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A Method for Sharing English Education Resources in Multiple Virtual Networks Based on 6G 基于6G的多虚拟网络英语教育资源共享方法
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-23 DOI: 10.1002/nem.2319
Hongliu He

The rapid advancement of communication technologies, particularly in English language learning, is sharing education with the implementation of sixth-generation (6G) networks, offering immersive and interactive learning experiences. The purpose of the research is to establish an advanced method for sharing English education resources across multiple virtual networks enabled by 6G technology. Traditional resource-sharing systems lack the effectiveness and optimization requirement for large-scale instructional assignments, especially in virtual settings with various user demands. To address this, the study proposed a novel Dynamic Tunicate Swarm Refined Graph Neural Networks (DTS-RGNN) model to optimize resource allocation and improve the efficiency of resource sharing among educational tasks. The approach uses TSO for resource allocation scalable through 6G technology and GNN for task assignment according to the previous performances and interaction with the students to balance resource utilization. The experimental group performed writing (90%), sharing (91%), listening (85%), and reading (75%), finishing the task in 5.5 s at 1000 GB. Throughput increased by 5.0 GBps and resource utilization efficiency improved to (96%) and student outcomes showed high satisfaction (93%), retention (89%), and engagement (90%). The findings demonstrated the proposed method significantly improves the sharing of online English education resources, promoting more interactive and effective language learning experiences in virtual networks.

通信技术的快速发展,特别是在英语语言学习方面,正在与第六代(6G)网络的实施共享教育,提供身临其境的互动学习体验。本研究的目的是在6G技术的支持下,建立一种跨多个虚拟网络共享英语教育资源的先进方法。传统的资源共享系统缺乏对大规模教学作业的有效性和优化要求,特别是在具有多种用户需求的虚拟环境中。针对这一问题,本研究提出了一种新的动态束状群精细图神经网络(DTS-RGNN)模型来优化资源分配,提高教育任务间资源共享效率。该方法使用TSO进行资源分配,可通过6G技术进行扩展,并根据以往的表现和与学生的互动进行任务分配,以平衡资源利用。实验组进行写作(90%)、分享(91%)、听力(85%)和阅读(75%),在1000 GB的情况下,5.5 s完成任务。吞吐量提高了5.0 GBps,资源利用效率提高到(96%),学生成绩表现出很高的满意度(93%),保留率(89%)和参与度(90%)。研究结果表明,所提出的方法显著提高了在线英语教育资源的共享,促进了虚拟网络中更具互动性和有效性的语言学习体验。
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引用次数: 0
An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent Autoencoder in Cloud Computing 在云计算中利用基因修饰金豺优化和循环自动编码器实现高效工作流调度
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-18 DOI: 10.1002/nem.2318
Saurav Tripathi, Sarsij Tripathi

In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM-GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE-BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM-GJO method for multiworkflow scheduling. GM-GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.

本文提出了一种基于循环自编码器的遗传金豺狼优化(GM-GJO)的工作流调度框架。使用集成的自动编码器和双向门控循环单元(iAE-BiGRU)来预测管理系统当前工作负载所需的虚拟机(vm)数量。下面的步骤是通过使用GM-GJO方法进行多工作流调度,将多个工作流的任务分配给云虚拟机。GM-GJO通过考虑最小化利用率、最小化完工时间和最小化错过截止日期的工作流数量来提供最佳工作流调度。所建议的方法试图根据截止日期、成本和服务质量(QoS)等目标为工作流分配尽可能好的资源集。使用CloudSIM工具进行了大量实验,并从调度长度比、成本、QoS等方面对性能进行了评估。513.45 ms的执行时间是在包含30个任务的Sipht工作流中实现的。当将建议的策略与当前的方法进行比较时,建议的方法性能更好。
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引用次数: 0
Security Protection Method for Electronic Archives Based on Homomorphic Aggregation Signature Scheme in Mobile Network 移动网络中基于同态聚合签名方案的电子档案安全保护方法
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-18 DOI: 10.1002/nem.2316
Junwei Li, Huaquan Su, Li Guo, Wanshuo Wang, Yongjiao Yang, You Wen, Kai Li, Pingyan Mo

Electronic archives are now widely used in many different industries and serve as the primary method of information management and storage because of the rapid growth of information technology and mobile networks. To enhance the security of electronic archives in mobile networks, the research utilizes the federated learning mechanism to design a federated learning model based on homomorphic aggregation cryptographic signature scheme combined with mobile network management. The use of homomorphic encryption technology in the signing process of electronic archives enables the aggregation of multiple electronic file signatures into a single signature without exposing the data of the electronic archives. This reduces the computational and storage requirements for signature verification. At the same time, a secure aggregation signature scheme is used to ensure the integrity and security of the data in the aggregation process. A novel approach is presented in this study, whereby trusted federated learning models are innovatively combined with homomorphic aggregate signature technology. This integration ensures data integrity through aggregate signature schemes. The results showed that, under mobile network management, the longest encryption time of the trusted federated learning model was 52 ms, and the longest decryption time was 44 ms. The accuracy of the optimized learning model reached 97.49%, and the loss value was significantly reduced to 0.09. To summarize, the electronic archive security protection method based on homomorphic aggregation signature scheme effectively improves the archive data protection efficiency and security.

由于信息技术和移动网络的快速发展,电子档案已广泛应用于许多不同的行业,并成为信息管理和存储的主要方法。为了提高移动网络中电子档案的安全性,本研究利用联邦学习机制,结合移动网络管理,设计了一种基于同态聚合密码签名方案的联邦学习模型。在电子档案签名过程中使用同态加密技术,可以在不暴露电子档案数据的情况下,将多个电子文件签名聚合为一个签名。这减少了签名验证的计算和存储需求。同时,采用安全的聚合签名方案,保证了聚合过程中数据的完整性和安全性。本研究提出了一种新颖的方法,将可信联邦学习模型与同态聚合签名技术创新地结合起来。这种集成通过聚合签名方案确保了数据的完整性。结果表明,在移动网络管理下,可信联邦学习模型的最长加密时间为52 ms,最长解密时间为44 ms。优化后的学习模型准确率达到97.49%,损失值显著降低至0.09。综上所述,基于同态聚合签名方案的电子档案安全防护方法有效地提高了档案数据的防护效率和安全性。
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引用次数: 0
REMEDIATE: Improving Network and Middlebox Resilience With Virtualisation 补救:通过虚拟化提高网络和中间件的弹性
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-03 DOI: 10.1002/nem.2317
Lyn Hill, Charalampos Rotsos, Chris Edwards, David Hutchison

The increasing demand for low-latency, high-bandwidth connectivity has introduced novel challenges to delivering strong resilience guarantees in production network environments. Closed hardware platforms, known as middleboxes, that lack visibility and support for state retention remain a key challenge for continuous service delivery during network failures. These middleboxes rarely employ recovery mechanisms of their own, inspiring renewed interest in the field of NFV in recent years due to this gap within the industry. The increasing availability of VNF capabilities in modern infrastructures offers an opportunity to exploit the flexibility of software and use hybrid architectures to improve resilience. REMEDIATE is a high-availability service that propagates state between unmodified hardware middleboxes and backup PNF or VNF appliances. The platform utilises targeted packet mirroring to allow network devices to organically construct equivalent state and thus allow an easy transition between hardware and software. To demonstrate its viability, we have evaluated REMEDIATE against a wide range of common hardware middlebox use cases built using multiple open-source packet processing frameworks. Results show upwards of 90% matching state with no observable delay to normal traffic or impact on its functionality.

对低延迟、高带宽连接的需求不断增长,为在生产网络环境中提供强大的弹性保证带来了新的挑战。封闭的硬件平台(称为中间件)缺乏可见性和对状态保留的支持,这对网络故障期间的持续服务交付仍然是一个关键挑战。这些中间设备很少采用自己的恢复机制,由于行业内的差距,近年来激发了人们对NFV领域的兴趣。在现代基础设施中,VNF功能的可用性越来越高,这为利用软件的灵活性和使用混合架构来提高弹性提供了机会。REMEDIATE是一个高可用性服务,它在未修改的硬件中间件和备份PNF或VNF设备之间传播状态。该平台利用目标包镜像,允许网络设备有机地构建等效状态,从而实现硬件和软件之间的轻松转换。为了证明它的可行性,我们针对使用多个开源包处理框架构建的广泛的通用硬件中间件用例对REMEDIATE进行了评估。结果显示90%以上的匹配状态,没有观察到对正常交通的延迟或对其功能的影响。
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引用次数: 0
Spectrum Allocation in 5G and Beyond Intelligent Ubiquitous Networks 5G及超越智能泛在网络的频谱分配
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-29 DOI: 10.1002/nem.2315
Banoth Ravi, Utkarsh Verma

Effective spectrum allocation in 5G and beyond intelligent ubiquitous networks is vital for predicting future frequency band needs and ensuring optimal network performance. As wireless communication evolves from 4G to 5G and beyond, it has brought about remarkable advancements in speed and connectivity. However, with the growing demand for higher data rates and increased network capacity, new challenges in managing and utilizing network frequencies have emerged. Accurately forecasting spectrum requirements is critical to addressing these challenges. This research explores how machine learning (ML) plays a pivotal role in optimizing network performance through intelligent decision-making, predictive analysis, and adaptive management of network resources. By leveraging ML algorithms, networks can autonomously self-optimize in real time, adjusting to changing conditions and improving performance in 5G and beyond. The effectiveness of our approach was demonstrated through an extensive case study, which showed that it not only meets spectrum requirements in various environments but also significantly reduces energy consumption by pinpointing the appropriate spectrum range for each location. These results underscore the approach's potential for enhancing spectrum management in future networks, offering a scalable and efficient solution to the challenges facing 5G and beyond.

5G及智能泛在网络之后的有效频谱分配对于预测未来频带需求和确保最佳网络性能至关重要。随着无线通信从4G演进到5G及以后,它在速度和连接方面带来了显着的进步。然而,随着对更高数据速率和网络容量的需求不断增长,在管理和利用网络频率方面出现了新的挑战。准确预测频谱需求对于应对这些挑战至关重要。本研究探讨了机器学习(ML)如何通过智能决策、预测分析和网络资源的自适应管理在优化网络性能方面发挥关键作用。通过利用机器学习算法,网络可以实时自主自我优化,适应不断变化的条件,提高5G及以后的性能。通过广泛的案例研究证明了我们方法的有效性,该方法不仅满足了各种环境下的频谱要求,而且通过精确定位每个位置的适当频谱范围,显著降低了能耗。这些结果强调了该方法在增强未来网络频谱管理方面的潜力,为5G及以后面临的挑战提供了可扩展和高效的解决方案。
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引用次数: 0
Intent-Based Network Configuration Using Large Language Models 使用大型语言模型进行基于意图的网络配置
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-20 DOI: 10.1002/nem.2313
Nguyen Tu, Sukhyun Nam, James Won-Ki Hong

The increasing scale and complexity of network infrastructure present a huge challenge for network operators and administrators in performing network configuration and management tasks. Intent-based networking has emerged as a solution to simplify the configuration and management of networks. However, one of the most difficult tasks of intent-based networking is correctly translating high-level natural language intents into low-level network configurations. In this paper, we propose a general and effective approach to perform the network intent translation task using large language models with fine-tuning, dynamic in-context learning, and continuous learning. Fine-tuning allows a pretrained large language model to perform better on a specific task. In-context learning enables large language models to learn from the examples provided along with the actual intent. Continuous learning allows the system to improve overtime with new user intents. To demonstrate the feasibility of our approach, we present and evaluate it with two use cases: network formal specification translation and network function virtualization configuration. Our evaluation shows that with the proposed approach, we can achieve high intent translation accuracy as well as fast processing times using small large language models that can run on a single consumer-grade GPU.

网络基础设施的规模和复杂性不断增加,给网络运营商和管理员执行网络配置和管理任务带来了巨大挑战。基于意图的网络已成为简化网络配置和管理的一种解决方案。然而,基于意图的联网最困难的任务之一是正确地将高级自然语言意图转化为低级网络配置。在本文中,我们提出了一种通用而有效的方法,利用具有微调、动态上下文学习和持续学习功能的大型语言模型来完成网络意图翻译任务。微调可以使预先训练好的大型语言模型在特定任务中发挥更好的作用。上下文学习使大型语言模型能够从提供的示例和实际意图中学习。持续学习允许系统根据新的用户意图不断改进。为了证明我们的方法的可行性,我们介绍并评估了两个使用案例:网络形式规范翻译和网络功能虚拟化配置。我们的评估结果表明,利用所提出的方法,我们可以实现较高的意图翻译准确率,并利用可在单个消费级 GPU 上运行的小型大型语言模型实现快速处理。
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引用次数: 0
MSC: A Unique Chameleon Hash-Based Off-Chain Storage Framework for Metaverse Applications MSC:一个独特的变色龙基于哈希的链下存储框架,用于元宇宙应用程序
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-14 DOI: 10.1002/nem.2314
Chenxi Xiong, Ting Yang, Gang Mao

Blockchain has evolved into a secure and trustworthy environment for decentralized applications, offering the advantages of tamper-resistant, while simultaneously introducing on-chain overhead issues. The development of metaverse related smart contracts on blockchain has given rise to a compelling research inquiry concerning the secure reduction of on-chain storage overhead. In this research, the Metaverse Off-chain Storage Framework based on Chameleon hash (MSC), a unique framework for decentralized system based on chameleon hash, supports code or stored data updates without changing on-chain data is presented. The index of decentralized applications' data is calculated using the Chameleon hash to ensure that the index remains unchanged during the data modification process. Simultaneously, data can be stored outside of the blockchain with proper authentication mechanisms in place. The experimental results have shown that MSC exhibits reduced on-chain storage requirements when compared to similar frameworks. Furthermore, MSC significantly reduced overhead as compared to the direct storage of data within a smart contract.

区块链已发展成为去中心化应用的安全可信环境,具有防篡改的优势,但同时也带来了链上开销问题。区块链上元宇宙相关智能合约的发展,引发了有关安全减少链上存储开销的迫切研究探索。本研究提出了基于变色龙哈希的元宇宙链外存储框架(MSC),这是一种独特的基于变色龙哈希的去中心化系统框架,支持代码或存储数据更新而不改变链上数据。去中心化应用程序的数据索引使用变色龙哈希计算,以确保在数据修改过程中索引保持不变。同时,数据可以通过适当的认证机制存储在区块链之外。实验结果表明,与同类框架相比,MSC 可减少链上存储需求。此外,与在智能合约中直接存储数据相比,MSC 大大减少了开销。
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引用次数: 0
SentinelGuard Pro: Deploying Cutting-Edge FusionNet for Unerring Detection and Enforcement of Wrong Parking Incidents SentinelGuard Pro:部署先进的 FusionNet,准确无误地检测和执行错误停车事件
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-10 DOI: 10.1002/nem.2310
Vankadhara Rajyalakshmi, Kuruva Lakshmanna

Wrong parking incidents pose a pervasive challenge in urban environments, disrupting the smooth flow of traffic, compromising safety and contributing to various logistical issues. Unauthorized parking occurs when vehicles are parked in locations not designated for such purposes, leading to a myriad of problems for both authorities and the general public. This research introduces a pioneering approach to confront the persistent challenge of unauthorized parking incidents in urban environments. The study focuses on harnessing the advanced capabilities of the FusionNet model to enhance the accuracy of license plate detection. This paper introduces the YOLO v8 Model, a deep learning architecture designed to enhance urban parking management by accurately detecting vehicles parked in unauthorized slots. The objective is to enhance parking management efficiency by accurately detecting vehicles and their occupancy status in designated parking areas. The methodology begins with data collection and preprocessing of images of parking spaces, followed by the training of YOLO v8 to identify vehicles and parking spaces in real time. Leveraging a diverse dataset encompassing various parking scenarios, including instances of unauthorized parking, the model achieves an accuracy of 98.50% in identifying vehicles outside designated areas. This model segments characters from detected license plates, enabling the accurate extraction of alphanumeric information associated with each vehicle. The integrated system provides timely identification of parking violations and facilitates effective enforcement actions through captured license plate data. Results demonstrate the model's effectiveness in real-world scenarios, showcasing its potential for improving urban safety and efficiency. The implementation of FusionNet in the Python programming language, the proposed solution aims to streamline parking management, improve compliance with parking regulations and enhance overall urban mobility., with robust precision 96.17%, specificity 97.42% and sensitivity 96.19%, surpassing other MobileNet, CNN, ANN, DNN and EfficientNet models.

错误停车事件是城市环境中普遍存在的挑战,它扰乱了交通的顺畅流动,损害了安全,并导致各种后勤问题。当车辆停放在未指定的地点时,就会发生违章停车,从而给当局和公众带来无数问题。本研究采用一种开创性的方法来应对城市环境中长期存在的违章停车问题。研究重点是利用 FusionNet 模型的先进功能来提高车牌检测的准确性。本文介绍了 YOLO v8 模型,这是一种深度学习架构,旨在通过准确检测停放在未经授权停车位上的车辆来加强城市停车管理。其目的是通过准确检测指定停车区域内的车辆及其占用状态来提高停车管理效率。该方法首先对停车位图像进行数据收集和预处理,然后训练 YOLO v8 实时识别车辆和停车位。利用包含各种停车场景(包括未经授权的停车情况)的多样化数据集,该模型在识别指定区域外的车辆方面达到了 98.50% 的准确率。该模型可从检测到的车牌中分割字符,从而准确提取与每辆车相关的字母数字信息。集成系统可及时识别违章停车行为,并通过捕获的车牌数据促进有效的执法行动。研究结果证明了该模型在实际场景中的有效性,展示了其在提高城市安全和效率方面的潜力。通过在 Python 编程语言中实施 FusionNet,所提出的解决方案旨在简化停车管理、提高停车法规的合规性并增强城市的整体流动性。
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引用次数: 0
Massive Data HBase Storage Method for Electronic Archive Management 用于电子档案管理的海量数据 HBase 存储方法
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-10-27 DOI: 10.1002/nem.2308
Huaquan Su, Junwei Li, Li Guo, Wanshuo Wang, Yongjiao Yang, You Wen, Kai Li, Pingyan Mo

The acceleration of the digitalization process in enterprise and university education management has generated a massive amount of electronic archive data. In order to improve the intelligence, storage quality, and efficiency of electronic records management and achieve efficient storage and fast retrieval of data storage models, this study proposes a massive data storage model based on HBase and its retrieval optimization scheme design. In addition, HDFS is introduced to construct a two-level storage structure and optimize values to improve the scalability and load balancing of HBase, and the retrieval efficiency of the HBase storage model is improved through SL-TCR and BF filters. The results indicated that HDFS could automatically recover data after node, network partition, and NameNode failures. The write time of HBase was 56 s, which was 132 and 246 s less than Cassandra and CockroachDB. The query latency was reduced by 23% and 32%, and the query time was reduced by 9988.51 ms, demonstrating high reliability and efficiency. The delay of BF-SL-TCL was 1379.28 s after 1000 searches, which was 224.78 and 212.74 s less than SL-TCL and Blockchain Retrieval Acceleration and reduced the delay under high search times. In summary, this storage model has obvious advantages in storing massive amounts of electronic archive data and has high security and retrieval efficiency, which provides important reference for the design of storage models for future electronic archive management. The storage model designed by the research institute has obvious advantages in storing massive electronic archive data, solving the problem of lack of scalability in electronic archive management when facing massive data, and has high security and retrieval efficiency. It has important reference for the design of storage models for future electronic archive management.

随着企业和高校教育管理数字化进程的加快,产生了海量的电子档案数据。为了提高电子档案管理的智能化、存储质量和效率,实现高效存储、快速检索的数据存储模型,本研究提出了基于HBase的海量数据存储模型及其检索优化方案设计。此外,引入 HDFS 构建两级存储结构并进行优化取值,以提高 HBase 的可扩展性和负载均衡性,并通过 SL-TCR 和 BF 过滤器提高 HBase 存储模型的检索效率。结果表明,HDFS能在节点、网络分区和NameNode故障后自动恢复数据。HBase 的写入时间为 56 秒,分别比 Cassandra 和 CockroachDB 短 132 秒和 246 秒。查询延迟分别减少了 23% 和 32%,查询时间减少了 9988.51 毫秒,表现出很高的可靠性和效率。BF-SL-TCL在1000次搜索后的延迟为1379.28 s,比SL-TCL和区块链检索加速分别减少了224.78和212.74 s,减少了高搜索次数下的延迟。综上所述,该存储模型在存储海量电子档案数据方面优势明显,具有较高的安全性和检索效率,为未来电子档案管理的存储模型设计提供了重要参考。该研究所设计的存储模型在存储海量电子档案数据方面具有明显优势,解决了电子档案管理在面对海量数据时缺乏可扩展性的问题,具有较高的安全性和检索效率。它对未来电子档案管理的存储模型设计具有重要的借鉴意义。
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引用次数: 0
The Role of Network Centralization in Shaping Digital Sovereignty: An Analysis Under the DNS Lens 网络集中化在塑造数字主权中的作用:DNS 透视镜下的分析
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-10-24 DOI: 10.1002/nem.2309
Andrei C. Azevedo, Eder J. Scheid, Muriel F. Franco, Demétrio F. F. Boeira, Luciano Zembruzki, Lisandro Z. Granville

Centralization of Internet-based services in a few key players has been a topic of study in recent years. One of such services, the domain name system (DNS), is one of the pillars of the Internet, which allows users to access websites on the Internet through easy-to-remember domain names rather than complex numeric IP addresses. In this DNS context, the reliance on a small number of large DNS providers can lead to (a) risks of data breaches and disruption of service in the event of failures and (b) concerns about the digital sovereignty of countries regarding DNS hosting. As several essential services are provided through electronic government (E-Gov), it is highly important to be able to measure the digital sovereignty of a nation and the impacts that the lack of such feature can bring to its citizens. This work approaches the issue of DNS concentration on the Internet by presenting a solution to measure DNS hosting centralization and digital sovereignty in different countries, such as Brazil, India, China, Russia, and South Africa. With the data obtained through these measurements, relevant questions are answered, such as which are the top-10 DNS providers, if there is DNS centralization, and how dependent countries are on such providers to manage domains using their country code top-level domains (ccTLD). Future opportunities could investigate the impacts on sovereignty under the lens of other layers of the open systems interconnection (OSI) Network Sovereignty representation model presented in this work.

近年来,将基于互联网的服务集中到少数关键参与者手中一直是一个研究课题。域名系统(DNS)是此类服务之一,也是互联网的支柱之一,它允许用户通过易于记忆的域名而不是复杂的数字 IP 地址访问互联网上的网站。在域名系统方面,对少数大型域名系统提供商的依赖可能导致:(a) 数据外泄和服务中断 的风险,(b) 各国在域名系统托管方面的数字主权问题。由于一些基本服务是通过电子政务(E-Gov)提供的,因此能够衡量一个国家的数字主权以及缺乏这种功能可能给其公民带来的影响是非常重要的。这项工作通过提出一种解决方案来衡量 DNS 主机集中化和不同国家(如巴西、印度、中国、俄罗斯和南非)的数字主权,从而解决互联网上 DNS 集中化的问题。通过这些测量获得的数据,可以回答相关问题,如哪些是排名前十的 DNS 提供商,是否存在 DNS 集中化,以及各国在使用其国家代码顶级域 (ccTLD) 管理域名时对这些提供商的依赖程度。未来有机会可以从本研究中提出的开放系统互连 (OSI) 网络主权表示模型的其他层面来研究对主权的影响。
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引用次数: 0
期刊
International Journal of Network Management
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