ResiSC: A system for building resilient smart city communication networks

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Expert Systems Pub Date : 2024-08-08 DOI:10.1111/exsy.13698
Mohammed J. F. Alenazi
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引用次数: 0

Abstract

Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats.
ResiSC:构建弹性智能城市通信网络的系统
智能城市网络对于提供医疗保健、教育和商业运营等基本服务至关重要。然而,这些网络极易受到自然灾害和蓄意网络攻击等一系列威胁的影响,从而严重破坏其功能。针对这些弱点,我们提出了弹性智能城市(ResiSC)系统,旨在通过拓扑设计方法增强智能城市通信网络的弹性。我们的系统采用图论算法来确定给定节点集的最佳网络拓扑结构,旨在最大限度地提高连通性,同时最大限度地降低链路配置成本。我们引入了两种新的连通性测量方法,即所有节点可达性(ANR)和所有节点可达性总和(SANR),以评估网络弹性。我们将我们的方法应用于两所不同规模的公立大学的数据,模拟各种攻击场景来评估所生成的网络拓扑结构的鲁棒性。评估结果表明,与 k-近邻(k-NN)图等基线方法相比,我们的解决方案将网络抵御有针对性攻击的能力提高了 38%,同时还减少了额外链接的数量及其相关成本。结果还表明,我们提出的解决方案在网络抵御有针对性攻击的能力方面比 k-NN 等基线方法高出 41%。这项工作为开发能够抵御各种威胁的强大智能城市网络提供了一个实用框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Expert Systems
Expert Systems 工程技术-计算机:理论方法
CiteScore
7.40
自引率
6.10%
发文量
266
审稿时长
24 months
期刊介绍: Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.
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