Centralized Data Storage Research Articles

Political or Religious Policy? Indigenous Christian Education in the Dutch East Indies Era

This study is a textual analysis discussing the existence of Christian educational institutions for indigenous students under the Dutch colonial administration in the Dutch East Indies. The primary objective of this research is to examine the underlying motives behind the Dutch colonial government’s policies on Christian education for indigenous communities, specifically to determine whether these policies were politically or religiously motivated. Focusing on historical contexts, this research employs historical methods to reconstruct past events. The central question addressed is the Dutch colonial government’s main focus in shaping policies for Christian education through mission institutions for indigenous communities: whether these were driven by political interests or religious considerations. Data sources were obtained from literature and historical documents archived in data storage centers and libraries. The findings, using a structuration methodology, reveal a pragmatic dualism underlying the colonial government’s policies, which shifted according to the socio-political context.

Federated Learning's Dynamic Defense Against Byzantine Attacks: Integrating SIFT-Wavelet and Differential Privacy for Byzantine Grade Levels Detection

Federated learning, which enables decentralized training across multiple devices while maintaining data privacy, is susceptible to Byzantine poisoning attacks. This paradigm reduces the need for centralized data storage and transmission, thereby mitigating privacy risks associated with traditional data aggregation methods. However, FL introduces new challenges, notably susceptibility to Byzantine poisoning attacks, where rogue participants can tamper with model updates, threatening the consistency and security of the aggregated model. Our approach addresses this vulnerability by implementing robust aggregation methods, sophisticated pre-processing techniques, and a novel Byzantine grade-level detection mechanism. We introduce a federated aggregation operator designed to mitigate the impact of malicious clients. Our pre-processing includes data loading and transformation, data augmentation, and feature extraction using SIFT and wavelet transforms. Additionally, we employ differential privacy and model compression to improve the robustness and performance of the federated learning framework. Our approach is assessed using a tailored neural network model applied to the MNIST dataset, achieving 97% accuracy in detecting Byzantine attacks. Our results demonstrate that robust aggregation significantly improves the resilience and performance. This comprehensive approach ensures the integrity of the federated learning process, effectively filtering out adversarial influences and sustaining high accuracy even when faced with adversarial Byzantine clients.

Leveraging blockchain and federated learning in Edge-Fog-Cloud computing environments for intelligent decision-making with ECG data in IoT

Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated with centralized data storage. This paper presents a blockchain and FL-based Smart Decision Making framework for ECG data in microservice-based IoT medical applications. Leveraging edge/fog computing for real-time critical applications, the framework implements a FL model across edge, fog, and cloud layers. Evaluation criteria including energy consumption, latency, execution time, cost, and network usage show that edge-based deployment outperforms fog and cloud, with significant advantages in energy consumption (0.1% vs. Fog, 0.9% vs. Cloud), network usage (1.1% vs. Fog, 31% vs. Cloud), cost (3% vs. Fog, 20% vs. Cloud), execution time (16% vs. Fog, 28% vs. Cloud), and latency (1% vs. Fog, 79% vs. Cloud).

Evaluation of a New-Concept Secure File Server Solution

Ransomware attacks are becoming increasingly sophisticated, and defensive solutions must necessarily evolve. Unfortunately, automated backup management through validation—critical for data recovery after an attack—and strengthening file server protection are not sufficiently addressed in current protection strategies. To focus on this area, an architectural model has been developed that complements traditional central data storage with an additional high-level file server protection solution. However, in addition to the functional adequacy of file server protection solutions, the measurability of operational performance is also important. In order to validate and evaluate the developed ransomware-proof file server protection, a dynamic performance metric is introduced for comparability, by which the performance results measured on the tested client–server architectures are presented, together with the methodology and results of the measurements. Our results show that the investigated model does not cause any performance degradation when moving sensitive data files and their backups during operation and even shows performance improvements in some frequently used configurations. The result proves that the developed real-time approach provides a solution to this critical problem in terms of the time required to restore key data from backups and to ensure file availability and continuity of accessibility. Based on a review of the literature and available solutions, it is concluded that there is no integrated solution for implementing a similar concept in practice, and therefore, the developed model is a gap-filling in this priority area.

An edge computing and distributed ledger technology architecture for secure and efficient transportation

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture’s effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization.

A Survey on IoT Application Architectures.

The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems' deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management.

Evaluating the Event Industry for a Web-Based Event Supplier Management System

Managing a diverse range of suppliers can often become a logistical challenge in the dynamic world of event planning. The multitude of event suppliers frequently leads to miscommunication, delays, and exceeding budgets. Motivated by the necessity for a more efficient solution, the study intends to propose a web-based event supplier management system to revolutionize the process of organizing events. The research approach integrates advanced technology with intuitive interfaces to simplify supplier coordination. With the proposed system, event suppliers can efficiently handle contracts, monitor deliveries, and communicate seamlessly with clients in real time. By combining automated notifications and centralized data storage, the system method ensures that all stakeholders remain informed and aligned throughout the event planning process. The analysis of the needs of the proposed system has yielded significant results from various event suppliers in Surigao City. Based on surveys of 10 event industry professionals and suppliers, it is evident that the limited availability of venues, schedules, and suppliers poses a significant challenge in managing event bookings, with 60% of professionals agreeing. Coordination with other suppliers and communication issues with clients are not significant difficulties for 80% of respondents, and 60% of suppliers are neutral about managing changes or cancellations, though 30% find it difficult. Additionally, 100% of event planners believe collaboration and communication among suppliers are crucial, 90% emphasize the importance of an accessible booking system, and 60% agree that real-time synchronization would enhance efficiency, particularly with a user-friendly interface tailored to local needs in Surigao City. Event suppliers have reported notable reductions in administrative overhead, fewer instances of miscommunication, and improved overall event coordination. Suppliers can allocate resources more effectively through our platform, leading to smoother events and increased client satisfaction. The results of the study will be the basis in proposing web-based event supplier management system in order to address the challenges of suppliers in the event industry

Plant Recommendation System Using Smart Irrigation Integrated with IoT and Machine/Deep Learning

ABSTRACT Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation’s land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of “Spider” plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield.

Prototype of a Software Complex for Creating Digital Twins of Distributed Data Acquisition, Storage, and Processing Centers

Prototype of a Software Complex for Creating Digital Twins of Distributed Data Acquisition, Storage, and Processing Centers

A Secure Storage For Medical Information Scheme Using Blockchain

Nowadays, many companies, organizations, hospitals and individuals have adopted centralized data storage systems to store and share data. However, these systems create a single point of failure and involve a centralized entity or third party, which can cause concern for users. Decentralized storage systems are therefore needed to overcome the drawbacks of the traditional approach. However, in the face of centralization issues, this paper proposes a combination of Hyperledger Fabric, InterPlanetary File System (IPFS), Attribute-Based Access Control (ABAC), and proxy re-encryption to enhance the security and transparency features of decentralized storage systems. Thus, the proposed scheme provides a secure decentralized system storage of medical information using a consortium blockchain

SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

IN-MEMORY INTELLIGENT COMPUTING

Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market.
 Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric.
 Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data.
 Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses.
 Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. KEYWORDS: Intelligent

Healthcare 5.0: A secure and distributed network for system informatics in medical surgery

IntroductionHealth records serve not only as a database of a patient's health history and treatment process but also as a crucial tool for doctors to diagnose and treat patients. However, the storage and sharing of these records are sensitive issues as they involve maintaining patient privacy and ensuring data transparency, security, and interoperability between different parties. Challenges to achieving these goals in the current surgical process can impact the allocation of medical resources and surgical outcomes. MethodsThis article proposes a healthcare 5.0 framework for medical surgery that deploys a secure and distributed network using Blockchain to demonstrate transactions between different parties in the orthopedic surgery process. The proposed network uses the Hyperledger Composer platform for deployment, and a patient-doctor-supplier orthopedic surgery network is designed and implemented to enable the safe sharing of medical records. ResultsA benchmarking tool was implemented for analyzing different scenarios of applying blockchain technology to orthopedic surgery. The application of blockchain technology to orthopedic surgery presents a promising solution for data sharing and supply chain management in the field. The integration of blockchain with cloud storage and hybrid encryption ensures secure and efficient storage of Electronic Health Record (EHR) and Personal Health Record (PHR) data. By leveraging the tamper-proof nature of blockchain and addressing concerns regarding centralized data storage, this scenario demonstrates enhanced security, improved access efficiency, and privacy protection in medical data sharing. ConclusionsThe article demonstrates the feasibility of using an IoT-based blockchain network in orthopedic surgery, which can reduce medical errors and improve data interoperability among different parties. This unique application of blockchain enables secure sharing of medical records, ensuring transparency, security, and interoperability. The network design may also be applicable to other surgeries and medical applications in the future.

Enhancing Accreditation Processes in Polytechnic of Media Kreatif Through Cloud-based Academic Data Collection and Analysis

Data retention plays a crucial role in the accreditation process of an on-campus program of study. Since 2012, Polytechnic of Media Kreatif has been engaged in the implementation of accreditation procedures. However, the effectiveness of these procedures is hindered by the decentralized storage of data, resulting in disruptions throughout the data-gathering process. Consequently, stakeholders have difficulties in their efforts to evaluate and accredit study programs, as they are required to manually look through individual files and data. This research anticipates that the deployment of centralized data storage for certification purposes will alleviate the challenges previously associated with this process. The agile method was used to develop the systems. In conclusion, this study found that the applications that have been created to help with the management of accreditation-related data were supported by a well-running black-box test. It is also backed by an average usability test of 74%.
 Keywords: cloud storage, data management, agile method

Enhancing Coal Mining Efficiency

Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.

Studentsourcing-Aggregating and reusing data from a practical cell biology course.

Practical courses mimic experimental research and may generate valuable data. Yet, data that is generated by students during a course is often lost as there is no centrally organized collection and storage of the data. The loss of data prevents its reuse. To provide access to these data, I present an approach that I call studentsourcing. It collects, aggregates, and reuses data that is generated by students in a practical course on cell biology. The course runs annually, and I have recorded the data that was generated by >100 students over 3 years. Two use cases illustrate how the data can be aggregated and reused either for the scientific record or for teaching. As the data is obtained by different students, in different groups, over different years, it is an excellent opportunity to discuss experimental design and modern data visualization methods such as the superplot. The first use case demonstrates how the data can be presented as an online, interactive dashboard, providing real-time data of the measurements. The second use case shows how central data storage provides a unique opportunity to get precise quantitative data due to the large sample size. Both use cases illustrate how data can be effectively aggregated and reused.

Empowering Edge Intelligence: A Comprehensive Exploration of Fog Computing in the Era of Real-time Data Processing and the Internet of Things

Fog computing, a paradigm that extends cloud computing closer to the edge of the network, has emerged as a transformative solution to address the growing demands of latency-sensitive applications and the massive influx of data from the Internet of Things (IoT). Unlike traditional cloud computing, which centralizes data processing and storage in distant data centers, fog computing leverages a distributed architecture, bringing computation, storage, and networking resources closer to the end-users and devices. This abstract explores the key concepts, principles, and advantages of fog computing. By distributing computing resources across a continuum from the cloud to the edge, fog computing minimizes latency, enhances efficiency, and optimizes network bandwidth. The seamless integration of edge devices into the computing infrastructure facilitates real-time processing of data, enabling timely decision-making and improved user experiences in applications ranging from autonomous vehicles to smart cities. Furthermore, the abstract delves into the architectural components of fog computing, including fog nodes, gateways, and the interaction with cloud resources. The dynamic nature of fog environments allows for scalable and flexible deployments, catering to the diverse requirements of modern applications. Security and privacy concerns are also addressed, emphasizing the need for robust mechanisms to protect data integrity and user confidentiality in decentralized computing environments. As the digital landscape continues to evolve, fog computing emerges as a pivotal enabler of edge intelligence, empowering organizations to harness the benefits of real-time data processing and analysis at the edge of the network. The adoption of fog computing signifies a paradigm shift in the way we approach data management and processing, opening new avenues for innovation and efficiency in the era of the Internet of Things. Keywords: IOT, Fog Computing, Autonomous Vehicles.

Configuring and Coordinating End-to-end QoS for Emerging Storage Infrastructure

Modern data center storage systems are invariably networked to allow for consolidation and flexible management of storage. They also include high-performance storage devices based on flash or other emerging technologies, generally accessed through low-latency and high-throughput protocols such as Non-volatile Memory Express (NVMe) (or its derivatives) carried over the network. With the increasing complexity and data-centric nature of the applications, properly configuring the quality of service (QoS) for the storage path has become crucial for ensuring the desired application performance. Such QoS is substantially influenced by the QoS in the network path, in the access protocol, and in the storage device. In this article, we define a new transport-level QoS mechanism for the network segment and demonstrate how it can augment and coordinate with the access-level QoS mechanism defined for NVMe, and a similar QoS mechanism configured in the device. We show that the transport QoS mechanism not only provides the desired QoS to different classes of storage accesses but is also able to protect the access to the shared persistent memory devices located along with the storage but requiring much lower latency than storage. We demonstrate that a proper coordinated configuration of the three QoS’s on the path is crucial to achieve the desired differentiation, depending on where the bottlenecks appear.

Decentralizing spatial data: the convergence of Geographic Information Systems and Web 3.0 technologies


 
 
 Geographic Information Systems (GIS) have long served as pivotal tools for map- ping and understanding spatial relationships. However, the dawn of Web 3.0 technologies has catalyzed a transformative shift in the geospatial domain. This research meticulously investigates this transformation, focusing on the integration of decentralized spatial data storage, blockchain, Artificial Intelligence (AI), and Machine Learning (ML) with traditional GIS frameworks. Decentralized systems, un- derpinned by the principles of Web 3.0, present a promising alternative to centralized data storage, addressing challenges related to scalability, data sovereignty, and system vulnerabilities. Blockchain technology, traditionally associated with financial transactions, emerges as a cornerstone in this new GIS paradigm, ensuring unparalleled data integrity, transparency, and security. Its decentralized ledger system, combined with consensus mechanisms, offers a robust and transparent framework for managing diverse spatial data- sets, ranging from land registries to intricate environmental monitoring systems. The incorporation of AI and ML technologies further augments the capabilities of GIS. Beyond mere visualization, GIS, when powered by AI and ML, can process vast datasets, discern complex patterns, and even predict future spatial trends with remarkable accuracy. This research emphasizes the role of real-time and dynamic queries, highlighting the transition from static GIS analyses to more adaptive and predictive geospatial evaluations. While the potential advantages of this technological convergence are substantial, the research also sheds light on inherent challenges, espe- cially those related to the management of high-volume real-time data and ensuring data consistency across diverse sources. Building upon foundational works in the field, this study offers a holistic and comprehensive perspective on the synergistic potential of Web 3.0 technologies, AI, ML, and blockchain within GIS. It not only extends the findings of prior research but also paves the way for future explorations, setting the stage for innovative advancements in geospatial analysis.
 
 

Collaborative planning of data center and energy storage based on i-C&CG solving algorithm

Collaborative planning of data center and energy storage based on i-C&CG solving algorithm