Cloud Integration Research Articles

A method for analyzing discontinuities characterization using the integration of underground drift point cloud with 3DEC software

The geometric statistical characteristics of rock mass discontinuities, such as mean trace length and intensity, cannot be measured directly and require the use of statistical tools (such as sampling lines, sampling windows, and drilling) for analysis. In this paper, we propose a method for extracting discontinuity characteristics using the 3DEC software in three-dimensional (3D) drift models (3DM). Specifically, we introduce the Surface Disk Discontinuity Model (SDDM) as a representation model for planar and linear discontinuities identified in point clouds. By importing the 3DM and SDDM into 3DEC and utilizing a multi-circular window, we calculate the mean trace lengths and two-dimensional (2D) intensity (P21). Additionally, we propose a method to estimate the three-dimensional (3D) intensity (P32), which provides a more comprehensive reflection of the discontinuous characteristics in different spatial orientations. To validate the proposed method, we apply it to the underground drift of the Jiapigou gold mine in Jilin Province to extract the geometric statistical characteristics of discontinuities. The results demonstrate that our method successfully captures the orientation, trace length, and P32 characteristics of four dominant discontinuity groups in the study area. This approach proves to be an effective technique for utilizing 3D laser scanning to obtain accurate discontinuity characteristics of rock mass.

Exploring Strategic Decision-Making Process In Hybrid Cloud Computing Adoption Among Small And Medium-Sized Enterprises

This study delves into the strategic decision-making process concerning the adoption of hybrid cloud computing among Small and Medium-Sized Enterprises (SMEs) within the context of Bahrain. With the proliferation of digital transformation and the evolving landscape of technological infrastructure, the adoption of hybrid cloud solutions presents a compelling avenue for SMEs to enhance operational efficiency and competitive advantage. This research employs a qualitative approach, engaging in-depth interviews with 11 key stakeholders from diverse SMEs across Bahrain. Through thematic analysis, this study elucidates the multifaceted dimensions of the strategic decision-making process, encompassing factors influencing adoption, perceived benefits, challenges encountered, and the integration of hybrid cloud computing within the broader organizational strategy. The findings shed light on the considerations, including technological, organizational, regulatory, and socio-cultural aspects, shaping the adoption landscape of hybrid cloud solutions among SMEs in Bahrain.

Exploration of Machine Learning Model for Diabetes Prediction

Diabetes is a metabolic disorder characterized by a malfunction in insulin release, resulting in a rise in blood sugar levels in the body. Diabetes diagnosis must be made on time and precisely in order to be effective and enhance patient outcomes. The diabetes management presents a formidable challenge in modern healthcare, demanding a combination of timely interventions, precise data analysis, and personalized medical services. Furthermore, there exists a growing demand for advanced predictive models that not only provide accurate forecasts but also offer transparency and interpretability. The study objectives are to develop an innovative machine learning model for data-driven diabetes prediction and medical services. To identify the machine learning model that best suits the proposed system, a literature review related to different methods used in diagnosing diabetes is conducted. The merits and demerits of each existing system were identified to devise a proposed model that seamlessly integrates data from various sources, including blood glucose levels and patient health information. Based on the review, the study suggests an innovative machine learning model that utilizes the Explainable Decision Tree Model, leveraging cloud computing to analyse diabetes patient data and make predictions. The integration of cloud computing allows for seamless data integration from various sources. This research project represents a significant step forward in personalized diabetes care, enabling patients to proactively manage their condition while providing healthcare professionals with a powerful tool for delivering tailored medical services.

Medical Sensors and their Integration in Wireless Body area Networking for Pervasive Healthcare Delivery

Wireless Body Area Networks (WBANs) integrate diverse sensors like heart rate monitors, electrocardiograms, pulse oximeters, respiration rate sensors, gyroscopes, and skin conductance sensors into a network for pervasive healthcare delivery. This interconnected system enables real-time monitoring and data fusion, ensuring accurate and comprehensive health assessments. Efficient communication protocols, cloud integration, and power management enhance the reliability and longevity of wearable devices. Security measures and user-friendly interfaces prioritize the protection of sensitive health data and ease of access. Machine learning algorithms contribute to predictive analytics for early detection of health issues. WBANs offer a holistic approach to personalized healthcare, allowing for continuous and remote monitoring of physiological parameters. The seamless integration of these sensors establishes a foundation for timely intervention and improved patient outcomes.

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

Digital twin applications in an archaeological site: A virtual reconstruction of the Pishan site, Zhejiang, China

A digital twin is a virtual counterpart of a physical object or system based on precise data collection. Although digital twin applications are gaining traction in the virtual reconstruction of built heritage, their relevance in archaeological sites remains limited, especially for those with only foundations. The Pishan (毘山) site in Huzhou, featuring the remains of a high-platform building and a large stilt-style architecture, represents the largest settlement site from the late Shang (商, ca. 1600 – 1046 BCE) and early Western Zhou (西周, 1046 – 771 BCE) dynasties in Zhejiang Province, China. At present, confronting contradictions among preservation, restoration, and reuse as a heritage park, the site leverages digital twin technologies to address two concerns: (i) reconstructing a 3D scene for further restoration and related studies and (ii) integrating multimedia to enhance visitors’ experiences and dissemination. Photogrammetry, unmanned aerial vehicle, and a mirrorless camera are employed to collect sky and ground dual graphic data and reconstruct the 3D model of the loess terrace. A panoramic roaming environment is created through panoramic photography. Geographic information system is integrated to enable visual analysis and information management while building information modeling facilitates the integration of parametric modeling and point cloud, aiding virtual restoration research. In conclusion, a workflow entitled “Virtual Reconstruction – Management and Analysis – Restoration – Exhibition” is proposed, promising in-depth exploration in further studies.

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

Intrusion Detection Systems Using Machine Learning.

The utilization of machine learning to enhance Intrusion Detection Systems (IDS). It encompasses an exploration of diverse IDS categories, fundamental evaluation metrics, and the dynamic landscape of machine learning methodologies. Recent trends underscore a shift towards the adoption of deep learning techniques for improving attack detection capabilities. Challenges arise from heightened model complexity and increased resource requirements. The paper also suggests future directions that encompass the development of updated datasets and the efficient management of resources through cloud integration. Throughout, this study emphasizes the continuous demand for research and innovation in the field of cybersecurity.

Integration of cloud, fog, and edge technologies for the optimization of high-load systems

The study is dedicated to analyzing methods and tools for optimizing the performance of high-load systems using cloud, fog, and edge technologies. The focus is on understanding the concept of high-load systems, identifying the main reasons for increased load on such systems, and studying the dependency of the load on the system's scalability, number of users, and volume of processed data. The introduction of these technologies implies the creation of a multi-level topological structure that facilitates the efficient operation of distributed corporate systems and computing networks. Modern approaches to load management are considered, the main factors affecting performance are investigated, and an optimization model is proposed that ensures a high level of system efficiency and resilience to peak loads while ensuring continuity and quality of service for end-users. The methodology is based on a comprehensive approach, including the analysis of existing problems and the proposal of innovative solutions for optimization, the application of architectural solutions based on IoT, cloud, fog, and edge computing to improve performance and reduce delays in high-load systems. The scientific novelty of this work lies in the development of a unique multi-level topological structure capable of integrating cloud, fog, and edge computing to optimize high-load systems. This structure allows for improved performance, reduced delays, and effective system scaling while addressing the challenges of managing large data volumes and servicing multiple requests simultaneously. The conclusions of the study highlight the significant potential of IoT technology in improving production processes, demonstrating how the integration of modern technological solutions can contribute to increased productivity, product quality, and risk management.

Integration of Cloud Based Scheme with Industrial Wireless Sensor Network for Data Publishing in Privacy of Point Source

Integration of Cloud Based Scheme with Industrial Wireless Sensor Network for Data Publishing in Privacy of Point Source

Cloud Integration of Industrial IoT Systems. Architecture, Security Aspects and Sample Implementations

Cloud Integration of Industrial IoT Systems. Architecture, Security Aspects and Sample Implementations

Hybrid Cloud Computing: An In-Depth Analysis of Integration Strategies, Characteristics, and Prospective Future Applications

Hybrid cloud computing plays an important role of developing landscape of IT infrastructure. It integrates the control and security inherent in private clouds with the scalability and resource availability characteristic of public clouds. This paper provides a thorough exploration of the characteristics involved in combining these cloud models and highlights essential aspects such as flexibility, scalability, cost savings, and security. Based on a thorough literature review along with case studies, the study explores how current technological advancements, including containerization, and artificial intelligence, are influencing the development of hybrid cloud infrastructures. The discussions show that effective integration strategies not only increase operational efficiency but also introduce substantial benefits to diverse fields including finance, healthcare, and education. This article provides a comprehensive understanding of hybrid cloud and builds up a foundation and direction for future studies focused on optimizing cloud integration and application.

Strategies for Multimodal Image Data Transformation to a Common Format for Cloud Integration and Visualization

Strategies for Multimodal Image Data Transformation to a Common Format for Cloud Integration and Visualization

Integration of cloud technologies into virtual reality

Investigated the possibilities of integrating cloud technologies into virtual reality in order to further improve software tools and increase its efficiency and accessibility. Analysed architectural and infrastructure solutions for the use of cloud services, processing and storage of large data in virtual environments. Conducted a comprehensive scientific study of the possibilities of integrating cloud technologies into virtual reality in order to increase the efficiency and accessibility of this technological segment. Studied the challenges that arise when integrating cloud technologies, in particular, the problems of delays in data transmission and measures to ensure security and privacy in cloud environments. Considered the necessity of developing a cloud infrastructure in popular cloud services, in particular, Amazon Web Services (AWS), to optimise computing tasks and increase overall efficiency, as well as the possibility of scaling virtual reality technology. Article considers the possibility of creating a solution for interacting with cloud resources and using virtual reality applications. The services of a cloud provider are used to allocate computing resources, create a data warehouse, create and manage a virtual network, install and configure solutions for deploying and configuring server software components, and conduct an experiment. Evaluated the impact of cloud technology integration on the quality and availability of virtual reality, and provide prospects for the development of this association. In addition, considered the possibilities of creating optimised solutions for interacting with cloud resources and using virtual reality applications, and assessed the impact of cloud integration on the quality and availability of virtual reality. Developed a software solution and approach for building a cloud infrastructure, described in detail all the steps of creating and deploying a virtual server and data storage for its interaction with virtual reality systems. Prospects for the development of such an association open up wide horizons for innovative technologies in the information technology segment. The results of the study can become an important source of relevant strategic information for further improvement of cloud-virtual environments and development of innovative technologies in the information technology segment.

Physically Based Rendering of Animated Point Clouds for EXtended Reality

Abstract Point cloud 3D models are gaining increasing popularity due to the proliferation of scanning systems in various fields, including autonomous vehicles and robotics. When employed for rendering purposes, point clouds are typically depicted with their original colors acquired during the acquisition, often without taking into account the lighting conditions of the scene in which the model is situated. This can result in a lack of realism in numerous contexts, especially when dealing with animated point clouds used in eXtended reality applications, where it is desirable for the model to respond to incoming light and seamlessly blend with the surrounding environment. This paper proposes the application of physically based rendering (PBR), a rendering technique widely used in real-time computer graphics applications, to animated point cloud models for reproducing specular reflections, and achieving a photo-realistic and physically accurate look under any lighting condition. To achieve this, we first explore the extension of commonly used animated point cloud formats to incorporate normal vectors and PBR parameters, like roughness and metalness. Additionally, the encoding of the animated environment maps necessary for the PBR technique is investigated. Then, an animated point cloud model is rendered with a shader implementing the proposed PBR method. Finally, we compare the outcomes of this PBR pipeline with traditional renderings of the same point cloud produced using commonly used shaders, taking into account different lighting conditions and environments. Through these comparisons, we demonstrate how the proposed PBR method enhances the visual integration of the point cloud with its surroundings. Furthermore, it will be shown that using this rendering technique, it is possible to render different materials, by exploiting the features of PBR and the encoding of the surrounding environment.

Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence

Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades. Simultaneously, given the optimized energy consumption in modern cloud data centers, this transition is expected to reduce the environmental footprint of radiologic operations. The path to cloud integration comes with its own challenges, and radiology informatics leaders must consider elements such as cloud architectural choices, pricing, data security, uptime service agreements, user training and support, and broader interoperability. With the increasing importance of data-driven tools in radiology, understanding and navigating the cloud landscape will be essential for the future of radiology and its various stakeholders.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field

Traditional methods for 3D reconstruction of unmanned aerial vehicle (UAV) images often rely on classical multi-view 3D reconstruction techniques. This classical approach involves a sequential process encompassing feature extraction, matching, depth fusion, point cloud integration, and mesh creation. However, these steps, particularly those that feature extraction and matching, are intricate and time-consuming. Furthermore, as the number of steps increases, a corresponding amplification of cumulative error occurs, leading to its continual augmentation. Additionally, these methods typically utilize explicit representation, which can result in issues such as model discontinuity and missing data during the reconstruction process. To effectively address the challenges associated with heightened temporal expenditures, the absence of key elements, and the fragmented models inherent in three-dimensional reconstruction using Unmanned Aerial Vehicle (UAV) imagery, an alternative approach is introduced—the neural radiance field. This novel method leverages neural networks to intricately fit spatial information within the scene, thereby streamlining the reconstruction steps and rectifying model deficiencies. The neural radiance field method employs a fully connected neural network to meticulously model object surfaces and directly generate the 3D object model. This methodology simplifies the intricacies found in conventional 3D reconstruction processes. Implicitly encapsulating scene characteristics, the neural radiance field allows for iterative refinement of neural network parameters via the utilization of volume rendering techniques. Experimental results substantiate the efficacy of this approach, demonstrating its ability to complete scene reconstruction within a mere 5 min timeframe, thereby reducing reconstruction time by 90% while markedly enhancing reconstruction quality.