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

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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Publication Details

Journal
IEEE Transactions on Signal and Information Processing over Networks
Published
2025-01-01
DOI
https://doi.org/10.1109/tsipn.2025.3587404
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

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

IEEE Transactions on Signal and Information Processing over Networks
Indoor and Outdoor Localization Technologies
article

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

article en

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

IEEE Transactions on Signal and Information Processing over NetworksVol. 11
Henan University (CN), ETC International (NL)
Openalex Percentile: Top 54%
Indoor and Outdoor Localization Technologies
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