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Reinforcing Localization Credibility Through Convex Optimization
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Resumo(s)
This work proposes a novel approach to reinforce localization security in wireless networks in the presence of malicious nodes that are able to manipulate (spoof) radio measurements. It substitutes the original measurement model by another one containing an auxiliary variance dilation parameter that disguises corrupted radio links into ones with large noise variances. This allows for relaxing the non-convex maximum likelihood estimator (MLE) into a semidefinite programming (SDP) problem by applying convex-concave programming (CCP) procedure. The proposed SDP solution simultaneously outputs target location and attacker detection estimates, eliminating the need for further application of sophisticated detectors. Numerical results corroborate excellent performance of the proposed method in terms of localization accuracy and show that its detection rates are highly competitive with the state of the art.
Descrição
Funding information:
This work was supported in part by the European Union’s Horizon Europe Research and Innovation Programme through Marie Skłodowska-Curie under Grant 101086387; in part by the Science Fund of the Republic of Serbia under Grant No. 221, Agile Drone Swarm Control based on Federated Reinforcement Learning and Optimization - ASCENT; in part by the Fundação para a Ciência e a Tecnologia under Project UIDB/50008/2020 (10.54499/UIDB/50008/2020) and Project 2021.04180.CEECIND; and in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) and Horizon Europe Guarantee under Grant EP/X039021/1- REMARKABLE.
Publisher Copyright:
© 1994-2012 IEEE.
Palavras-chave
Attacker detection convex-concave programming (CCP) measurement-spoofing secure localization semidefinite programming (SDP) Signal Processing Electrical and Electronic Engineering Applied Mathematics