Data Storage Research Articles

Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Method: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users.

Programmable photonic latch memory.

Significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications, from data communications and high-performance computing to medical diagnosis, sensing, and ranging. However, data storage in these systems has been dominated by electronic memories that in addition to signal conversion between optical and electrical domains, necessitates conversion between analog to digital domains and electrical data movement between processor and memory that reduce the speed and energy efficiency. To date, scalable optical memory with optical control has remained an open problem. Here, we report an integrated photonic set-reset latch as a fundamental optical static memory unit based on universal optical logic gates. As a proof of concept, experimental implementation of the universal logic gates and realistic simulation of the latch are demonstrated on a programmable silicon photonic platform. Optical set, reset, and complementary outputs, scalability to a large number of memory units via the independent latch supply light, and compatibility with wavelength division multiplexing scheme and different photonic platforms enable more efficient and lower latency optical processing systems.

Enhancing multi-cloud security: novel method for big data storage

Enhancing multi-cloud security: novel method for big data storage

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

Temperature Gradients as a Data Storage Principle

In this work, we analyze the thermodynamic principles underlying modern data storage systems, including Random Access Memory (RAM), hard disk drive (HDD), flash memory, magnetic RAM (MRAM), ferroelectric RAM (FeRAM), and phase-change RAM (PCRAM), as well as other less well-known data storage mechanisms. The analysis is conducted in the context of data storage and processing in relation to Landauer’s principle, with special emphasis on hysteresis. Analogous to how heat engines are characterized by thermodynamic cycles, data storage systems are examined in terms of the hysteresis loop of their fundamental data unit. We explore the role of heat in data storage systems. Afterward, we introduce the concept of temperature gradient memory (TeGraM) along with a detailed layout of a realizable device. Experimental results demonstrating this technology are also presented.

A 64 × 1 Multi-Mode Linear Single-Photon Avalanche Detector with Storage and Shift Reuse in Histogram

Single-photon avalanche detectors (SPADs) have significant applications in fields such as autonomous driving. However, processing massive amounts of background data requires substantial storage and computational resources. This paper designs a linear SPAD sensor capable of three detection modes: 2D intensity detection, 3D synchronous detection, and 3D asynchronous detection. A configurable coincidence circuit is used to effectively suppress background light. To overcome the significant resource demands for storage and computation, this paper designs a histogram circuit that simultaneously possesses data storage and shifting capabilities. This circuit can not only perform statistical counting on time data but also shift data to quickly complete computational analysis. The chip is fabricated using a 0.13 μm mixed-signal CMOS process, with a pixel scale of 64 elements, a time resolution of 132 ps, and a power consumption of 12.9 mW. Test results indicate that the chip has good detection capabilities and good background light suppression. When the background light intensity is 6000 lux, the maximum background data are suppressed by 95.4%, and the average suppression rate increases to 86% as the coincidence threshold is raised from 0 to 1.

Construction of an Intelligent Medical Monitoring System Network for Exercise Rehabilitation Based on the Internet of Things

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1[Formula: see text]s, while wireless sensors can detect heart rates up to 1.28[Formula: see text]s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.

High‐Isolation Multidimensional Holography Multiplexing in Non‐Interleaved Metasurfaces

AbstractMetasurfaces have revolutionized holography by enabling high‐density channel multiplexing, positioning them as promising candidates for applications in full‐color holographic imaging, optical data storage, and encryption. However, conventional non‐interleaved metasurfaces face limitations in the number of controllable dimensions, restricting the channels available in each dimension. Here, a novel high‐dimensional multiplexing scheme is introduced that significantly enhances both the channel multiplexing capacity and isolation in non‐interleaved metasurfaces. By integrating the Rayleigh‐Sommerfeld diffraction (RSD) formula into the Gerchberg‐Saxton (GS) algorithm and incorporating gradient descent optimization, the approach achieves precise phase profile control across multiple dimensions—extending the controllable parameters to include wavelength, polarization state, and spatial distance. This method enables simultaneous multiplexing of three polarization channels, three wavelength channels, and two focal plane distances, producing a total of 18 distinct, well‐isolated holographic channels with minimal crosstalk. These results showcase the powerful channel multiplexing and isolation capabilities of this non‐interleaved metasurface design, underscoring its potential to advance optical color imaging, secure data storage, and high‐capacity information transmission, thereby contributing to the development of next‐generation intelligent optical devices.

Intelligent Upgrading of the Localized GNSS Monitoring System: Profound Integration of Blockchain and AI

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns.

Lossless Phase Change Materials for Adjustable Tamm Plasmon Polaritons in the Near‐Infrared

AbstractIncorporation of phase‐change materials (PCM) into nanophotonic structures is a straightforward method for making them tunable. The binary semiconducting chalcogenide antimony trisulfide (Sb2S3) is a suitable PCM for nanophotonic applications in the near‐infrared (NIR) owing to its high refractive index, low optical losses, and wide bandgap. Therefore, in this study, Sb2S3 Tamm plasmon polaritons (TPPs) are fabricated with a focus on their widespread use in nanophotonic applications. For this, a gold film and Sb2S3 are deposited on the distributed Bragg reflector through e‐beam evaporation. TPPs are excited at the interface between the distributed Bragg reflector (DBR) and the metal layer. The refractive index, extinction coefficient, and high‐Q reflectance spectra of the developed Sb2S3 are measured and analyzed. The Sb2S3 TPPs exhibit a resonance shift of 45 nm caused by the phase change of Sb2S3 from amorphous to crystalline. In addition, the angle‐dependent resonance shifts of 85, 76, and 63 nm are achieved by unpolarized, transverse magnetic (TM), and transverse electric (TE) modes near NIR light, respectively. The developed Sb2S3 TPP can be applied in various nanophotonics applications, including optical memory, optical data storage, and LiDAR receiver systems.

All-silicon non-volatile optical memory based on photon avalanche-induced trapping

Implementing on-chip non-volatile optical memories has long been an actively pursued goal, promising significant enhancements in the capability and energy efficiency of photonic integrated circuits. Here, we demonstrate an non-volatile optical memory exclusively using the most common semiconductor material, silicon. By manipulating the photon avalanche effect, we introduce a trapping effect at the silicon-silicon oxide interface, which in turn demonstrates a non-volatile reprogrammable optical memory cell with a record-high 4-bit encoding, robust retention and endurance. This silicon avalanche-induced trapping memory provides a distinctively cost-efficient and high-reliability route to realize optical data storage in standard silicon foundry processes. We demonstrate its applications in trimming in optical interconnects and in-memory computing. Our in-memory computing test case reduces energy consumption by approximately 83% compared to conventional optical approaches.

Intelligent Travel Assistance for Bus Passengers

The 'Intelligent Travel Assistance for Bus Passengers' introduces an innovative approach by integrating AI capabilities to enhance the notification system as passengers enter a predefined geo-fence. Geo-fence is a virtual border which is defined by GPS, which the user’s destination is at the center point. Users can have the flexibility to customize the notification range, determining how far in advance they receive alerts. This system employs Geo-Fencing and GPS technologies, accessible through the passenger's mobile phone location. The application, developed in Java and utilizing the Google Maps API for destination and geo-fence determination, ensures a user-friendly interface. Going beyond traditional methods, Passengers, can effortlessly select their bus services along with boarding and dropping points and specify approximate times, are ushered into a journey that prioritizes intuitiveness, security, and tailored convenience. In this project, the integration of AI takes this customization to the next level, learning real-time analysis and analyzing recent historical data to deliver personalized and adaptive travel assistance. The system's proactive approach, fueled by real-time traffic analysis and the ability to navigate route diversions intelligently, ensures that users receive timely and accurate alerts, even in dynamically changing conditions. On the infrastructure front, the project incorporates a PostgreSQL database, offering a robust and reliable foundation for data storage, retrieval, and management. Cloud services from AWS, Azure, and Google Cloud provide scalable and efficient hosting solutions. The Android app leverages background location tracking, ensuring accurate and continuous positioning. This holistic integration of technologies creates a sophisticated travel assistance system that combines innovation, customization, and reliability.

Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models

Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation. To address this challenge, we propose RePACK, a threefold data protection scheme that safeguards neural network input, weight, and structural information. It utilizes a bipartite-sort coding scheme to store data with a fully on-chip physical unclonable function. Experimental results demonstrate the effectiveness of increasing enumeration complexity to 5.77 × 1075 for a 128-column compute-in-memory core. We further implement and evaluate a RePACK computing system on a 40 nm resistive memory compute-in-memory chip. This work represents a step towards developing safe, robust, and efficient edge neural network accelerators. It potentially serves as the hardware infrastructure for edge devices in federated learning or other systems.

Development of Heuristic Strategy With Hybrid Encryption for Energy Efficient and Secure Data Storage Scheme in Blockchain‐Based Mobile Edge Computing

ABSTRACTInternet of Things (IoT) devices is extensively employed to collect physiological health data and provide diverse services to end‐users. Nevertheless, in recent applications, cloud computing‐based IoT proves beneficial for standard data storage and ensuring high‐security information sharing. Due to limitations in battery capacity, storage, and computing power, IoT devices are often considered resource‐constrained. Consequently, data signing by IoT devices, aimed at ensuring data integrity and authentication, typically demands significant computational resources. Unsafe data storage and high latency are considered as the major issues in the IoT‐based data storage mechanism for duplicating and misusing the information while it is stored in the cloud database. Hence, blockchain technologies are needed to provide high security over the stored data. Hence, the research aimed to implement an efficient blockchain‐based data storage system in mobile edge computing, safeguarding data from unauthorized access. In this approach, it contains four layers that are cloud layer, the entity layer, the block‐chain layer, and the edge computing layer. The user's data are stored in the optimal location in the entity layer, where the data storing location is find out using the proposed Hybrid Battle Royale with Archimedes Optimization Algorithm (HBRAOA). In the edge computing layer, an optimal key‐based homomorphic encryption algorithm using Elliptic Curve Cryptography (ECC) is introduced to encrypt data with the most optimal key, ensuring secure storage. This encryption method leverages the same HBRAOA to enhance the efficiency. Next, the digital signature is demonstrated to give the authorization of the user, and it is distributed to the blockchain layer. Thus, the indexes of the shared data are stored in the blockchain layer to avoid fault tolerance and tamper‐proofing. Finally, the cloud layer receives the valuable encrypted data, and the authenticated users with known encrypted keys are able to access the data by decrypting them. The result analysis shows that the performance of the developed model, which attains 27%, 98%, 35%, and 18% enhanced than Particle Swarm Optimization (PSO)‐ECC, Black Widow Optimization (BWO)‐ECC, Battle Royale Optimization (BRO)‐ECC and Archimedes Optimization Algorithm (AOA)‐ECC. The efficiency of the proposed blockchain‐based mobile edge computing scheme with the optimization strategy is validated by conducting several similarity measures over the conventional methods.

A practical DNA data storage using an expanded alphabet introducing 5-methylcytosine

The DNA molecule is a promising next-generation data storage medium. Recently, it has been theoretically proposed that non-natural or modified bases can serve as extra molecular letters to increase the information density. However, this strategy is challenging due to the difficulty in synthesizing non-natural DNA sequences and their complex structure. Here, we described a practical DNA data storage transcoding scheme named R+ based on an expanded molecular alphabet that introduces 5-methylcytosine (5mC). We demonstrated its experimental validation by encoding one representative file into several 1.3∼1.6 kbps in vitro DNA fragments for nanopore sequencing. Our results show an average data recovery rate of 98.97% and 86.91% with and without reference, respectively. Our work validates the practicability of 5mC in DNA storage systems, with a potentially wide range of applications. Availability and implementation R+ is implemented in Python and the code is available under a MIT license at https://github.com/Incpink-Liu/DNA-storage-R_plus.

An Artificial Intelligence Home Monitoring System That Uses CNN and LSTM and is Based on the Android Studio Development Platform

This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud data storage and computation, and end device control. A mobile application was developed using MongoDB software, which is a file-oriented NoSQL database management system developed using C++. This system represents a new database for processing big sensor data. The k-nearest neighbor (KNN) algorithm was used to impute missing data. Node-RED development software was used within the server as a data-receiving, storage, and computing environment that is convenient to manage and maintain. Data on indoor temperature, humidity, and carbon dioxide concentrations are transmitted to a mobile phone application through the MQTT communication protocol for real-time display and monitoring. The system can control a fan or warning light through the mobile application to maintain ambient temperature inside the house and to warn users of emergencies. A long short-term memory (LSTM) model and a convolutional neural network (CNN) model were used to predict indoor temperature, humidity, and carbon dioxide concentrations. Average relative errors in the predicted values of humidity and carbon dioxide concentration were approximately 0.0415% and 0.134%, respectively, for data storage using the KNN algorithm. For indoor temperature prediction, the LSTM model had a mean absolute percentage error of 0.180% and a root-mean-squared error of 0.042 °C. The CNN–LSTM model had a mean absolute percentage error of 1.370% and a root-mean-squared error of 0.117 °C.

An Application of SEMAR IoT Application Server Platform to Drone-Based Wall Inspection System Using AI Model

Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal.

The Race for Data: Utilizing Informative or Persuasive Cues to Gain Opt-In?

The European Union's General Data Protection Regulation (GDPR) mandates explicit user opt-in consent for data access. It recommends transparency in opt-in requests about data collection, storage, and use, without specifying the format of these requests. Consequently, the GDPR gives firms flexibility in designing opt-in messages. This research uses theory, multiple datasets, and methods to investigate firms’ communication formats for opt-in requests, addressing three questions: (1) How do firms design their opt-in requests? (2) Does the chosen format affect consumer response? (3) What drives firms’ choices of formats? The analysis of 1,506 repermission emails from 1,396 firms post-GDPR shows that 26% use only persuasive cues to request data, while 24% blend persuasive and informative cues. Notably, businesses with an offline presence use more persuasive cues compared with purely digital entities. A field experiment rationalizes this behavior showing that informative cues alone did not improve opt-in; a mix of persuasive and informative cues proved more successful. Additionally, firms dependent on personal data use persuasive cues more often than firms concerned with reputational risks of GDPR noncompliance. This study offers pivotal insights for regulators, firms, and consumers, revealing variations in how different firms acquire consent and the impact of their strategies on user behavior.

Scaling Data Infrastructure for High-Volume Manufacturing: Challenges and Solutions in Big Data Engineering

Abstract—Scaling data infrastructure for high- volume manufacturing presents significant challenges owing to the rapid growth, diversity, and complexity of the data generated by modern production processes. This review explores the key challenges and solutions in big-data engineering to enable efficient, scalable, and reliable data management in manufacturing environments. The primary challenges include handling the volume, velocity, and variety of data; ensuring real-time processing and analysis; managing data storage and retrieval at scale; and maintaining data quality and consistency. To address these challenges, various big data engineering solutions have been discussed, including distributed computing frameworks, cloud-based storage and computing resources, data lakes, data governance and metadata management, stream processing technologies, machine learning, and AI for predictive analytics. This review also examines the role of data architecture and infrastructure in building scalable systems, highlighting the importance of microservices, containerization, orchestration, NoSQL databases, and data security and privacy. Performance optimization techniques, such as query optimization, data partitioning, sharding, caching, and data compression, have been explored to ensure efficient operation of large-scale data systems. The review includes case studies of successful implementations and discusses emerging trends, such as edge computing, and the growing importance of data interoperability and standardization. Future research directions were identified, emphasizing the need for ongoing development in this field to meet the ever- growing demand for high-volume manufacturing. Keywords—high-volume manufacturing, big data, data infrastructure, distributed computing frameworks, cloud computing, data lakes, microservices, containerization, orchestration, query optimization, data partitioning, sharding, caching, data compression

WALSH: Write-Aggregating Log-Structured Hashing for Hybrid Memory

Persistent memory (PM) brings important opportunities for improving data storage including the widely used hash tables. However, PM is not friendly to small writes, which causes existing PM hashes to suffer from high hardware write amplification. Hybrid memory offers the performance and concurrency of DRAM and the durability and capacity of PM, but existing hybrid memory hashes cannot deliver high performance, low DRAM footprint, and fast recovery at the same time. This paper proposes WALSH, a flat hash with novel log-structured separate chaining designs to optimize the performance while ensuring low DRAM footprint and fast recovery. To address the overhead of hash resizing and garbage collection (GC), WALSH further proposes partial resizing/GC mechanisms and a 4-phase protocol for concurrent hash operations. As a result, WALSH is the first flat index for hybrid memory with embedded write aggregation ability. A comprehensive evaluation shows that WALSH substantially outperforms state-of-the-art hybrid memory hashes; e.g., its insert throughput is up to 2.4X that of related works while saving more than 87% of DRAM. WALSH also provides efficient recovery; e.g., it can recover a dataset with 1 billion objects in just a few seconds.