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Optimization of reservoir operation by sine cosine algorithm: A case of study in Algeria 利用正弦余弦算法优化水库运行:阿尔及利亚研究案例
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-11 DOI: 10.1016/j.suscom.2024.101035
Merouane Boudjerda , Bénina Touaibia , Mustapha Kamel Mihoubi , Ozgur Kisi , Mohammd Ehteram , Ahmed El-Shafie
The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system’s operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.
水库的优化运行在水利工程中至关重要。本文采用了一种名为正弦余弦算法(SCA)的新优化方法,以获得灌溉系统的运行策略。SCA 被用于阿尔及利亚北部 Boukerdane 大坝的月度运行。拟合函数是研究期间总短缺量的最小化。考虑到三个不同的流入季节(干旱、正常和潮湿),采用了三种方案来优化水库系统的运行。将 SCA 输出与粒子群优化(PSO)和肾算法(KA)进行了比较。结果表明,SCA 的收敛速度超过了 PSO 和 KA。总体结果表明,KA 和 PSO 的收敛速度较低。结果表明,SCA 达到的最高 RES(弹性指数)、SUS(可持续性指数)和 REL(可靠性指数)分别为 65%、86% 和 92%。将第三种方案与第一种和第二种方案进行比较后发现,第三种方案(雨季)的结果有所改善。
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引用次数: 0
Optimized dynamic service placement for enhanced scheduling in fog-edge computing environments 优化动态服务布局,增强雾边缘计算环境的调度能力
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-11 DOI: 10.1016/j.suscom.2024.101037
Yongxing Lin , Yan Shi , Nazila Mohammadnezhad

The traditional cloud computing model struggles to efficiently handle the vast number of Internet of Things (IoT) services due to its centralized nature and physical distance from end-users. In contrast, edge and fog computing have emerged as promising solutions for supporting latency-sensitive IoT applications by distributing computational resources closer to the data source. However, these technologies are limited by their size and computational capacities, making optimal service placement a critical challenge. This paper addresses this challenge by introducing a dynamic and distributed service placement policy tailored for edge and fog environments. By leveraging the inherent advantages of proximity in fog and edge nodes, the proposed policy seeks to enhance service delivery efficiency, reduce latency, and improve resource utilization. The proposed method focuses on optimizing the placement of high-demand services closer to the data generation sources to enhance scheduling efficiency in fog computing environments. Our method is divided into three interconnected modules. The first module is the service type estimator, which is responsible for distributing services to appropriate nodes. Here, we use the Political Optimizer (PO) as a new metaheuristic algorithm for deploying IoT services. The second module is service dependency estimator, which manages service dependencies. Here, we load dependent services near the data using a path matrix based on the Warshall algorithm. Finally, the third module is resource demand scheduling, which estimates resource demand to facilitate optimal scheduling. Here, we use a popularity-based policy to manage resource demand and service execution scheduling. Implementation results demonstrate significant improvements over existing state-of-the-art policies, highlighting the efficacy of the proposed policy in enhancing service delivery within fog-edge computing frameworks.

传统的云计算模式由于其集中性和与终端用户的物理距离,很难有效处理大量的物联网(IoT)服务。相比之下,边缘计算和雾计算通过将计算资源分布在更靠近数据源的地方,成为支持延迟敏感型物联网应用的有前途的解决方案。然而,这些技术受限于其规模和计算能力,使得优化服务布局成为一项严峻挑战。本文针对这一挑战,引入了一种为边缘和雾环境量身定制的动态分布式服务放置策略。通过利用雾节点和边缘节点固有的邻近优势,所提出的策略旨在提高服务交付效率、减少延迟并提高资源利用率。所提方法的重点是优化高需求服务的布局,使其更接近数据生成源,从而提高雾计算环境中的调度效率。我们的方法分为三个相互关联的模块。第一个模块是服务类型估计器,负责将服务分配到合适的节点。在这里,我们使用政治优化器(PO)作为部署物联网服务的一种新元启发式算法。第二个模块是服务依赖性估算器,负责管理服务依赖性。在这里,我们使用基于 Warshall 算法的路径矩阵加载数据附近的依赖服务。最后,第三个模块是资源需求调度,用于估算资源需求以促进优化调度。在这里,我们使用基于流行度的策略来管理资源需求和服务执行调度。实施结果表明,与现有的最先进策略相比,我们的策略有了明显改善,这凸显了我们提出的策略在加强雾边缘计算框架内服务交付方面的功效。
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引用次数: 0
A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions 关于绿色云计算的最新简明调查:挑战、战略和未来方向
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-10 DOI: 10.1016/j.suscom.2024.101036
Dipto Biswas , Sohely Jahan , Sajeeb Saha , Md. Samsuddoha

Cloud computing is a method of providing various computing services, including software, hardware, databases, data storage, and infrastructure, to the public through the Internet. The rapid expansion of cloud computing services has raised significant concerns over their environmental impact. Cloud computing services should be designed in a green manner, efficient in energy consumption, virtualized, consolidated, and eco-friendly. Green Cloud Computing (GCC) is a significant field of study that focuses on minimizing the environmental impact and energy usage of cloud infrastructures. This survey provides a comprehensive overview of the current state of GCC, focusing on the challenges, strategies, and future directions. The review study begins by identifying important challenges in GCC from practical implementations, identifying GCC-introduced environmental protection and prevention initiatives, and expressing the demand for long-term technical progression. It then addresses GCC’s primary concerns, such as energy efficiency, resource management, operational costs, and carbon emissions, and categorizes implementations according to algorithms, architectures, frameworks, general issues, and models and methodologies. Furthermore, enhancements in virtualization, multi-tenancy, and consolidation have been identified, analyzed, and accurately portrayed to address the advancements in GCC. Finally, the survey outlines future research directions and opportunities for advancing the field of GCC, including the development of novel algorithms, technologies for energy harvesting, and energy-efficient and eco-friendly solutions. By providing a comprehensive overview of GCC, this survey aims to serve as documentation for further evolving new emerging technological approaches in the GCC environment.

云计算是一种通过互联网向公众提供各种计算服务的方法,包括软件、硬件、数据库、数据存储和基础设施。云计算服务的迅速扩展引起了人们对其环境影响的极大关注。云计算服务应采用绿色设计、高效能耗、虚拟化、整合和生态友好。绿色云计算(GCC)是一个重要的研究领域,其重点是最大限度地减少云基础设施对环境的影响和能源消耗。本调查全面概述了 GCC 的现状,重点关注挑战、战略和未来方向。回顾性研究首先从实际实施中确定了 GCC 面临的重要挑战,明确了 GCC 引入的环境保护和预防措施,并表达了对长期技术进步的需求。然后,它探讨了 GCC 的主要关注点,如能源效率、资源管理、运营成本和碳排放,并根据算法、架构、框架、一般问题以及模型和方法对实施进行了分类。此外,还对虚拟化、多租户和整合方面的改进进行了识别、分析和准确描绘,以应对 GCC 的进步。最后,调查报告概述了推动 GCC 领域发展的未来研究方向和机遇,包括开发新型算法、能源采集技术以及高能效和生态友好型解决方案。通过对 GCC 的全面概述,本调查旨在为进一步发展 GCC 环境中的新兴技术方法提供文献资料。
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引用次数: 0
A new hybrid fixed and adaptive gains control algorithm to reduce power losses on LLCL filter-based renewable energy conversion systems with systematic parametrization using Grey Wolf optimizer 基于 LLCL 滤波器的可再生能源转换系统采用新的固定增益和自适应增益混合控制算法,利用灰狼优化器进行系统参数化,以降低功率损耗
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-08-23 DOI: 10.1016/j.suscom.2024.101034
Felipe Pontes de Matos, Marlon Mauricio Hernandez Cely, Paulo Jefferson Dias de Oliveira Evald

Currently, there is a transition in the energy matrix around the world, where traditional sources of energy generation are continually being replaced by energy generation systems based on renewable sources to mitigate the climate crisis. In this bias, this work presents the mathematical modeling of an LLCL filter, used to connect power generation systems based on renewable energy sources to the electrical grid, and presents a novel hybrid fixed-and adaptive gains control strategy for current injection into the grid using this system. The developed hybrid controller is composed of a proportional–integral controller and a direct robust adaptive controller. The first term of the controller guarantees the reference current, while the second term of the controller is used for disturbance rejection. Furthermore, a systematic procedure for the controller’s parametrization based on Grey Wolf Optimizer is also provided. The control of the current injected into the grid is carried out considering the LLCL filter without passive damping resistors in the filter structure to avoid power losses due to the passive filter elements. Additionally, the LLCL filter model considers minimal parasitic resistances to evaluate the controller’s performance and optimize it for the application of interest, aiming to maximize the system performance by ensuring a short transient regime due to the fast closed-loop system response. Simulation results indicate high performance of this optimized control strategy with small tracking error even considering grid impedance variations.

目前,全世界的能源结构正在发生转变,传统的能源发电方式不断被基于可再生能源的发电系统所取代,以缓解气候危机。在此背景下,本作品介绍了用于将基于可再生能源的发电系统连接到电网的 LLCL 滤波器的数学模型,并提出了一种新颖的固定增益和自适应增益混合控制策略,用于利用该系统向电网注入电流。所开发的混合控制器由比例积分控制器和直接鲁棒自适应控制器组成。控制器的第一项保证了参考电流,而第二项则用于抑制干扰。此外,还提供了基于灰狼优化器的控制器参数化系统程序。在对注入电网的电流进行控制时,考虑到在滤波器结构中不使用无源阻尼电阻器的 LLCL 滤波器,以避免无源滤波器元件造成的功率损耗。此外,LLCL 滤波器模型考虑了最小寄生电阻,以评估控制器的性能,并针对相关应用对其进行优化,目的是通过快速闭环系统响应确保短瞬态机制,从而最大限度地提高系统性能。仿真结果表明,即使考虑到电网阻抗变化,这种优化控制策略的性能也很高,跟踪误差很小。
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引用次数: 0
Retraction notice to ‘Deep learning-based energy inefficiency detection in the smart buildings' [Sustainable Computing: Informatics and Systems 40 (2023) 100921] 基于深度学习的智能建筑能效检测》撤稿通知 [Sustainable Computing: Informatics and Systems 40 (2023) 100921]
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-08-06 DOI: 10.1016/j.suscom.2024.101022
Jueru Huang , Dmitry D. Koroteev , Marina Rynkovskaya
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引用次数: 0
Secured osprey-based energy efficient routing and congestion control in WSN WSN 中基于鹗的安全节能路由和拥塞控制
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-08-02 DOI: 10.1016/j.suscom.2024.101026
R.M. Bhavadharini , B. Surendiran

An adaptive metaheuristic optimization-based QoS-aware, Energy-balancing, Secure Routing Protocol (AQoS-ESRP) is proposed in this article. The network is modelled as a biconcentric hexagon (BiCon-HexA), and the clusters are formed within the BiCon-HexA network. The BiCon-HexA is divided into six sectors to support effective data aggregation, and then clusters are formed within all sectors. The optimal cluster head (CH) selection mechanism is modelled by an Adaptive Hunter-Prey Optimization (AdapH-PO) algorithm considering QoS parameters. Data aggregation is then done securely with an enhanced encryption approach. Here, upgraded elliptic curve cryptography (UEllip-CC) is used to encode data in CH. This UEllip-CC approach provides security improvements in data transmission. Furthermore, in this study, CHs are combined in the multi-hop routing of data packets to reduce the power consumption problems of wireless sensor networks (WSN). To determine the optimal route for data transmission, an energy-balanced multi-path routing algorithm called improved convolutional osprey network (ICON) is presented. Nevertheless, the data transmission nodes can be overloaded in the data routing phase. Here, the congestion problem can be solved by applying an improved version of the Random Early Detection (RED) congestion control model to discard the data packets more noticeably. The simulation of AQoS-ESRP is done with Matlab, and the performance is evaluated using different metrics. When compared to existing systems, the simulation results clearly indicate a significantly higher throughput and delay. Thus, the AQoS ESRP model is employed to maximize the overall data transfer in the WSN.

本文提出了一种基于自适应元启发式优化的 QoS 感知、能量平衡、安全路由协议(AQoS-ERP)。网络被模拟成一个双中心六边形(BiCon-HexA),并在 BiCon-HexA 网络中形成集群。为了支持有效的数据聚合,BiCon-HexA 被划分为六个扇区,然后在所有扇区内形成簇。最佳簇头(CH)选择机制采用自适应猎人-猎物优化(AdapH-PO)算法建模,其中考虑了 QoS 参数。然后,采用增强型加密方法安全地进行数据聚合。在这里,升级的椭圆曲线加密法(UEllip-CC)被用来对 CH 中的数据进行加密。这种 UEllip-CC 方法提高了数据传输的安全性。此外,在这项研究中,CH 在数据包的多跳路由中被结合起来,以减少无线传感器网络(WSN)的功耗问题。为了确定数据传输的最佳路由,提出了一种名为改进卷积鹗网络(ICON)的能量平衡多路径路由算法。然而,在数据路由阶段,数据传输节点可能会超载。在这里,可以通过应用改进版的随机早期检测(RED)拥塞控制模型来解决拥塞问题,从而更明显地丢弃数据包。使用 Matlab 对 AQoS-ESRP 进行了仿真,并使用不同指标对其性能进行了评估。与现有系统相比,仿真结果清楚地表明吞吐量和延迟都有明显提高。因此,采用 AQoS ESRP 模型可以最大限度地提高 WSN 的整体数据传输能力。
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引用次数: 0
DBSCAN based approach for energy efficient VM placement using medium level CPU utilization 基于 DBSCAN 的方法,利用中等水平的 CPU 利用率实现高能效虚拟机放置
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-08-02 DOI: 10.1016/j.suscom.2024.101025
Akanksha Tandon, Sanjeev Patel

Virtual machine placement (VMP) is a popular problem in Cloud Data Centers (CDCs). An efficient virtual machine (VM) allocation is essential for processor speed and energy saving. This is more useful where the CDC uses an Internet of Things (IoT) infrastructure. To enhance energy savings, we aim to improve the adaptive four thresholds energy-aware framework for VM deployment. We observed that the role of the threshold for identifying the over-loaded host is crucial. In order to determine the appropriate threshold, we employed density-based spatial clustering of applications with noise (DBSCAN), medium absolute deviation (MAD), and interquartile range (IQR) using the medium fit power efficient decreasing (MFPED) algorithm. Our proposed algorithm modified medium fit energy efficient decreasing (MMFEED) achieves a reduction in energy consumption of 47.3%, 46.1%, 39%, 23.2%, 10.9%, and 3.4% compared to the IQR, MAD, static threshold (THR), exponential weighted moving average (EWMA), modified energy-efficient virtual machine placement (MEEVMP), and adaptive four threshold energy-aware framework for VM deployment energy efficient (AFED-EF), respectively, under the minimum migration time (MMT) selection policy. The proposed algorithm outperforms these algorithms in terms of energy consumption for VM selection policy MMT.

虚拟机分配(VMP)是云数据中心(CDC)中的一个常见问题。高效的虚拟机(VM)分配对处理器速度和节能至关重要。当 CDC 使用物联网(IoT)基础设施时,这一点更为有用。为了提高节能效果,我们旨在改进用于虚拟机部署的自适应四阈值能源感知框架。我们注意到,阈值在识别过载主机方面的作用至关重要。为了确定合适的阈值,我们采用了基于密度的应用空间聚类噪声(DBSCAN)、中等绝对偏差(MAD)和四分位数范围(IQR),并使用了中等拟合功率效率递减(MFPED)算法。在最小迁移时间(MMT)选择策略下,与 IQR、MAD、静态阈值(THR)、指数加权移动平均值(EWMA)、改进型节能虚拟机放置(MEEVMP)和用于虚拟机部署节能的自适应四阈值能量感知框架(AFED-EF)相比,我们提出的改进型中等拟合节能递减(MMFEED)算法分别实现了 47.3%、46.1%、39%、23.2%、10.9% 和 3.4% 的能耗降低。就虚拟机选择策略 MMT 的能耗而言,所提出的算法优于这些算法。
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引用次数: 0
An intelligent task scheduling approach for the enhancement of collaborative learning in cloud computing 增强云计算协作学习的智能任务调度方法
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-07-22 DOI: 10.1016/j.suscom.2024.101024
P. Sathishkumar , Narendra Kumar , S. Hrushikesava Raju , D. Rosy Salomi Victoria

Cloud computing is the foremost technology that reliably connects end-to-end users. Task scheduling is a critical process affecting the performance enhancement of cloud computing. The scheduling of the enormous data results in increased response time, makespan time, and makes the system less efficient. Therefore, a unique Squirrel Search-based AlexNet Scheduler (SSbANS) is created for adequate scheduling and performance enhancement in cloud computing suitable for collaborative learning. The system processes the tasks that the cloud users request. Initially, the priority of each task is checked and arranged. Moreover, the optimal resource is selected using the fitness function of the squirrel search, considering the data rate and the job schedule. Further, during the scheduled task-sharing process, the system continuously checks for overloaded resources and balances based on the squirrel distribution function. The efficacy of the model is reviewed in terms of response time, resource usage, makespan time, and throughput. The model achieved a higher throughput and resource usage rate with a lower response and makespan time.

云计算是可靠连接端到端用户的最重要技术。任务调度是影响云计算性能提升的关键过程。海量数据的调度会导致响应时间和间隔时间的增加,并降低系统的效率。因此,我们创建了一种独特的基于松鼠搜索的 AlexNet 调度器(SSbANS),用于在适合协作学习的云计算中进行适当的调度和性能提升。该系统处理云用户请求的任务。首先,检查并安排每个任务的优先级。此外,考虑到数据传输速率和任务计划,使用松鼠搜索的适应度函数选择最佳资源。此外,在预定的任务共享过程中,系统会持续检查资源是否过载,并根据松鼠分布函数进行平衡。我们从响应时间、资源使用、间隔时间和吞吐量等方面对该模型的功效进行了评估。该模型实现了较高的吞吐量和资源使用率,较低的响应时间和间隔时间。
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引用次数: 0
Hybrid Boosted Chameleon and modified Honey Badger optimization algorithm-based energy efficient cluster routing protocol for cognitive radio sensor network 认知无线电传感器网络基于混合提升变色龙和改进蜜獾优化算法的高能效集群路由协议
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-07-20 DOI: 10.1016/j.suscom.2024.101023
G. Sathya , C. Balasubramanian

Single clustering protocols cannot meet the event-driven and time-triggered traffic requirements of Cognitive Radio Sensor Networks (CRSNs). The long wait between the completion of events and the process of clustering and searching for accessible routes results in increased time for information transmission. This paper proposed a Hybrid Boosted Chameleon and Modified Honey Badge optimization Algorithm-based Energy Efficient cluster routing protocol (HBCMHBOA) for handling the issues of traffic driven information transfer with energy efficiency in the CRSNs. This HBCMHBOA is proposed as one among few event-driven and time-triggered clustering protocol for the requirements of CRSNs. The integration of Boosted Chameleon and Modified Honey Badge optimization Algorithm is adopted for determining optimal number of clusters and constructs the structure of primitive clusters in an automated way to serve the time-triggered traffic in a periodic manner. It adopted priority-based schedule and its associated frame structure for guaranteeing reliable event-driven information delivery. It leveraged the merits of time-triggering for the construction of clustering architecture and confirmed than none of the cluster construction and selection of routes are facilitated after the emergent events. This characteristic helps in permitting only the nodes and their associated Cluster Heads (CHs) of CRSNs to discover emergent events. It facilitates the coverage of a fewer nodes, especially when sink is positioned in a corner to minimize the delay and node energy consumption. The simulation results of the proposed HBCMHBOA confirmed a reduction in total energy consumption and number of covered nodes on an average of 34.12 %, and 26.89 % than the prevailing studies.

单一聚类协议无法满足认知无线电传感器网络(CRSN)的事件驱动和时间触发流量要求。事件完成与聚类和搜索可访问路由过程之间的等待时间较长,导致信息传输时间增加。本文提出了一种基于混合提升变色龙和修正蜜蜂徽章优化算法的高能效集群路由协议(HBCMHBOA),用于处理 CRSN 中由流量驱动的信息传输问题并提高能效。该 HBCMHBOA 是为满足 CRSN 要求而提出的少数事件驱动和时间触发聚类协议之一。它采用了 "变色龙"(Boosted Chameleon)和 "蜜蜂徽章"(Modified Honey Badge)优化算法,用于确定最佳聚类数量,并以自动化方式构建原始聚类结构,以周期性方式为时间触发的流量提供服务。它采用了基于优先级的时间表及其相关框架结构,以保证可靠的事件驱动信息传输。它利用时间触发的优点来构建集群架构,并确认在突发事件发生后,集群的构建和路由的选择都不会受到影响。这一特性有助于只允许 CRSN 的节点及其相关簇首(CH)发现突发事件。它有利于覆盖更少的节点,特别是当水槽位于角落时,以最大限度地减少延迟和节点能耗。建议的 HBCMHBOA 的模拟结果证实,与现有研究相比,总能耗和覆盖节点数分别平均减少了 34.12 % 和 26.89 %。
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引用次数: 0
Optimizing risk mitigation: A simulation-based model for detecting fake IoT clients in smart city environments 优化风险缓解:基于仿真的智能城市环境中假物联网客户端检测模型
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-07-19 DOI: 10.1016/j.suscom.2024.101019
Mahmoud AlJamal , Ala Mughaid , Bashar Al shboul , Hani Bani-Salameh , Shadi Alzubi , Laith Abualigah

Smart cities represent the future of urban evolution, characterized by the intricate integration of the Internet of Things (IoT). This integration sees everything, from traffic management to waste disposal, governed by interconnected and digitally managed systems. As fascinating as the promise of such cities is, they have its challenges. A significant concern in this digitally connected realm is the introduction of fake clients. These entities, masquerading as legitimate system components, can execute a range of cyber-attacks. This research focuses on the issue of fake clients by devising a detailed simulated smart city model utilizing the Netsim program. Within this simulated environment, multiple sectors collaborate with numerous clients to optimize performance, comfort, and energy conservation. Fake clients, who appear genuine but with malicious intentions, are introduced into this simulation to replicate the real-world challenge. After the simulation is configured, the data flows are captured using Wireshark and saved as a CSV file, differentiating between the real and fake clients. We applied MATLAB machine learning techniques to the captured data set to address the threat these fake clients posed. Various machine learning algorithms were tested, and the k-nearest neighbors (KNN) classifier showed a remarkable detection accuracy of 98 77%. Specifically, our method increased detection accuracy by 4.66%, from 94.02% to 98.68% over three experiments conducted, and enhanced the Area Under the Curve (AUC) by 0.49%, reaching 99.81%. Precision and recall also saw substantial gains, with precision improving by 9.09%, from 88.77% to 97.86%, and recall improving by 9.87%, from 89.23% to 99.10%. The comprehensive analysis underscores the role of preprocessing in enhancing the overall performance, highlighting its superior performance in detecting fake IoT clients in smart city environments compared to conventional approaches. Our research introduces a powerful model for protecting smart cities, merging sophisticated detection techniques with robust defenses.

智能城市代表着城市发展的未来,其特点是物联网(IoT)的复杂整合。在这种整合中,从交通管理到垃圾处理,一切都由相互连接的数字化管理系统来管理。这种城市的前景固然诱人,但也存在挑战。这种数字互联领域的一个重大问题是引入假冒客户。这些伪装成合法系统组件的实体可以实施一系列网络攻击。本研究利用 Netsim 程序设计了一个详细的智能城市模拟模型,重点研究假客户问题。在这个模拟环境中,多个部门与众多客户合作,以优化性能、舒适度和节能。假客户看似真实,实则心怀恶意,他们被引入模拟环境,以应对现实世界中的挑战。模拟配置完成后,使用 Wireshark 捕获数据流并保存为 CSV 文件,以区分真实和虚假客户。我们将 MATLAB 机器学习技术应用于捕获的数据集,以应对这些虚假客户带来的威胁。我们对各种机器学习算法进行了测试,k-近邻(KNN)分类器的检测准确率高达 98 77%。具体来说,我们的方法在三次实验中将检测准确率提高了 4.66%,从 94.02% 提高到 98.68%,并将曲线下面积 (AUC) 提高了 0.49%,达到 99.81%。精确度和召回率也有大幅提高,精确度提高了 9.09%,从 88.77% 提高到 97.86%,召回率提高了 9.87%,从 89.23% 提高到 99.10%。综合分析凸显了预处理在提高整体性能方面的作用,与传统方法相比,预处理在检测智慧城市环境中的虚假物联网客户端方面表现出色。我们的研究为保护智慧城市引入了一个强大的模型,将复杂的检测技术与强大的防御功能融为一体。
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引用次数: 0
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Sustainable Computing-Informatics & Systems
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