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Reinforcing Localization Credibility Through Convex Optimization

2025-08-22Artigo científico Acesso aberto
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dc.contributor.authorTomic, Slavisa
dc.contributor.authorBeko, Marko
dc.contributor.authorTsado, Yakubu
dc.contributor.authorAdebisi, Bamidele
dc.contributor.authorOladipo, Abiola
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2026-01-26T11:40:01Z
dc.date.available2026-01-26T11:40:01Z
dc.date.issued2025-08-22
dc.descriptionFunding 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.
dc.description.abstractThis 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent5
dc.format.extent470162
dc.identifier.doi10.1109/LSP.2025.3601835
dc.identifier.issn1070-9908
dc.identifier.otherPURE: 151148529
dc.identifier.otherPURE UUID: 05bb52fe-0549-4af0-aa41-bdc3ceb54f7d
dc.identifier.otherScopus: 105014020560
dc.identifier.urihttp://hdl.handle.net/10362/199709
dc.identifier.urlhttps://www.scopus.com/pages/publications/105014020560
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50008%2F2020/PT
dc.subjectAttacker detection
dc.subjectconvex-concave programming (CCP)
dc.subjectmeasurement-spoofing
dc.subjectsecure localization
dc.subjectsemidefinite programming (SDP)
dc.subjectSignal Processing
dc.subjectElectrical and Electronic Engineering
dc.subjectApplied Mathematics
dc.titleReinforcing Localization Credibility Through Convex Optimizationen
dc.typejournal article
degois.publication.firstPage3445
degois.publication.lastPage3449
degois.publication.titleIEEE Signal Processing Letters
degois.publication.volume32
dspace.entity.typePublication
rcaap.rightsopenAccess

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