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Attack-resistant localization algorithm via adaptive weights for range-based wireless sensor networks.

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Titel: Attack-resistant localization algorithm via adaptive weights for range-based wireless sensor networks.
Autoren: Wang, Miao1,2 (AUTHOR) wm_ubuntu@zju.edu.cn, Cui, Can1,2 (AUTHOR) cuican1990@zju.edu.cn, Zhou, Xiaobin1,2 (AUTHOR) xbzhou_zju@zju.edu.cn
Quelle: Journal of Industrial & Management Optimization. Nov2025, Vol. 21 Issue 11, p1-19. 19p.
Schlagwörter: WIRELESS sensor networks, WIRELESS geolocation systems, NONCONVEX programming, ROBUST statistics
Abstract: Localization is a critical challenge for robots operating within a wireless sensor network (WSN) that contains malicious nodes, which can disrupt positioning by broadcasting false information. Existing methods often rely on prior knowledge of such nodes, which is unavailable and can lead to inaccurate or divergent localization results. To address this, we propose an attack-resistant localization algorithm with adaptive weighting, achieving accurate positioning without prior knowledge of malicious anchors and ensuring robustness in adversarial environments. First, the tag localization function is improved by incorporating distance attack estimation and a dynamic weight update mechanism to mitigate the impact of malicious anchors. Additionally, we exploit the gradient properties of the subfunctions in the localization function to avoid convergence to local minima in the non-convex optimization problem. Finally, simulations and real-world experiments validate the method under various attack scenarios, with comparisons to state-of-the-art localization methods, demonstrating its superior accuracy and resilience. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract:Localization is a critical challenge for robots operating within a wireless sensor network (WSN) that contains malicious nodes, which can disrupt positioning by broadcasting false information. Existing methods often rely on prior knowledge of such nodes, which is unavailable and can lead to inaccurate or divergent localization results. To address this, we propose an attack-resistant localization algorithm with adaptive weighting, achieving accurate positioning without prior knowledge of malicious anchors and ensuring robustness in adversarial environments. First, the tag localization function is improved by incorporating distance attack estimation and a dynamic weight update mechanism to mitigate the impact of malicious anchors. Additionally, we exploit the gradient properties of the subfunctions in the localization function to avoid convergence to local minima in the non-convex optimization problem. Finally, simulations and real-world experiments validate the method under various attack scenarios, with comparisons to state-of-the-art localization methods, demonstrating its superior accuracy and resilience. [ABSTRACT FROM AUTHOR]
ISSN:15475816
DOI:10.3934/jimo.2025144