Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

Basit Öğe Kaydı
dc.authorider, Mehmet Bilal/0000-0002-2074-1776
dc.contributor.authorEr, Mehmet Bilal
dc.contributor.authorIsik, Ibrahim
dc.contributor.authorKuran, Umut
dc.contributor.authorIsik, Esme
dc.date.accessioned2024-08-04T20:56:06Z
dc.date.available2024-08-04T20:56:06Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMolecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.en_US
dc.description.sponsorshipTubitak [123E265]; Scientific and Technological Research Council of Turkeyen_US
dc.description.sponsorshipThis study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK-Project number: 123E265).en_US
dc.identifier.doi10.1007/s13369-024-09221-0
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.scopus2-s2.0-85196396446en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-024-09221-0
dc.identifier.urihttps://hdl.handle.net/11616/102066
dc.identifier.wosWOS:001251006300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMobile molecular communicationen_US
dc.subjectDiffusion constanten_US
dc.subjectNano bioscienceen_US
dc.subjectDeep learningen_US
dc.subjectReceiveren_US
dc.titleClassification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Modelen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu