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Special Collection on Advances in Quantum Computing: Methods, Algorithms, and Systems 量子计算进展特辑:方法、算法和系统
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-30 DOI: 10.1016/j.future.2024.107503
Stefano Markidis, Michela Taufer, Lucio Grandinetti
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引用次数: 0
CSMD: Container state management for deployment in cloud data centers CSMD:用于云数据中心部署的容器状态管理
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-28 DOI: 10.1016/j.future.2024.107495
Shubha Brata Nath , Sourav Kanti Addya , Sandip Chakraborty , Soumya K. Ghosh

As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance. To increase the consolidation ratio of a cloud data center, in this paper, we propose a system called Container State Management for Deployment (CSMD) to manage the container states. CSMD uses an algorithm to checkpoint the idle containers so that their resources can be released. The new containers are deployed using the released resources in a server. In addition, CSMD uses an algorithm to check the container status periodically, and the containers are resumed from the checkpoint state when the user requests them. We evaluate CSMD in Amazon Elastic Compute Cloud (Amazon EC2) by performing efficient state management of the containers. The experiments in the Amazon cloud show that the proposed CSMD system is superior to the existing algorithms as the proposed system increases the consolidation ratio of data centers.

由于容器在资源使用方面是轻量级的,因此是云计算和边缘计算服务部署的首选。只要用户发送查询,容器就会为请求提供服务;但当没有用户请求时,容器就会处于闲置状态。同样,提高容器部署的整合率对于确保在云数据中心使用更少的服务器并实现最佳资源平衡至关重要。为了提高云数据中心的整合率,我们在本文中提出了一种名为 "部署容器状态管理"(CSMD)的系统来管理容器状态。CSMD 使用一种算法对闲置容器进行检查点,以便释放其资源。新容器将使用服务器中释放的资源进行部署。此外,CSMD 还使用一种算法定期检查容器状态,并在用户请求时从检查点状态恢复容器。我们在亚马逊弹性计算云(Amazon EC2)中对 CSMD 进行了评估,对容器进行了有效的状态管理。在亚马逊云中的实验表明,拟议的 CSMD 系统优于现有算法,因为拟议的系统提高了数据中心的整合率。
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引用次数: 0
VLR-BPP: An intelligent virtual location replacement based bilateral privacy-preserving architecture for edge cloud systems VLR-BPP:基于双边隐私保护架构的边缘云系统智能虚拟位置替换技术
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-28 DOI: 10.1016/j.future.2024.107488
Bochang Yang , Anfeng Liu , Neal N. Xiong , Tian Wang , Shaobo Zhang

Mobile Crowdsourcing (MCS) has emerged as a significant edge-cloud computing paradigm in which workers perceive data at the network edge and report it to cloud-based computing services for processing, enabling the construction of various applications. Consequently, it is imperative to achieve Bilateral Location Privacy-Preserving (BLPP) to protect the privacy of both Data Requester (DR) and workers, as disclosing location information entails many sensitive details that can result in losses for DR and workers alike. The Local Differential Privacy (LDP) approach is widely employed in Privacy-Preserving (PP) techniques due to its inherent advantages, wherein owners release data with added noise, allowing for proactive customization of privacy strength without relying on any third party. However, the current state of LDP methods presents a dilemma: when privacy protection is strong, introducing excessive location noise can lead to a decrease in the accuracy of task-worker matching, while a high rate of task-worker matching necessitates the compromise of privacy strength. In this paper, an intelligent Virtual Location Replacement based enhanced Bilateral Privacy-Preserving (VLR-BPP) architecture is proposed to improve privacy protection strength and matching accuracy in MCS simultaneously. Within the VLR-BPP architecture, a Bipartite-Graph-based Matrix Completion (BGMC) model is employed to establish the spatiotemporal correlations among data. Then, a Virtual Location Replacement (VLR) strategy is proposed to obfuscate the locations of tasks or workers to their highly correlated virtual location before publishing. Based on VLR, three preemptive location virtualization approaches are introduced: Only Task Location Virtual (OTLV), Only Workers Location Virtual (OWLV), and Both Task and Workers Location Virtual (BTWLV). For workers and DR, Randomized Response (RR) techniques and Random Matrix Multiplication Mechanism (RMM) are used to implement LDP independently. A greedy algorithm is adopted to recruit workers for tasks. In response to the data submitted by workers, BGMC imputation mechanism is utilized to enhance data quality. Finally, simulations based on real-world datasets demonstrate that the performance of our architecture surpasses existing state-of-the-art methods in privacy protection and data collection quality by 18.92∼38.17% and 15.49∼50.77%, respectively.

移动众包(MCS)已成为一种重要的边缘云计算模式,工人在网络边缘感知数据并将其报告给云计算服务进行处理,从而构建各种应用。因此,实现双边位置隐私保护(Bilateral Location Privacy-Preserving,BLPP)以保护数据请求者(DR)和工作人员的隐私势在必行,因为泄露位置信息会涉及许多敏感细节,可能会给数据请求者和工作人员带来损失。本地差分隐私(LDP)方法因其固有的优势而被广泛应用于隐私保护(PP)技术中,在这种方法中,数据所有者在发布数据时会增加噪音,从而可以主动定制隐私强度,而无需依赖任何第三方。然而,LDP 方法的现状却让人进退两难:当隐私保护强度较高时,引入过多的位置噪声会导致任务-工作者匹配的准确性降低,而任务-工作者匹配率较高时,又必须牺牲隐私强度。本文提出了一种基于虚拟位置替换的增强型双边隐私保护(VLR-BPP)智能架构,以同时提高 MCS 中的隐私保护强度和匹配精度。在 VLR-BPP 架构中,采用了基于双方格图的矩阵补全(BGMC)模型来建立数据之间的时空相关性。然后,提出了虚拟位置替换(VLR)策略,在发布前将任务或工人的位置混淆为高度相关的虚拟位置。在 VLR 的基础上,引入了三种抢先位置虚拟化方法:仅任务位置虚拟化(OTLV)、仅工人位置虚拟化(OWLV)和任务与工人位置虚拟化(BTWLV)。对于工人和 DR,采用随机响应(RR)技术和随机矩阵乘法机制(RMM)来独立实现 LDP。采用贪婪算法为任务招募工人。针对工人提交的数据,采用 BGMC 估算机制来提高数据质量。最后,基于真实数据集的仿真表明,我们的架构在隐私保护和数据收集质量方面的性能分别比现有的最先进方法高出 18.92∼38.17% 和 15.49∼50.77%。
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引用次数: 0
HashGrid: An optimized architecture for accelerating graph computing on FPGAs HashGrid:在 FPGA 上加速图计算的优化架构
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-28 DOI: 10.1016/j.future.2024.107497
Amin Sahebi , Marco Procaccini , Roberto Giorgi

Large-scale graph processing poses challenges due to its size and irregular memory access patterns, causing performance degradation in common architectures, such as CPUs and GPUs. Recent research includes accelerating graph processing using Field Programmable Gate Arrays (FPGAs). FPGAs can provide very efficient acceleration thanks to reconfigurable on-chip resources. Although limited, these resources offer a larger design space than CPUs and GPUs.

We propose an approach in which data are preprocessed in small chunks with an optimized graph partitioning technique for execution on FPGA accelerators. The chunks, located on the host, are streamed directly into a customized memory layer implemented in the FPGA, which is tightly coupled with the processing elements responsible for the graph algorithm execution. This improves application memory access latency, which is crucial in large-sale graph computing performance.

This work presents a hardware design that, combined with graph partitioning, enables us to achieve high-performance and potentially scalable handling of large graphs (i.e., graphs with millions of vertices and billions of edges in current scenarios) while using popular graph algorithms. The proposed framework accelerates performance 56 times compared with CPU (multicore with 16 logical cores in our reference experiments), 2.5 times and 4 times faster compared to state-of-the-art FPGA and GPU solutions (FPGA has 15 compute units, and GPU reference has 128 streaming-multiprocessors in our experiments), respectively, when using the PageRank algorithm. For the Single-Source-Shortest-Past (SSSP) algorithm, we achieve speedups of up to 65x, 26x, and 18x compared to CPU, GPU, and FPGA works, respectively. Lastly, in the context of the Weakly Connected Component (WCC) algorithm, our framework achieves a speedup of up to 403 times compared to the CPU, 7.4x against the GPU, and it is faster than the FPGA alternatives up to 10.3x.

大规模图形处理因其规模和不规则的内存访问模式而面临挑战,导致 CPU 和 GPU 等常见架构的性能下降。最近的研究包括利用现场可编程门阵列(FPGA)加速图处理。FPGA 具有可重新配置的片上资源,因此可以提供非常高效的加速。我们提出了一种方法,利用优化的图形分割技术,将数据分成小块进行预处理,以便在 FPGA 加速器上执行。位于主机上的数据块直接流向 FPGA 中的定制内存层,该内存层与负责图形算法执行的处理元件紧密耦合。这项工作提出了一种硬件设计,它与图分区相结合,使我们能够在使用流行图算法的同时,实现对大型图(即当前场景中具有数百万顶点和数十亿条边的图)的高性能和潜在可扩展处理。在使用 PageRank 算法时,与最先进的 FPGA 和 GPU 解决方案(在我们的实验中,FPGA 有 15 个计算单元,GPU 参考有 128 个流式多核处理器)相比,所提出的框架将性能分别提高了 56 倍、2.5 倍和 4 倍。在单源最短过去算法(SSSP)方面,与 CPU、GPU 和 FPGA 相比,我们的速度分别提高了 65 倍、26 倍和 18 倍。最后,在弱连接成分(WCC)算法方面,我们的框架与 CPU 相比提速达 403 倍,与 GPU 相比提速达 7.4 倍,与 FPGA 相比提速达 10.3 倍。
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引用次数: 0
Big Data-driven MLOps workflow for annual high-resolution land cover classification models 大数据驱动的年度高分辨率土地覆被分类模型 MLOps 工作流程
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-28 DOI: 10.1016/j.future.2024.107499
Antonio M. Burgueño-Romero, Cristóbal Barba-González, José F. Aldana-Montes

Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the maintenance of these models, a feature particularly beneficial in systems that require generating new models each year alongside the continuous integration of validated data. This article details the development of an end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.

绘制年度和全球高分辨率土地覆被图是遥感领域最雄心勃勃的任务之一,其重要性因验证数据和卫星图像的不断增加而与日俱增。土地覆被分类模型的成功在很大程度上取决于数据质量,以及大数据技术和分布式计算的应用。这对于高效处理大量可用卫星数据至关重要。然而,维持多个年度机器学习模型的生命周期是一项复杂的挑战。机器学习运维的兴起为自动维护这些模型提供了机会,这一功能对于需要每年生成新模型并持续集成已验证数据的系统尤为有益。本文详细介绍了端到端 MLOps 工作流的开发过程,该工作流精心整合了采用大数据策略处理大规模、高分辨率空间数据的土地覆被分类模型。该工作流在 Kubernetes 环境中设计,实现了按需自动扩展、分布式计算和负载平衡。这一集成展示了在一个自动化、可扩展的框架中管理和部署处理卫星图像的模型的实用性和效率,从而标志着遥感和 MLOps 的重大进步。
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引用次数: 0
A multi-level IIOT platform for boosting mines digitalization 促进矿山数字化的多层次 IIOT 平台
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-27 DOI: 10.1016/j.future.2024.107501
Raúl Miñón , Juan López-de-Armentia , Lander Bonilla , Aitor Brazaola , Ibai Laña , M. Carmen Palacios , Szymon Mueller , Michal Blaszczak , Herwig Zeiner , Julia Tschuden , Mohammad Yusuf Quadri , Ignasi Garcia-Milà , Andrea Bartoli , Norbert Gormolla , Alberto Fernández , Pablo Segarra , José A. Sanchidrián , Philipp Hartlieb

This paper presents an innovative IIoT multi-level platform tailored to address the specific needs of the mining domain. The platform has been conceptualized and built in the context of the illuMINEation European project. For this purpose, mining specific use cases have been designed such as promoting underground safe areas, performing efficient mining operations or boosting predictive maintenance approaches. Then, specific requirements have been identified and, as a result, the platform has been developed. It consists of four-level layered platform: (1) edge devices layer to manage several sensors deployed in the mines; (2) edge box layer to provide in-mine operations such as filtering, streaming and processing; (3) fog layer which offers an overall perspective of each mine; and (4) cloud layer to centralize the data of all the mines and to provide powerful processing capabilities. In addition, the platform is robustly secured in terms of protecting communications confidentiality and access control and also provides a toolbox aimed at manipulating 3D complex images to obtain operable mine-domain novel user interfaces. Finally, a platform validation is proposed where three different use cases are explained to better show and demonstrate the capabilities of the platform.

本文介绍了一个创新的物联网多层次平台,该平台专为满足采矿领域的特殊需求而量身定制。该平台是在 illuMINEation 欧洲项目的背景下构思和构建的。为此,设计了采矿业的特定用例,如促进地下安全区域、执行高效采矿作业或促进预测性维护方法。然后,确定了具体要求,并由此开发了该平台。它由四层平台组成:(1) 边缘设备层,用于管理部署在矿井中的多个传感器;(2) 边缘盒层,提供矿井内操作,如过滤、流媒体和处理;(3) 雾层,提供每个矿井的整体视角;(4) 云层,集中所有矿井的数据,并提供强大的处理能力。此外,该平台在保护通信保密性和访问控制方面具有强大的安全性,还提供了一个工具箱,旨在处理三维复杂图像,以获得可操作的矿区新用户界面。最后,提出了一个平台验证方案,对三个不同的使用案例进行了说明,以更好地展示和证明该平台的能力。
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引用次数: 0
Global reduction for geo-distributed MapReduce across cloud federation 跨云联盟的地理分布式 MapReduce 全局缩减
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107492
Thouraya Gouasmi , Ahmed Hadj Kacem

Geo-distributed Bigdata processing is increasing day by day, resulting in the origins of data that are geographically distributed in different countries and hold datacenters (DCs) across the globe, and also the applications that use different sites to increase reliability, security, and processing performances. Most popular frameworks like Hadoop and Spark are re-designed to process geographically distributed data at their locations. However, these methods still suffer from a large amount of data transfer over the Internet, which prohibits a high processing time and cost for many applications, and in several cases, the output results of the computation are smaller than its inputs. In this paper, we keep the data locality principle for processing data at different locations but ignore the principle of transferring the entire intermediate results to a single global reducer. We propose Geo-MR, an intelligent geo-distributed MapReduce-based framework across federated cloud based on two heuristic algorithms: (i) chosen the best clusters as global reducers to reduce the communication and optimize the transfer on the bandwidth, GResearch. (ii) The second, Geo-MR, ensures the scheduling of only the relevant data to selected global reducers that process the final results. As a baseline, we propose an exact MapReduce scheduling model for benchmarking and to compare and discuss the Geo-MR heuristic algorithm results. The experimental results show that the proposed algorithm Geo-MR can improve resource (bandwidth and VMs of clusters) utilization of the cloud federation and consequently reduce cost and job response time.

地理分布式大数据处理与日俱增,导致数据的来源在地理上分布在不同的国家,数据中心(DC)遍布全球,而且应用程序使用不同的站点来提高可靠性、安全性和处理性能。大多数流行的框架(如 Hadoop 和 Spark)都经过重新设计,可在不同地点处理地理分布数据。然而,这些方法仍然存在通过互联网传输大量数据的问题,这使得许多应用无法获得较高的处理时间和成本,而且在某些情况下,计算的输出结果比输入结果要小。在本文中,我们保留了在不同地点处理数据的数据本地性原则,但忽略了将整个中间结果传输到单个全局还原器的原则。我们提出了基于两种启发式算法的跨联盟云的智能地理分布式 MapReduce 框架--Geo-MR:(i) 选择最佳集群作为全局还原器,以减少通信并优化带宽上的传输,即 GResearch。(ii) 第二种是 Geo-MR,确保只将相关数据调度给处理最终结果的选定全局还原器。作为基准,我们提出了一个精确的 MapReduce 调度模型,用于基准测试,并比较和讨论 Geo-MR 启发式算法的结果。实验结果表明,所提出的 Geo-MR 算法可以提高云联盟的资源(带宽和集群虚拟机)利用率,从而降低成本并缩短作业响应时间。
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引用次数: 0
Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence 战术 6G 网络的网络安全:威胁、架构和情报
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107500
Jani Suomalainen , Ijaz Ahmad , Annette Shajan , Tapio Savunen

Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.

边缘智能、网络自治、宽带卫星连接和其他专用 6G 网络概念正在为公共安全机构(如警察和救援人员)带来新的应用。丰富的态势感知、带有高质量视频的群组通信、大规模物联网以及车辆和机器人的远程控制将在任何地点和情况下可用。我们分析了智能战术气泡中的网络安全,即公共安全行动中的自主快速部署移动网络。机器学习在使这些网络能够快速协调不同行动、保护这些网络免受新兴威胁以及扩大威胁范围方面发挥着重要作用。我们探索了不同威胁和风险分析方法在关键任务网络应用中的适用性。我们介绍了联合风险优先级研究的结果。我们对安全解决方案进行了调查,并提出了一个安全架构,该架构建立在当前地面和非地面 6G 标准化活动的基础上,并利用基于机器学习的安全概念来保护网络边缘的关键任务资产。
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引用次数: 0
Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network 基于能量收集的线性无线传感器网络中的有限地平线能量分配方案
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107493
Shengbo Chen , Shuai Li , Guanghui Wang , Keping Yu

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

线性无线传感器网络(LWSN)是无线传感器网络(WSN)的一种特殊拓扑结构,广泛用于环境监测。传统的 WSN 依靠电池提供能量,由于电池容量的限制,其性能受到限制,而可再生能源采集技术是缓解电池容量瓶颈的有效方法。然而,可再生能源的随机性使得设计一种有效的能源管理方案来提高网络性能成为一个迫切的研究课题。在本文中,我们研究了基于能量采集的线性无线传感器网络(EH-LWSN)在有限地平线时间段内吞吐量最大化的问题。原始问题的解决方案非常复杂,这种复杂性主要源于两个因素。首先,最优能量分配方案具有时间耦合性,即当前的最优策略依赖于未来收获的能量。第二,最优能量分配方案具有空间耦合性,即任何节点的当前最优策略都依赖于网络中其他节点的可用能量。为了应对这些挑战,我们提出了一种 EH-LWSN 的迭代能量分配算法。首先,我们从理论上证明了算法的最优性,并分析了算法的时间复杂性。接下来,我们设计了相应的分布式版本,并考虑了估计能量收获的情况。最后,通过使用真实世界的可再生能源数据集进行实验,结果表明所提出的算法在网络吞吐量方面优于其他两种启发式能量分配方案。
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引用次数: 0
READ: Resource efficient authentication scheme for digital twin edge networks 阅读:数字孪生边缘网络的资源高效认证方案
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107498
Kai Wang, Jiankuo Dong, Yijie Xu, Xinyi Ji, Letian Sha, Fu Xiao

In recent vigorous developments, digital twin edge networks (DITEN) have emerged as a network paradigm to improve network communication efficiency. Given that Web 3.0 technologies promise secure decentralized data storage and effective information exchange, it is feasible to construct a wireless edge intelligence-enabled Web 3.0 physical infrastructure through DITEN. However, DITEN encounters various security threats related to communication and authentication, and establishing a secure and cost-effective authentication scheme for confidential access to physical entities poses a significant challenge. To tackle this issue, in this article, we introduce READ, a provably secure multi-factor user authentication scheme tailored for DITEN in industrial applications. Using designed ASCON cryptography primitive cipher suite, physical unclonable functions, extended Chebyshev chaotic maps, one-way secure collision-resistant hash functions, and lightweight bitwise exclusive-or operations, READ enables mutual authentication and session key negotiation among mobile users, smart gateways, and smart industrial devices. Rigorous security assessments, conducted through the real-or-random (ROR) model, the automated validation of internet security-sensitive protocols and applications (AVISPA) simulation tool, and heuristic informal security analysis, confirm that READ meets all 13 security evaluation criteria. Furthermore, compared to other seven advanced multi-factor user authentication schemes, READ excels in security and efficiency, making it ideal for practical multi-factor user authentication scenarios.

近年来,数字孪生边缘网络(DITEN)蓬勃发展,成为提高网络通信效率的一种网络范式。鉴于 Web 3.0 技术有望实现安全的分散式数据存储和有效的信息交换,通过 DITEN 构建支持无线边缘智能的 Web 3.0 物理基础设施是可行的。然而,DITEN 会遇到与通信和身份验证有关的各种安全威胁,而建立一个安全且经济高效的身份验证方案以实现对物理实体的保密访问则是一项重大挑战。为解决这一问题,我们在本文中介绍了 READ,一种专为工业应用中的 DITEN 量身定制的可证明安全的多因素用户验证方案。READ 利用设计的 ASCON 密码学原始密码套件、物理不可克隆函数、扩展的切比雪夫混沌图、单向安全抗碰撞哈希函数和轻量级比特排他运算,实现了移动用户、智能网关和智能工业设备之间的相互验证和会话密钥协商。通过真实或随机(ROR)模型、互联网安全敏感协议和应用自动验证(AVISPA)模拟工具以及启发式非正式安全分析进行的严格安全评估证实,READ 符合所有 13 项安全评估标准。此外,与其他七种先进的多因素用户身份验证方案相比,READ 在安全性和效率方面都非常出色,是实际多因素用户身份验证方案的理想选择。
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引用次数: 0
期刊
Future Generation Computer Systems-The International Journal of Escience
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