Cyber-WISE: A cyber-physical deep wireless indoor positioning system and digital twin approach

Basit Öğe Kaydı
dc.authorid0000-0001-6363-2732
dc.authorid0000-0002-9054-0005
dc.contributor.authorKarakuşak, Muhammed Zahid
dc.contributor.authorKıvrak, Hasan
dc.contributor.authorWatson, Simon
dc.contributor.authorÖzdemir, Mehmet Kemal
dc.date.accessioned2024-01-17T07:22:43Z
dc.date.available2024-01-17T07:22:43Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik ve Elektronik Mühendisliği ve Siber Sistemler Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of <= 2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.
dc.description.sponsorshipEuropean Union (EU) Marie Curie Actionsen_US
dc.identifier.citationKarakuşak, M. Z., Kıvrak, H., Watson, S. ve Özdemir, M. K. (2023). Cyber-WISE: A cyber-physical deep wireless indoor positioning system and digital twin approach. Sensors, 23(24). https://dx.doi.org/10.3390/s23249903
dc.identifier.doi10.3390/s23249903
dc.identifier.issn1424-8220
dc.identifier.issue24
dc.identifier.pmid38139747
dc.identifier.scopus2-s2.0-85180716569
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.3390/s23249903
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12157
dc.identifier.volume23
dc.identifier.wos001131317900001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorKarakuşak, Muhammed Zahid
dc.institutionauthorÖzdemir, Mehmet Kemal
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/121N350
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectInternet of Things (IoT)
dc.subjectDigital Twins
dc.subjectCyber-Physical Systems (CPSs)
dc.subjectSmart Space
dc.subjectIndoor Localization
dc.subjectWireless LAN Positioning
dc.subjectFingerprint Matrix
dc.subjectReceived Signal Strength (RSS)
dc.subjectDeep Learning
dc.subjectLong Short-Term Memory (LSTM)
dc.titleCyber-WISE: A cyber-physical deep wireless indoor positioning system and digital twin approach
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Karakusak-Zahid-2023.pdf
Boyut:
4.89 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Fen Bilimleri Enstitüsü Dergi Yayınlar Koleksiyonu
Makale Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu