Network Data Storage Research Articles

Securing the IoT-enabled smart healthcare system: A PUF-based resource-efficient authentication mechanism

As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

Blockchain-based data sharing algorithm in distributed network data storage

This paper proposes a sharing algorithm based on blockchain principles to address the issues of data sharing, low efficiency, and performance in traditional systems. The algorithm is integrated with the domain name system to develop a data storage system based on blockchain. The performance of the sharing algorithm is evaluated, and the data storage system is tested. This demonstrates that the sharing algorithm’s average latency is 436 ms and average throughput is 5439 tps. Furthermore, it outperforms the other comparison algorithms. Additionally, the study conducts performance experiments to compare the data storage system. The data storage system proposed in this study demonstrates a higher average throughput of 6.42*108 tps and a faster data access time of 0.15 s than the other comparison systems. The comprehensive results show that the proposed sharing algorithm and data storage system outperform the comparison algorithm and system in terms of latency, throughput, and data access performance. The constructed model exhibits good centralized and distributed storage crawling performance, which can achieve more secure, efficient, and trustworthy data sharing in distributed network data storage.

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain

AbstractIn this paper, a hybrid blockchain‐based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway‐based Energy‐Aware Multi‐hop Routing (I‐HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.

Developing Decentralized Data Storage Network Using Blockchain Technology to Prevent Data Alteration

In the face of escalating global data exchange, the pronounced vulnerability oftraditional centralized storage networks to manipulation and attacks poses a pressing challenge. Digital service providers, entrusted with vast datasets, grapple with the formidable task of ensuring the security, integrity, and continuous availability of their stored information. This paper tackles these multifaceted issues by proposing a decentralized data storage network empowered by blockchain technology. This approach systematically mitigates the inherent susceptibilities of centralized systems, thereby providing heightened resilience against unauthorized alterations and malicious attacks that compromise digital information integrity. Moreover, the decentralized model holds significant promise for securing public data. By leveraging the transparency and immutability of blockchain ledgers, this approach not only safeguards against unauthorized access but also actively fosters transparency and accountability in data management. This makes it particularly well-suited for ensuring the security and integrity of public data, addressing concerns related to trust and reliability in the ever-evolving landscape of information exchange.

Design and application of airport data storage under an information security optimization system

Abstract With the advancement of technology, the “partial construction” model is no longer able to meet the current information requirements of the aviation industry. Therefore, it is necessary to establish a complete “global informatization”, that is, an airport data center, which comprehensively includes multiple production management systems within the airport group, achieves information sharing, data analysis, and intelligent decision-making, thereby enabling the airport to achieve more accurate and reliable comprehensive economic and socially sustainable development. The current network storage technology often generates massive amounts of data when processing it, which leads to an increase in storage cycles and a decrease in storage performance. On this basis, the FPGA interface in FLASH chips was studied and analyzed in detail. On this basis, the FPGA receiving module is used to accurately receive and filter the massive data obtained, and convert it into high-quality FLASH format for daily use and maintenance. By analyzing the three-layer architecture, a large-scale network data storage solution based on the three-layer architecture is proposed. Finally, an implementation scheme for a new distributed storage system based on this model is presented, and simulation results are provided.

Secure Storage of Computer Network Data based on Cloud Computing

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.

A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks.

Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods.

Exploring 6G Wireless Communication: Application Technologies, Challenges and Future Direction

The data traffic has increased significantly along with the quick development of smart wireless technologies, and current 5G networks are still not completely prepared to handle future huge data traffic in the areas of services, data transfer, data storage, and processing. 6G is expected to offer very high-level extended 5G capabilities, such as a Tbps data transfer rate and a sub-millisecond response time. The main objective of this study is to be aware of new technologies being enhanced day by day and provide existing research content, issues, and directions for the future regarding 6G wireless networks. Therefore, a systematic literature review of research articles is provided by categorizing the selected studies published between 2019 and 2022 in terms of enabling technologies. First, the researcher discussed the need for 6G by predicting the explosive growth of mobile traffic from 2022 to 2030. Second, 6G requirements, trends, and services are addressed and contrasted with 5G in terms of a group of key performance indicators. Third, conduct a comparative analysis of 6G challenges and gaps, such as security and privacy, the need for a decentralized network, Omnipresent service coverage, and storage efficiency. So, it is essential to develop an accurate, faster, and well-organized system for the 6G wireless network. We have presented an efficient and secure mode R6GS, which combines blockchain and 6G to improve security from the lower to upper layers while also improving storage efficiency. Cybertwin technologies are used at the edge layer of the 6G network in the proposed model R6GS to improve security. The smart contracts, IPFS and Ethereum blockchain are used in the proposed R6GS model to enhance the 6G network's data integrity and data storage. It also processes and stores massive amounts of data at various network layers, which removes the large storage capacity issue of traditional P2P and Client/Server (C/S) networks due to centralized servers in 6G technology. For the performance evaluation, the model is evaluated through expert opinion. Moreover, the proposed R6GS model also improves time efficiency through the edge layer, because if a task is available at the edge layer, there is no need to send a request to the core layer. The server at the edge layer efficiently delivers data to end nodes. Furthermore, a taxonomy is presented in this study. To the best of our knowledge, the proposed methodology worked efficiently.

Devising a method for detecting “evil twin” attacks on IEEE 802.11 networks (Wi-Fi) with KNN classification model

The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called "evil twin". Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the "evil twin" type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the basis of the k-nearest neighbors method. This method is based on the classification of previously collected data, namely the signal strength from the access point, and then continuous comparison of the newly collected data with the trained model. A compact and energy-efficient prototype of a hardware and software system has been designed for the implementation of monitoring, analysis of ethernet network packets and data storage based on time series. In order to reduce the load on the computer network and taking into account the limited computing power of the system, a method of data aggregation was proposed, which ensures fast transfer of information. The results, namely 100 % of test cases (more than 7 thousand), were classified correctly, which indicates that the chosen method of data analysis will significantly increase the security of information and communication systems at the state and private levels

Lightweight Video Super-Resolution for Compressed Video

Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.

Development Strategy of Intelligent Digital Library without Human Service in the Era of "Internet +".

Internet technology has a great impact on the application and development of libraries. In order to provide users with more convenient and efficient services, this paper puts forward the development strategy of unmanned intelligent digital libraries in the era of “Internet +.” Design by IntelliSense module, network transmission module, data storage and analysis module, and intelligent service module of no wisdom digital library service mode, IntelliSense module using RFID technology, close range wireless communication technology, and other Internet technologies to collect the wisdom of unmanned services of all kinds of information in the digital library; use network transmission module of the wireless LAN network, Internet technology such as RFID network to transmit information collected to store in a data storage and analysis module; and at the same time using k-means clustering algorithm in data mining techniques such as analysis of collected data, based on the analysis results provide users with intelligence services such as search, wisdom is recommended. The experimental results show that the developed strategy meets the expected functional requirements, can accurately perceive the location of data, and effectively realize the intelligent service function.

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

MLOps BASED PROTOTYPE OF AI SYSTEM FOR EDGE COMPUTING

The paper represents an approach for developing the edge AI system which is based on the modern MLOps concept. For the edge hardware part we utilize Nvidia Jetson Nano microcomputer which provides server side for network requests processing, data storage and machine learning models of self-deployment. We propose the working MLOps pipeline fully designed by the industrial software solutions like TensorFlow 2, Mlflow, Apache Airflow, which is integrated into the developed application. The considered pipeline scheme consists of three operational stages: a) data storage and processing, which stands for fetching the data from database, cleansing and transformation; b) machine learning modeling with synchronous hyper-parameters optimization and model registration; c) model deployment and serving. The whole pipeline is wrapped by the REST API created via FastAPI micro-framework and orchestrated using Apache Airflow service. To implement the described pipeline we chose the time dependant temperature data to be learned and short-term predicted by the GRU-based recurrent neural network. The latter one is tuned in terms of hyper-parameters configuration by genetic algorithm which is embedded into the second stage of the pipeline. Also, a design which combines Nvidia Jetson Nano server with the inference edge device like STM32 H745 microcontroller via sockets is discussed. Key words : edge computing, MLOps, machine learning, Mlflow, genetic algorithm.

An effective data transmission scheme based on IoT system in opportunistic social networks

SummaryWith the advent of the era of Big Data, users are increasingly demanding network bandwidth and data storage in the Internet of Things (IoT) system. While current research on the opportunistic social network does not take the redundant influence of high‐speed data transmission into account, which leads to problems such as lower delivery ratio, we present an effective data transmission scheme based on IoT system (EDTS). To divide the community, which consists of devices with relatively close connections, we measure the social relationship of nodes by quantifying the remaining cache, social relation, and interest similarity in opportunistic social networks. An efficient strategy is designed in this paper to update the community by analyzing the change of edge weights between two nodes in the community, deciding whether the community should be further divided, and whether the nodes should join the larger community. The proposed scheme also involves data transmission methods based on community, which involve inter‐community forwarding and intra‐community forwarding. Simulation results show that EDTS outperforms other comparative algorithms with regard to average delivery ratio, end‐to‐end delay, and routing overhead.

Data Analytics for Energy-Efficient Code Refactoring in Large-Scale Distributed Systems

It examines how data analytics improves energy efficiency in large-scale distributed systems via code reworking. The primary goal is to study how data-driven techniques maximize resource allocation, energy usage, and system performance. Secondary data-based reviews of energy-efficient data analytics case studies from Google, Facebook, AWS, and Microsoft are used in the process. Significant results show that performance profiling, real-time monitoring, predictive modeling, and energy-aware resource management reduce energy use and ensure system scalability and performance. Energy savings were realized utilizing dynamic resource allocation, job scheduling, load balancing, and predictive analytics using machine learning. Energy consumption is also reduced by managing network traffic and data storage. However, integrating contemporary analytics tools into older systems and handling their massive data sets remain substantial obstacles. The paper recommends uniform legislation to promote energy-efficient practices, incentives for sustainable computing research, and industry best practices. This work emphasizes energy efficiency in large-scale distributed systems and advances sustainable computing research.

Research on Distributed Data Sharing System based on Internet of Things and Blockchain

AbstractAiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

True-data testbed for 5G/B5G intelligent network

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting internet of everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and provi di ng hi gh-quality servi ce. As a rapi d evolvi ng paradi gm, the AI-empowered mob i le communi cati ons demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world's first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.

Cloud Computing, Exigency, Issues, Solutions: A survey

In the era of digitalization all over the world, cloud computing is the ubiquitous supreme technology that provides many computer resources and data storage network to the consumers smoothly on-demand. Many researchers of both academics as well as industries are proactively indulge in providing the benefits of cloud computing to the society. As per ray of IT industries is increases drastically also leads in the need of network of dynamic nature. This dynamic and scalable nature of cloud computing creates hurdles in security of data appears in the network. Mainly, three services are the centre of study in cloud computing i.e. Saas, Paas, Iaas. Several security aspects namely multitancy, variability, trouble in data and hackers attacks also focused on priority basis. This paper demonstrate the conceve outlook of latest issues related with cloud computing and the solution of issues. Many authors also convey several methodologies for cloud computing.It is especially recommended to newcomer ensuing their study in field of cloud computing to find the solutions related the issues of cloud computing.

Cellular automata-based digital image scrambling under JPEG compression attack

Communication and storage technologies have greatly advanced in recent years, providing ever increased bandwidths and storage capacities respectively. But, efficient utilization of resources like network bandwidth and data storage is relevant even today. More and more data are generated every unit of time and this enormous data is sent over networks and stored in local and/or cloud storage devices. Compression techniques are used to compress or reduce the size of data so as to make efficient use of these resources. Compression techniques are basically of two types; lossless compression used by applications that require integrity of the data is preserved and lossy compression used by applications in which subtle changes in data either go unnoticed or does not affect the semantics of information. In this paper, we analyze the effect of JPEG lossy compression on scrambled images and propose a cellular automata-based method to recover the quality of de-scrambled images.

Research on Geological Information Collection Based on Data Mining

In order to improve the efficiency of collecting geological data and digitization, the geological data acquisition system based on WorldWind mobile terminal is developed, which integrates WorldWind map technology, network communication technology, multithread technology, and data storage technology. The realization methods of location module, data input module, solid projection module, and 3D display module are expounded. The research and development of this system realizes the information collection, browsing, and display of geological work, provides convenient data input mode for geological workers, and promotes the development of digitization, information, and modernization of geological and mineral work.