Analysis and classification of the mobile molecular communication systems with deep learning

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dc.authoridIsik, Esme/0000-0002-6179-5746
dc.authoridisik, ibrahim/0000-0003-1355-9420;
dc.authorwosidIsik, Esme/AAG-5927-2019
dc.authorwosidisik, ibrahim/AAG-5915-2019
dc.authorwosidER, Mehmet Bilal/ABA-3943-2020
dc.contributor.authorIsik, Ibrahim
dc.contributor.authorEr, Mehmet Bilal
dc.contributor.authorIsik, Esme
dc.date.accessioned2024-08-04T20:51:50Z
dc.date.available2024-08-04T20:51:50Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractNano networks focused on communication between nano-sized devices (nanomachines) is a new communication concept which is known as molecular communication system (MCs) in literature. The researchers have generally used fixed transmitter and receiver for MCs models to analyze the fraction of received molecules and signal to interference rate etc. In this study, contrary to the literature, a mobile MC model has been used in a diffusion environment by using five bits. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. After the fraction of received molecules is obtained for different mobility values of Rx and Tx (Drx and Dtx), deep learning's bi-directional long short-term memory (Bi-LSTM) model is applied for the classification of Rx and Tx mobilities to find the best MC model with respect to fraction of received molecules. Finally it is obtained that when the mobilities of Rx and Tx increase, the fraction of received molecules also increases. Bi-LSTM model of Deep learning is used on a data set consisting of five classes. The suggested model's accuracy, precision, and sensitivity values are obtained as 98.05, 96.49, and 98.01 percent, respectively.en_US
dc.identifier.doi10.1007/s12652-022-03790-4
dc.identifier.issn1868-5137
dc.identifier.issn1868-5145
dc.identifier.scopus2-s2.0-85127502770en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s12652-022-03790-4
dc.identifier.urihttps://hdl.handle.net/11616/100587
dc.identifier.wosWOS:000772726400002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of Ambient Intelligence and Humanized Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMolecular communicationen_US
dc.subjectDiffusion constanten_US
dc.subjectDeep learningen_US
dc.subjectBi-directional long short-term memoryen_US
dc.titleAnalysis and classification of the mobile molecular communication systems with deep learningen_US
dc.typeArticleen_US

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