Robust regression and outlier detection
Robust regression and outlier detection
Robust statistical methods for securing wireless localization in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Attack-resistant location estimation in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Malicious user detection in a cognitive radio cooperative sensing system
IEEE Transactions on Wireless Communications
Computers and Electrical Engineering
Computers and Electrical Engineering
Application and mobility aware integration of opportunistic networks with wireless infrastructures
Computers and Electrical Engineering
Physical process resilience-aware network design for SCADA systems
Computers and Electrical Engineering
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The use of WiMAX cellular networks has arisen as a promising solution in order to provide broadband access over large, often shadowed, areas. As in other cellular networks, localization of users is extremely useful for many services and even essential for some civilian and/or military logistic operations. In a cellular WiMAX network, a node can obtain its position from beacons received by several cell base stations. Therefore, securing the localization method against potential false or erroneous feedback is of paramount importance in order to allow the nodes to get reliable position estimations. This fact implies not only making the localization method robust against erroneous or forged measurements, but also identifying which WiMAX base stations are providing such measurements. In this paper, we propose a robust localization method that can identify up to k malicious or misbehaving base stations and provide with an accurate estimation of the node position even in their presence. Simulation results prove that this proposal outperforms other existing detection techniques.