UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Sensor Networks Pub Date : 2024-02-14 DOI:10.1145/3648373
Lijun Xiao, Dezhi Han, Kuan-Ching Li, Muhammad Khurram Khan
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Abstract

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration factors that impact device entry in smart cities, such as device performance, load, Bluetooth signal strength, and scan frequency. Despite existing techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on entropy weights methods are improved and applied, there exist unstable decision results. Due to this, we propose a technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on utility function and entropy weights, named UETOPSIS, where the corresponding utility function is applied according to the influence of each attribute on the decision, ensuring the stability of the ranking of decision results. We rely on an entropy-based weights mechanism to select a suitable master controller for the design of the multi-control protocol in the smart city system, and utilize a utility function to calculate the attribute values and then combine the normalized attribute values of utility numbers, starting by analyzing the main work of the controllers. Lastly, a prototype is developed for performance evaluation purposes. Experimental evaluation and analysis show that the proposed work has better authenticity and reliability than existing works and can reduce the workload of edge computing devices when forwarding data, with stability 24.7% higher than TOPSIS, significantly improving the performance and stability of system fault tolerance and reliability in smart cities, as the second-ranked controller can efficiently take over the work when a central controller fails or damaged.

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UETOPSIS:智能城市边缘计算安全决策的数据驱动智能方法
尽管智慧城市技术取得了长足进步,但仍面临云服务器连接不稳定、数据传输不安全、TCP/IP 网络架构略有缺陷等问题。为了解决这些问题,我们提出了一种数据驱动的智能方法,在边缘计算的命名数据网络(NDN)架构下进行安全决策,同时考虑到影响设备进入智慧城市的因素,如设备性能、负载、蓝牙信号强度和扫描频率等。尽管现有的基于熵权重方法的理想解相似度排序偏好(TOPSIS)技术得到了改进和应用,但仍存在决策结果不稳定的问题。因此,我们提出了一种基于效用函数和熵权重的理想解相似度排序偏好(TOPSIS)技术,命名为 UETOPSIS,根据各属性对决策的影响程度应用相应的效用函数,确保决策结果排序的稳定性。在智慧城市系统的多控制协议设计中,我们依靠基于熵的权重机制来选择合适的主控制器,并利用效用函数计算属性值,然后结合效用数的归一化属性值,从分析控制器的主要工作入手。最后,开发了一个原型用于性能评估。实验评估和分析表明,与现有作品相比,所提出的作品具有更好的真实性和可靠性,并能减少边缘计算设备在转发数据时的工作量,稳定性比 TOPSIS 高 24.7%,显著提高了智慧城市中系统容错和可靠性的性能和稳定性,因为当中央控制器发生故障或损坏时,排名第二的控制器可以高效地接管工作。
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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
期刊介绍: ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.
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