Evidence map›Paper›PMID 41381657›Full record

ArticleScientific reports2025

Comprehensive analysis of security threats and privacy issues in indoor localization systems.

Ayesha Ayub, Zuhairiah Zainal Abidin, Abdulraqeb Alhammadi, Muhammad Asim Khan, Naglaa F Soliman, Nurul Bashirah Ghazali, Abeer D Algarni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ayesha AyubAdvanced Telecommunication Research Center (ATRC), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia.
Zuhairiah Zainal AbidinAdvanced Telecommunication Research Center (ATRC), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia. zuhairia@uthm.edu.my.
Abdulraqeb AlhammadiFaculty of Artificial Intelligence, Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia. abdulraqeb.alhammadi@utm.my.
Muhammad Asim KhanSchool of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia.
Naglaa F SolimanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Nurul Bashirah GhazaliAdvanced Telecommunication Research Center (ATRC), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia.
Abeer D AlgarniDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Funding

Fundamental Research Grant Scheme FRGS/1/2023/ICT02/UTHM/02/3Princess Nourah bint Abdulrahman University Researchers PNURSP2025R66
6 · The paper itself

Abstract

The growing use of indoor localization systems (ILS) in essential applications, including healthcare, smart buildings, and logistics, has created serious security and privacy concerns. This paper thoroughly analyzes the existing security and privacy concerns in ILS, emphasizing risks such as spoofing, signal jamming, and adversarial attacks. We explore defense strategies, such as federated learning, adversarial machine learning, and cryptographic protocols, emphasizing their efficacy and constraints. The study examines the trade-offs among privacy, accuracy, and efficiency in ILS while tackling significant difficulties such as non-Independent and Identically Distributed (non-IID) data, energy efficiency, and scalability in practical applications. This review provides a comprehensive overview of the state of the art in protecting ILS against growing adversarial threats by integrating major trends and approaches from the last five years. This survey paper will help researchers and industry professionals gain a deeper understanding of privacy and security concerns in ILS.

Indexed as

Adversarial machine learningFederated learningIndoor localization systemsPrivacySecuritySignal jammingSpoofing attacks

Identifiers

PMID41381657
PMCPMC12749273

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.