

Technological progress poses unique challenges for the public sector. New technology should be adopted, but it must always be done within the framework of good administration. It follows laws governing public administration must be continuously adapted. Sweden recently amended its secrecy legislation to facilitate the use of third-party cloud solutions by public authorities. When the amendment was enacted, most public sector organisations had already been using external cloud solutions for a long time. Today, there is as much pressure on authorities to implement AI technology as there ever was to move administration into the cloud. This paper uses traditional legal methodology to investigate if the Swedish secrecy legislation adequately enables the use of cloud-based GenAI solutions. Findings indicate that the recent amendment is likely insufficient and that there are significant practical hurdles for the application of the law, particularly with services from global cloud providers. The paper contributes to the understanding of Swedish law, and of the difficulties that can occur anywhere when policy makers and legislators do not move in tandem.

Parsec-scale evolution of the gigahertz-peaked spectrum quasar PKS 0858 − 279

ABSTRACT We conducted multi-epoch, multifrequency parsec-scale studies on the gigahertz-peaked spectrum quasar PKS 0858 − 279 with the Very Long Baseline Array (VLBA). Our observations on 2005 November 26 elucidated a weak core, characterized by an inverted spectrum, and a distinctly bent jet that exhibited a notable bright feature in its Stokes I emission. Through comprehensive analysis of polarization and spectral data, we inferred the formation of a shock wave within this feature, stemming from interactions with a dense cloud in the ambient medium. In this paper, Very Long Baseline Interferometry-Gaia astrometry further reinforces the core identification. With a deep analysis of six additional VLBA epochs spanning from 2007 to 2018, we observed that while the quasar’s parsec-scale structure remained largely consistent, there were discernible flux density changes. These variations strongly imply the recurrent ejection of plasma into the jet. Complementing our VLBA data, RATAN-600 observations of the integrated spectra suggested an interaction between standing and travelling shock waves in 2005. Moreover, our multi-epoch polarization analysis revealed a drastic drop in rotation measure values from 6000 to 1000 rad m−2 within a single year, attributable to diminishing magnetic fields and particle density in an external cloud. This change is likely instigated by a shock in the cloud, triggered by the cloud’s interaction with the jet, subsequently prompting its expansion. Notably, we also observed a significant change in the magnetic field direction of the jet, from being perpendicular post its observed bend to being perpendicular prior to the bend – an alteration possibly induced by the dynamics of shock waves.

Securing healthcare data in industrial cyber-physical systems using combining deep learning and blockchain technology

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.

Blind single-image-based thin cloud removal using a cloud perception integrated fast Fourier convolutional network

Remote sensing images are frequently contaminated by clouds that often degrade the performance of subsequent applications. Cloud removal, therefore, is a standard step in remote sensing image preprocessing, and single-image-based thin cloud removal is a well-established area of research. Existing single-image-based thin cloud removal methods however, lack the capacity for simultaneous executions of efficient long-range modeling and physical attribute consideration. To extend this work and fill this gap, a novel blind single-image-based thin cloud removal method, called cloud perception integrated fast Fourier convolutional network (CP-FFCN), was designed and implemented. The CP-FFCN consists of two modules: the cloud perception module (CPM) and a fast Fourier convolution (FFC)-conducted reconstruction module (FFCN). The CPM uses a frequency spatial attention mechanism to realize long-range modeling of clouds, globally detect them in the cloudy image. It helps the CP-FFCN remove the clouds without external prior knowledge of the cloud distribution. The reconstruction module was designed with an FFC-conducted U-Net architecture to recover the clean images from cloudy scenarios, guided by the locations of clouds as detected by the CPM. In addition, the FFC blocks deployed in the encoder and decoder components in the U-Net architecture selectively learn the attributes of clouds and fogs from the frequency spectrograms to remove the clouds and reconstruct the underlying ground objects. The CP-FFCN selectively learns the frequency features for adequate cloud separation and at the same time efficiently models the long-range information for comprehensive scenario reconstruction with the help of these two modules. We adopted the Google Earth data and Landsat-8 imagery to train the CP-FFCN model and evaluate it on simulated and naturally occurring cloudy scenarios. The visual outcomes illustrate that the proposed CP-FFCN successfully removes thin and small-scale thick clouds with complex ground object scenarios, without external cloud masks and additional reference data. The quantitative analyses further demonstrate the higher effectiveness of the CP-FFCN when compared with several other state-of-the-art thin cloud removal methods, yielding a PSNR value over 39.24 and a SSIM value over 0.98 on the Landsat 8 images.

An Application to Improve the Performance and Security in Cloud by File Division Technique

Cloud computing has transformed the modern business landscape by providing scalable, flexible, and cost-effective solutions for data storage and processing. However, relying on external cloud providers to manage and protect sensitive data has raised significant security concerns, namely the risk of data breaches. To address these issues and optimize system performance concurrently, this research project introduces a novel approach that combines data fragmentation and replication in the cloud environment. Data fragmentation involves breaking down large files into smaller fragments or pieces, each of which contains only a portion of the original data. The fragments are then dispersed across multiple cloud nodes to ensure that no single node stores an entire file. This fragmentation and replication strategy reduces the potential risk of data exposure during a security breach. Data replication across multiple nodes improves system performance in addition to providing security benefits. Replicating data fragments facilitates more efficient data distribution and retrieval, thereby reducing response times and enhancing overall system performance. This paper provides a comprehensive overview of the methodology employed in this approach, highlighting its fundamental concepts, potential benefits, and key security considerations, such as the importance of data isolation and encryption. By combining data fragmentation and replication, organizations can strengthen their cloud data security while improving system performance, providing a comprehensive answer to the challenges posed by cloud computing.

A Federated Blockchain Architecture for File Storage with Improved Latency and Reliability in IoT DApp Services.

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

Dual ingress architecture design pattern for Kubernetes applications

Purpose: The article focuses on the analysis of the mechanism for exposing Services running on a Kubernetes cluster using an Ingress type definition. It discusses the basics of this mechanism, pointing out its fundamental limitation of being able to use only single technology simultaneously in handling traffic to a web application. The paper presents an architectural pattern that enables the simultaneous integration of two Ingress definitions, combining the advantages of both systems used. Design/methodology/approach: Available solutions for exposing applications served in the Kubernetes cluster were analyzed. As a result of the research, an enhancement was proposed to allow the use of two services simultaneously, providing broader system functionality. Findings: An approach was proposed to use two Ingress controllers simultaneously in the form of an external cloud service and an internal Nginx service running on a Kubernetes cluster. Originality/value: A design pattern is presented along with an example implementation of dual Ingress on an AKS cluster in Azure. Keywords: Kubernetes, Ingress, architectural pattern, limitations, Azure, Application Gateway, Nginx. Category of the paper: Research paper, Technical paper.

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

Abstract. Jet engine power loss due to ice particle accumulation is a recognized aviation hazard occurring in cloud conditions difficult to forecast or visually recognize. High-altitude cirrus clouds can have ice particle concentrations high enough to be dangerous; therefore, pilots must be informed when aircraft enter such environments. One approach to determining ice particle concentration is an onboard lidar system. Concurrent lidar measurements are compared to backscatter coefficients derived from particle size distributions obtained from wing-mounted, in situ probes during four case studies consisting of sixty-second flight segments at different temperatures: +7 and +4 ∘C for water droplet analysis, and −33 and −46 ∘C for ice particle analysis. Backscatter coefficients derived from external cloud probes (ECP) are correlated (0.91) with measurements by an airborne lidar system known as the Optical Ice Detector (OID). Differences between OID and ECP backscatter coefficients range from less than 1 to over 3 standard deviations in terms of uncertainties. The backscatter coefficients are mostly in agreement for liquid clouds and are in disagreement for the −33 and −46 ∘C cases, with ECP-derived backscatter coefficients lower than the OID for three out of the four cases. Measurements over four 60 s research flight segments show that measured total water content is correlated (0.74) with the OID backscatter coefficient, which indicates that the OID is a useful instrument for determining ice particle concentrations over a broad range of environments, including at ice water contents as low as 0.02 g m−3. Additionally, concurrent measurements from cloud imaging probes and the OID provide improved knowledge of cloud conditions, which may help in understanding cloud processes.

A Remote Monitoring and Maintenance System for Industrial Robots

Technologies such as external sensors, data acquisition, trend analysis, and cloud services make remote operation and maintenance of equipment status monitoring more convenient and lower maintenance costs. In this paper, an industrial robot operation and maintenance system is designed. First, the online monitoring of the robot is realized by collecting the control parameters, state parameters, fault information and external sensor monitoring information of the industrial robot. Then, the health baseline of the industrial robot is established based on the historical operation data. The online data is compared with the baseline data to realize the online assessment of the robot’s health status. Finally, a visualization platform for remote operation and maintenance of the robot is built to realize the online display of parameters, health trend tracking and fault status early warning, which is convenient for maintenance personnel to take timely countermeasures.

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

3D cloud envelope and cloud development velocity from simulated CLOUD (C3IEL) stereo images

Abstract. A method to derive the 3D cloud envelope and the cloud development velocity from high spatial and temporal resolution satellite imagery is presented. The CLOUD instrument of the recently proposed C3IEL mission lends itself well to observing at high spatial and temporal resolutions the development of convective cells. Space-borne visible cameras simultaneously image, under multiple view angles, the same surface domain every 20 s over a time interval of 200 s. In this paper, we present a method for retrieving cloud development velocity from simulated multi-angular, high-resolution top of the atmosphere (TOA) radiance cloud fields. The latter are obtained via the image renderer Mitsuba for a cumulus case generated via the atmospheric research model SAM and via the radiative transfer model 3DMCPOL, coupled with the outputs of an orbit, attitude, and camera simulator for a deep convective cloud case generated via the atmospheric research model Meso-NH. Matching cloud features are found between simulations via block matching. Image coordinates of tie points are mapped to spatial coordinates via 3D stereo reconstruction of the external cloud envelope for each acquisition. The accuracy of the retrieval of cloud topography is quantified in terms of RMSE and bias that are, respectively, less than 25 and 5 m for the horizontal components and less than 40 and 25 m for the vertical components. The inter-acquisition 3D velocity is then derived for each pair of tie points separated by 20 s. An independent method based on minimising the RMSE for a continuous horizontal shift of the cloud top, issued from the atmospheric research model, allows for the obtainment of a ground estimate of the velocity from two consecutive acquisitions. The mean values of the distributions of the stereo and ground velocities exhibit small biases. The width of the distributions is significantly different, with higher a distribution width for the stereo-retrieved velocity. An alternative way to derive an average velocity over 200 s, which relies on tracking clusters of points via image feature matching over several acquisitions, was also implemented and tested. For each cluster of points, mean stereo and ground positions were derived every 20 s over 200 s. The mean stereo and ground velocities, obtained as the slope of the line of best fit to the mean positions, are in good agreement.

Distributed denial of service attack detection in E-government cloud via data clustering

One of the main essential security issues of cloud computing is the detection and prevention of network intrusions. The gaps in the network directly affect the security of the cloud as it is the foundation of it. Attacks in the cloud are launched either by compromised nodes of the network outside of the cloud or by virtual machines (VMs) within the cloud network. So, monitoring both external and internal traffic of the cloud network is of great importance. In this paper, a machine learning method performing accurate clustering of network data to detect DDoS attacks has been proposed. The method uses a feature selection technique to increase the efficiency of data clustering. To provide the feature selection the PCA algorithm has been used. For the dataset formed on selected features, the DBSCAN (density-based spatial clustering of applications with noise), Agglomerative Clustering, and k-means algorithms are applied. In the experiment, the clustering results of the methods using fewer features were higher on all metrics than the clustering results of the methods using all the features. Сompared to the standard algorithms, the PCA + DBSCAN, PCA + Agglomerative, and PCA + k-means algorithms obtained higher values on the Adjusted Rand Index metric and reached 0.8989, 0.9130, 0.9094 values, respectively. The effectiveness of the approach also was evaluated on the other clustering metrics and obtained high results. The proposed system can be installed in both internal and external cloud infrastructure. This allows, to detect attacks on the external cloud network, as well as on the internal physical network or in the virtual network between hypervisors. DDoS attacks detection system.

DOLL

Most large-scale ML implementations scale to large amounts of data by utilizing multiple servers or virtual machines (VMs) that iteratively compute model updates on local data that are periodically synchronized. Due to the complexity of managing the resulting computing infrastructure, many companies run their ML jobs on external cloud providers' servers. However, cloud resources can be expensive, particularly for large ML jobs with long runtimes. A particularly popular method to limit the costs of training ML jobs is to utilize preemptible cloud instances. These may be interrupted at the cloud provider's discretion, but they are significantly (up to 90%) cheaper than conventional on-demand instances. Most studies of these ML methods, however, assume the availability of large datasets at training time. In practice, training data may arrive at irregular intervals and models may be trained online as new data samples arrive, e.g., when monitoring data from IoT sensors. While some software frameworks like Apache Kafka can feed online data arrivals to ML algorithms, they provide little insight into the resulting costs of ML training. We extend prior work on provisioning preemptible instances to analyze available pools of data in order to run online ML on incoming datastreams, which presents new challenges due to the need to carefully handle data arrivals. We design, analyze, and optimize DOLL, which to the best of our knowledge is the first system that provides provable performance guarantees for Distributed OnLine Learning over preemptible instances. Research Challenges and Our Contributions: When pools of data are readily available, the bottleneck to distributed ML training often lies in the time required for each VM to compute its model updates. In our scenario, however, the arrival rate of incoming data may also bottleneck data processing. An intuitive strategy would then be for each VM to process each data point as it arrives. However, since arrivals at different VMs may not be coordinated, synchronizing the model parameters at each VM between data arrivals may introduce additional delays, while asynchronous SGD methods can lead to slow convergence [1]. DOLL uses a batching and grouping process to limit the synchronization delay, which naturally realizes traditional mini-batch SGD so as to provide provable model convergence guarantees. Handling online data arrivals becomes particularly challenging when we use preemptible instances to compute model updates. Existing methods utilizing preemptible instances for ML jobs largely focus on mitigating training interruptions [2] and their effects on model convergence [3]. When used on datastreams, we face an additional challenge of interruptions pausing the data arrival process, which impedes the rate at which we can compute model updates and thus model convergence. Thus, one should ensure that preemptions do not happen "too often," e.g., by computing some updates on on-demand instances. Our work is the first to optimize the number of preemptible VMs used and demonstrate that we can meet ML convergence guarantees.

Structure-Aware Denoising for Real-world Noisy Point Clouds with Complex Structures

Point cloud denoising is a crucial and fundamental step in geometry processing, which has achieved significant progress in the last two decades. Denoising real-world noisy point clouds is a very challenging problem since it is hard to describe the complex real-world noise by simple distributions such as Gaussian distribution. Furthermore, existing methods may suffer from performance degradation when dealing with real-world noisy point clouds with complex structures, which contain not only sharp features (sharp edges, sharp corners, etc.) but also smooth features, fine features, etc. To solve the above-mentioned problems, we propose a novel structure-aware denoising approach by exploiting the prior information in both external clean point clouds and the given noisy point cloud. We first group nonlocal self-similarity (NSS) patches from a set of external clean point clouds. Then, we employ the Gaussian Mixture Model (GMM) learning algorithm to learn external NSS priors over patch groups. Next, the internal priors are learned from the given noisy point cloud in the same way to refine the prior model. We integrate both the learned external and internal priors into a set of orthogonal dictionaries to efficiently estimate point normals. Finally, we propose a feature-aware point updating method through adaptive neighborhood selection to reposition points to match the estimated normals. Extensive experiments show that our approach achieves favorable comprehensive performance compared with many popular or state-of-the-art methods in terms of both objective and visual perception. The source code can be found at https://zhiyongsu.github.io.

Design of IT Infrastructure Multicloud Management Platform Based on Hybrid Cloud

IT infrastructure has multiple master servers wherein its external and internal data fail to be controlled at the same time, resulting in low management efficiency of IT infrastructure cloudy data. Also, the platform information access security management becomes extremely difficult. Therefore, a multicloud management platform for IT infrastructure based on hybrid cloud is designed. The overall architecture and cloud structure design of the cloud management platform is implemented using two master servers, control platform internal and external IT infrastructure cloud data at the same time. The framework also ensures desensitized access to IT infrastructure resources. Also, a cloud data load balancing adaptation process and IT infrastructure cloud management process are designed. The fuzzy information feature extraction method is used to extract the association information of IT infrastructure cloud management and reconstruction of the association rules of frequent item sets of IT infrastructure information. The experimental results show that the cloud link utilization is about 90%, the cloud management delay is lower than 1.1 ms, and the cloud management error is less than 2% in 400 iterations. Also, the accuracy of IT infrastructure packet forwarding is higher than that of the state-of-the art frameworks.

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

An IoT System for Real-Time Monitoring of DC Motor Overload

The excavators are heavy machines widely used in the civil engineering and surface mining industry. Recent studies show that 95% of contractors face the problem of finding skilled operators. Unskilled operators not only worsen productivity but also very often cause machine failures through unprofessional handling. Motivated by these studies and guided by the mining company’s requirements, we present a prototype of an IoT system for monitoring DC motor overload on the EKG-15 excavator. The IoT system consists of a microprocessor device mounted inside the excavator and an external cloud platform that can be accessed via the Internet. The proposed solution detects and warns the operator when the DC motor overload occurs, thus reducing the probability of its damage. In addition, overload data is sent to the cloud platform for later research, analysis and processing. The main benefit of the proposed solution is that it can be applied to existing industry machinery, thus reducing the maintenance cost and increasing productivity. After several months of use of the proposed system in real working conditions, it has been shown that the overload occurrence and its duration time are approximately reduced by 60% and 80%, respectively.

Development of crypto-code constructs based on LDPC codes

The results of developing post-quantum algorithms of McEliece and Niederreiter crypto-code constructs based on LDPC (Low-Density Parity-Check) codes are presented. With the rapid growth of computing capabilities of mobile technologies and the creation of wireless mesh and sensor networks, Internet of Things technologies, and smart technologies on their basis, information security is becoming an urgent problem. At the same time, there is a need to consider security in two circuits, internal (directly within the network infrastructure) and external (cloud technologies). In such conditions, it is necessary to integrate threats to both the internal and external security circuits. This allows you to take into account not only the hybridity and synergy of modern targeted threats, but also the level of significance (degree of secrecy) of information flows and information circulating in both the internal and external security circuits. The concept of building security based on two circuits is proposed. To ensure the security of wireless mobile channels, it is proposed to use McEliece and Niederreiter crypto-code constructs based on LDPC codes, which allows integration into the credibility technology of IEEE 802.15.4, IEEE 802.16 standards. This approach provides the required level of security services (confidentiality, integrity, authenticity) in a full-scale quantum computer. Practical security technologies based on the proposed crypto-code constructs, online IP telephony and the Smart Home system based on the use of an internal server are considered