Virtual Convex Hull Based Distributed Iterative Localization for Mobile Sensor Networks Under Denial-of-Service Attacks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-07-10 DOI:10.1109/TSIPN.2025.3587404

Shaojie Yao;Lei Shi;Yong Wang;Rui Liu;Yi Zhou

引用次数: 0

Abstract

This paper addresses the problem of cybersecurity in the localization process of mobile sensor networks. A generic model of random periodic denial-of-service (DoS) attacks is considered, where the attacker’s behavior is bounded only by the duration and frequency of the DoS attacks. A class of distributed localization algorithms based on virtual convex hull is proposed, which is abstracted as a linear time-varying system by constructing virtual convex hull using a single anchor node. Using the method combining graph composition and sub-stochastic matrix, it is shown that the algorithm can accurately converge to the true locations of sensor nodes. At last, the effectiveness of the algorithm is verified by simulation examples.

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拒绝服务攻击下基于虚拟凸包的移动传感器网络分布式迭代定位

本文研究了移动传感器网络定位过程中的网络安全问题。考虑了随机周期性拒绝服务攻击的一般模型，攻击者的行为仅受DoS攻击的持续时间和频率的限制。提出了一种基于虚拟凸壳的分布式定位算法，该算法通过单个锚节点构造虚拟凸壳，将虚拟凸壳抽象为线性时变系统。采用图合成与次随机矩阵相结合的方法，表明该算法能够准确收敛到传感器节点的真实位置。最后，通过仿真算例验证了算法的有效性。

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

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.