Cloud Database Research Articles

The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overcome this, the authors have developed a unique sensor-driven, secure, cloud-based scalable data framework enabling real-time acquisition, streaming, and analytics of OR data, accessible to surgeons as feedback and performance reporting. For system validation, this dynamic digital platform was deployed across neurosurgical centers for precise, accurate, and fast analytics of surgical data, establishing an Internet of Things-OR (IoT-OR). Through recent deployment of a novel sensorized surgical device called the SmartForceps System, the authors established and validated a data-driven interconnected platform for neurosurgery, the IoT-OR. The system includes sensorized surgical bipolar forceps, allowing quantification of tool-tissue force in real time. Surgical microscope video live-streamed into the software allows a videographic data display time-stamped to tool-tissue interaction, enabling both quantification of surgery and real-time interrogation for feedback and guidance. This IoT platform, with secure data containers by each surgical center and hosted in the cloud, allows data flow and automated analytics through its custom artificial intelligence (AI) model, enriching the model with each new case in perpetuity. The output is a surgeon performance report unique to each procedure and accessible by the surgeon via secure personalized devices and authentication. In more than 250 neurosurgical procedures, spanning 3 neurosurgical units across western Canada (University Alberta Hospital, Edmonton, Alberta; Vancouver General Hospital, Vancouver, British Columbia; and Foothills Medical Centre, Calgary, Alberta, Canada), the system successfully demonstrated that a cloud-driven end-to-end secure platform for surgical procedures can be enabled and operated in real time. Linked to a smart surgical device, built-in intelligent software interface with cloud connectivity, a unique IoT-OR platform has thus been established, with built-in security and scalability to include other data sources (e.g., OR equipment, electronic medical records), multiple centers, and surgeons globally. The study thus demonstrates the utility of sensors, AI, and cloud interconnectivity in real-time monitoring, analytics, and feedback as a digital footprint of surgery. Using and quantifying closed-door OR data and weaving them into a secure and innovative data-rich pipeline, the system offers a glimpse toward standardization of surgery at the level where the tool meets the tissue.

As the number of natural and man-made catastrophes has increased in recent years, there has been an increasing need for quicker and more efficient disaster response. Information from traditional sources, such as radio, television, and websites, is sometimes incomplete or delayed. While mobile applications provide a means of enhancing real-time crisis communication, a secure mobile app-based solution has not been fully explored yet. In this paper, we propose a secure and scalable cross-layer disaster management system architecture. To validate the system performance, we developed a user-centred, scalable mobile application known as the disaster emergency events application (DEAPP) for real-time disaster reporting and visualization including disaster notifications and observing the affected areas on an interactive map. The solution connects a web-based backend, cloud database, and native Android mobile app via a cross-layer architecture. Role-based access control, HTTPS connection, and verified event publication all contribute to security. Moreover, Redis caching is employed to expedite data access in emergency situations. The need to verify publicly filed reports to prevent false alarms, safeguard real-time data transfer without slowing down the system, and create an intuitive user interface for individuals in high-stress circumstances are some of the issues that the project attempts to solve. The results obtained show that a mobile system that is secure, scalable, and easy to use can enhance catastrophe awareness and facilitate quicker emergency responses. For developers, researchers, and emergency organisations looking to leverage mobile technology for disaster preparedness, the findings provide helpful insights.

To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) (t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

This study aimed to examine glucose metrics and insulin delivery patterns in children, adolescents, and adults with type1 (T1D) or 2 (T2D) diabetes in France using the tubeless Omnipod DASH® pump with and a continuous glucose monitoring (CGM) sensor connected to myDiabby Healthcare® Data Management Platform (DMP). Time-stamped CGM and insulin data were extracted from the DMP on December 6, 2023 for 17,344 users whose first data point from the tubeless pump occurred after January 1, 2020. The study population included users with sufficient pump and CGM data (≥ 90days of use) and ≥ 15.5% of CGM use days reaching > 70% coverage. Analyses were performed by type of diabetes and age group. Among 14,757 users included in this analysis, most reported having T1D (93.7%), the median age was 33years (Q1-Q3, 16-51), and the median duration of pump use was 545days for people with T1D and 505days for people with T2D (1.49 and 1.38years, respectively). People with T1D spent a median of 52.5% (Q1-Q3, 43.4-62.5) of time in range (70-180mg/dL, TIR) and a TIR ≥ 70% was attained by 12.6% of users. The median time below range (TBR, < 70mg/dL) was 3.7% (Q1-Q3, 2.1-6.1). For users with T2D, median TIR was 66.9% (Q1-Q3, 54.0-77.8), with 42.8% of users achieving a TIR ≥ 70%. Over 90% of all users consumed less than 60UI/day. This robust and scalable analysis of a database of substantial quantity, density, and quality found that tubeless pump users achieved moderate glycemic outcomes overall with favorablesafety outcomes in particular, and used the pump consistently. Such databases could be useful for research and patient care, and further work will show how best to use them.

Drowning is a leading cause of accidental deaths among children, largely due to insufficient supervision and delayed emergency responses. This paper presents the development of the Smart Water Safety for Children system, an Internet of Things (IoT)-based prototype designed to enhance child safety during aquatic activities. The proposed system integrates multiple sensors including a MAX30100 pulse oximeter, a water-level sensor, and a NEO-6MV2 GPS module, all managed by a NodeMCU V3 microcontroller. These components collect real-time physiological and positional data from the child, which is transmitted via Wi-Fi to a Firebase cloud database and visualized through a mobile application developed on Android Studio. The application provides guardians with real-time updates on heart rate, oxygen saturation, and location, issuing alerts when critical thresholds are detected. Functional and physiological testing confirms the system's reliability in identifying near-drowning scenarios and effectively notifying emergency contacts. This system demonstrates a cost-effective, scalable, and accessible solution for enhancing aquatic safety among children.

SGDSAC: a scalable self-governing DSAC-based learning framework for on-chain sharding and off-chain cloud databases

This paper explores rolling element bearing data collection and hyperparameter tuning for machine learning-based fault diagnosis to aid in the development of modern condition monitoring systems. The integration of industrial internet of things (IIoT) products and cloud databases has led to an increased interest in utilizing artificial intelligence (AI) models, including artificial neural networks (ANNs) and convolutional neural networks (CNNs), to diagnose machine faults. However, the development of AI methodologies in smart monitoring is hindered by a lack of publicly available industry data, as well as limitations involved in the collection and storage of large high-dimensional datasets. Combining machine learning (ML) methods, such as traditional learning (TL), deep learning (DL), and bearing signature theory, will allow for a better understanding of data collection and hyperparameter tuning. Moreover, considering how high-dimensional datasets for rolling element bearing fault diagnosis affect ML algorithms has yet to be explored in the literature, providing little robustness for analysis. Concerns around the way data has been collected and used historically for both TL and DL are raised. Therefore, recommendations for data collection specifically suited to TL and DL methods for rolling element bearing fault diagnosis are proposed by analyzing existing lab-based datasets. The recommendations proposed combine knowledge of these methodologies to aid in selecting an appropriate sampling rate, as well as the ideal number of samples, stride, duration of each sample, and resolution for rolling element bearing fault diagnosis. The goal is to increase efficiency and reduce setup and collection time when selecting the design parameters for creating new rolling element bearing datasets. To achieve this, the study applied a structured approach with the use of multiple datasets to determine a threshold accuracy of 95% for fault diagnosis. Furthermore, the results of this study will help IIoT companies re-evaluate the constraints imposed by the limited data storage and transmission of their devices when used for ML. This paper will also help improve the efficiency and effectiveness of AI methodologies in smart monitoring systems by establishing data collection recommendations. This work will hopefully motivate the vast collection of open-access data that can be used by researchers to further develop ML-based methods for rolling element fault diagnosis.

Railway transportation offers high convenience, environmental sustainability, and strategic importance. Bogies, serving as the core mechanical structure of a railway train, play a vital role in ensuring safe and efficient operations. However, conventional bogie maintenance practices remain constrained by fixed schedules and reactive fault responses, lacking real-time monitoring and predictive capabilities. While previous digital twin (DT) studies have focused primarily on data analytics and system modeling, this study emphasizes a foundational yet often overlooked aspect: the development of sensor hardware and data acquisition systems essential for any DT platform. Leveraging the customized hardware, a rapid deployment DT platform is proposed in this study, integrating various sensors, microcontrollers, wireless communication protocols, and cloud databases to support real-time monitoring of train bogie conditions. A three-layered architecture, comprising the physical, digital, and service layers, is proposed to enable seamless data flow and predictive diagnostics. The designed predictive functionality of the platform is validated through a case study involving vibration-based fault detection of a high-speed train gearbox. All experiments are conducted in a laboratory setting to facilitate data acquisition. This work provides a practical framework for digital twin system implementation and underscores the critical role of hardware development in advancing intelligent railway maintenance solutions.

This study provides an analysis of the entry strategies of third-party data centers in the United States. We examine the market prior to the pandemic, in 2018 and 2019, when supply and demand for data services were relatively stable geographically. We compare these patterns with the entry strategies of major cloud-based data centers for services on demand, which include those known as cloud services. We conclude that third-party firms and cloud providers have different entry strategies for this digital infrastructure. The former favors urban settings more and appears sensitive to buyer demand for proximity. They trade off costs of supply, which vary with density, and economies of scale, which cannot be achieved without large volumes of demand. We also find that data center firms providing specialized services display an urban bias. Cloud providers tend to display a lower propensity for locating in urban areas, concentrating their buildings in a small number of locations. We see little evidence to suggest cloud providers will spread their data centers to any but a small number of low-density locations. Our findings support speculation about the likely direction of changes as demand shifts to the cloud, and the location decisions begin to concentrate in the hands of cloud providers. Funding: The authors are grateful to Rice University and the HBS Division of Research and Faculty Development for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0225 .

This paper introduces the design, development, and deployment of an NFC-based smart card system tailored explicitly for academic environments, aiming to improve data management, student identification, and administrative automation. Built around the Arduino Uno R3 and PN532 NFC module, the system provides seamless integration with a MariaDB relational database and a Java-based user interface. Key features include student attendance tracking, real-time access to academic records, and secure cloud-based data storage. A role-based access model is implemented to ensure that students and professors have appropriate visibility of data, thereby reinforcing data privacy and security. The system enables students to interact with NFC cards using their smartphones, granting access to personalized academic files stored on platforms such as Google Drive. The software layer, developed using Visual Studio Code and Apache POI for Excel exports, enables robust administrative control over student records, grades, and catalog updates.

Recognizing the significant spatial visualization challenges that high school students face in understanding abstract chemical compound structures—a limitation often inherent in conventional teaching methods based on 2D diagrams—this research presents the comprehensive development and user experience (UX) evaluation of an innovative adaptive learning application in Virtual Reality (VR). The application, developed using the Unity 3D engine and configured via XR Plugin Management to ensure broad hardware compatibility, places students in an interactive virtual laboratory. Within it, students can directly manipulate meticulously designed 3D atomic models to build molecules, observe the formation of covalent and ionic bonds, and interact with dynamic chemical processes. Its key innovation is the integration of an intelligent adaptive learning algorithm, which utilizes a Firebase cloud database to analyze user performance metrics—such as accuracy, completion time, and recurring areas of difficulty. Based on this data, the system dynamically personalizes learning pathways by recommending remedial content or more challenging topics. Furthermore, assessment materials such as quizzes were efficiently generated using large language models (LLMs) to ensure relevance and quality. An in-depth UX evaluation was conducted with high school students using a mixed-methods approach, combining standardized questionnaires to quantitatively measure metrics like usability, engagement, and satisfaction, with qualitative feedback sessions for contextual insights. The results indicate a highly positive user experience; participants reported that the ability to directly manipulate molecules in 3D space significantly enhanced their conceptual understanding, bridging the gap between theory and visualization. The adaptive system was highly valued for its ability to adjust to individual learning paces, which was shown to boost confidence and reduce frustration. This research provides strong evidence that VR-based adaptive learning platforms are powerful pedagogical tools, capable of transforming chemistry education by making complex scientific concepts more accessible, engaging, and comprehensible.

The blistering rise of cloud computing has created tremendous opportunities for large-scale storage and processing of data at its disposal, but it has put some great strains in terms of analytics and security management in the cloud. The volume, velocity, and variety of cloud-based data overwhelm traditional data analytics models, and traditional security frameworks are subject to increasingly intelligent attacks. The use of Artificial Intelligence (AI) supports cloud-based locations in addition to new possibilities of increasing analytics and data protection. This paper examines how data analytics and cloud-based security systems integrate artificial intelligence (AI)-driven algorithms such as machine learning, deep learning, and reinforcement learning. A comparative study of AI-based strategies over the conventional methods illustrates that of predictive accuracy, identification of anomalies, real-time decisions, and automated responses to threats. In addition, the study points out important issues linked to the domain, including biased algorithms, computational demands, and privacy, as well as directions that may be taken further in the future, including federated learning, edge-AIN, and quantum-enhanced cryptography. The results indicate that AI not only enhances trustworthiness levels of cloud ecosystem data analytics but also introduces proactive and flexible security systems, which foster trust, compliance, and resilience in digital networks. This paper adds value to the scholarly and practical knowledge through the presentation of an AI-driven framework that confronts the twofold path to analytical efficiency and data security in modern cloud computing.

Enhancing the efficiency of renewable energy on ships is crucial for reducing dependency on fossil fuels. This research employs the Research and Development (R&amp;D) method, aiming to design and implement a solar panel optimization system for battery charging, with a focus on increasing power efficiency and providing real-time performance monitoring. The system is designed using Maximum Power Point Tracking (MPPT) technology to maximize the solar panel's power output. A 200Wp solar panel with dimensions of 1290 x 760 x 30 mm was utilized. Static testing results show that the deployed sensors possess a high degree of accuracy, with an average error of 0.71% for the temperature sensor and only 1.81% for the light sensor used to monitor environmental conditions. Dynamic and system integration tests prove that the MPPT implementation significantly increases power output efficiency by 30.83% compared to a system without MPPT. Furthermore, the system with MPPT charges the battery approximately 27% faster. Additionally, the developed Modbus protocol-based monitoring system enables comprehensive and remote monitoring of key parameters such as voltage, current, temperature, and light intensity via a cloud database. Data communication reliability tests confirmed the system's capability to transmit entire data packets to a Google Sheets database at a periodic interval of 15 seconds without failure. Based on these results, the developed solar panel optimization system is feasible for implementation in maritime environments to enhance the utilization efficiency of renewable energy and the operational reliability of onboard systems.

This paper presents an innovative Arduino-based smart parking system that utilizes ‎real-time proximity sensing to efficiently manage parking spaces. The system ‎employs ultrasonic sensors, LCDs, LED indicators, and a Wi-Fi module to ‎deliver accurate and intuitive feedback to drivers. When an available parking space is ‎detected, the system automatically updates the parking status and guides the driver to ‎the vacant spot, thereby reducing search time and fuel consumption. The system can ‎also be integrated with cloud databases and mobile applications, enabling remote ‎access to real-time parking data and enhancing user convenience. Automated space ‎allocation minimizes human intervention, increases operational efficiency, and ‎reduces costs. IoT-enabled connectivity ensures seamless data exchange for ‎continuous monitoring and predictive analytics, optimizing overall parking ‎management. Security is enhanced through camera-based vehicle identification, ‎preventing unauthorized access and improving enforcement. The system’s modular ‎and scalable design makes it suitable for deployment in diverse settings such as ‎shopping malls, office complexes, hospitals, airports, and universities.

The importance of the ocean to humanity is undeniable, whether in terms of ecology, climate, resources. Utilizing collected ocean data combined with AI to achieve adaptive and automated processing and prediction is a current research focus. The effectiveness of AI applications largely depends on the integrity of ocean data. Ocean data has three characteristics: vast spatial coverage, long temporal duration, and large volume. Traditional cloud-based data integrity verification methods are no longer suitable. Ocean data should be processed on edge servers located closer to the data collection points and then sent to the appropriate data storage servers. The data processing methods should be lightweight to accommodate the sequential characteristics of data. Moreover, the data integrity monitoring process should be collaboratively completed on the data storage servers without the need for a central third party. To this end, we propose a ocean data integrity monitoring protocol. It generates data for different storage servers, using sensor sampling periods and data masks, and utilizes chameleon hash with ephemeral trapdoors to generate validators, thus supporting mutual integrity monitoring among storage servers. Experiments demonstrate that our scheme compared to the latest solutions, not only meets security requirements but also offers advantages of computational overhead.

The Attribute-Based Access Control (ABAC) model provides access control decisions based on subject, object (resource), and contextual attributes. However, the use of sensitive attributes in access control decisions poses many security and privacy challenges, particularly in cloud environment where third parties are involved. To address this shortcoming, we present a novel privacy-preserving Dummy-ABAC model that obfuscates real attributes with dummy attributes before transmission to the cloud server. In the proposed model, only dummy attributes are stored in the cloud database, whereas real attributes and mapping tokens are stored in a local machine database. Only dummy attributes are used for the access request evaluation in the cloud, and real data are retrieved in the post-decision mechanism using secure tokens. The security of the proposed model was assessed using a simulated threat scenario, including attribute inference, policy injection, and reverse mapping attacks. Experimental evaluation using machine learning classifiers (“DecisionTree” DT, “RandomForest” RF), demonstrated that inference accuracy dropped from ~0.65 on real attributes to ~0.25 on dummy attributes confirming improved resistance to inference attacks. Furthermore, the model rejects malformed and unauthorized policies. Performance analysis of dummy generation, token generation, encoding, and nearest-neighbor search, demonstrated minimal latency in both local and cloud environments. Overall, the proposed model ensures an efficient, secure, and privacy-preserving access control in cloud environments.

The convergence of Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) has transformed modern bioanalytical practices, enabling real-time physiological data acquisition, transmission, and analysis. This paper proposes a secure, scalable, and intelligent bioanalytical framework—BioCloudSense—which integrates IoT-based biosensors, cloud-based data processing, and deep learning models for early detection of critical health conditions. The system collects multi-modal physiological data (ECG, SpO₂, temperature, etc.) from wearable devices and transmits it securely to a federated cloud platform, where pre-trained convolutional and recurrent neural networks perform real-time diagnostic predictions. Differential privacy and blockchain-based identity management secure patient data during cloud processing. Experimental validation on the MIT-BIH and MIMIC-III datasets demonstrates 97.3% accuracy for arrhythmia detection and 94.1% precision in early sepsis prediction. The proposed framework offers a robust architecture for real-time, remote bioanalysis, ensuring high diagnostic accuracy while preserving privacy and scalability.

Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption

The rapid integration of digital technologies into testing and calibration laboratories has significantly increased both operational opportunities and information security risks. Compliance with ISO/IEC 17025:2017 requires laboratories not only to ensure the technical accuracy of testing and calibration activities but also to implement systematic information risk management practices. This paper presents a comprehensive study on the identification, analysis, and prioritization of information risks in a laboratory environment that employs a Laboratory Information Management System (LIMS), IoT devices, and cloud-based data infrastructures. The research adopts a hybrid methodology that combines qualitative tools (risk matrix and impact–probability assessment) with quantitative models (Common Vulnerability Scoring System, CVSS). Five predominant risks were identified: outdated and unpatched versions of LIMS, insecure IoT sensor communications, low staff cybersecurity awareness, weaknesses in cloud access control, and lack of logical network segmentation. Among these, unpatched LIMS platforms and insufficient staff awareness emerged as the most critical risks, each scoring high on both likelihood and impact, thus directly threatening laboratory accreditation and data integrity. The findings reveal that information risks in ISO/IEC 17025-compliant laboratories arise not only from technological vulnerabilities but also from human factors and insufficiently standardized processes. The absence of systematic patch management was identified as the most pressing risk, while inadequate network segmentation further exacerbates incident containment. To address these issues, the study proposes a set of mitigation strategies aligned with ISO/IEC 27001/27005, NIST SP 800-30, and ENISA best practices. Key recommendations include the adoption of automated patch management policies, implementation of network segmentation to isolate IoT devices from core systems, multi-factor authentication, encryption of sensitive data, and continuous staff training. The proposed framework enhances both compliance and resilience, ensuring that laboratories maintain the integrity, confidentiality, and availability of their information assets while meeting the requirements of ISO/IEC 17025 accreditation. Beyond compliance, this approach positions laboratories to effectively respond to evolving cybersecurity challenges in dynamic environments.

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead.