Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation

Basit Öğe Kaydı
dc.contributor.authorAkpamukcu, Mehmet
dc.contributor.authorAtes, Abdullah
dc.contributor.authorIsik, Ibrahim
dc.contributor.authorIsik, Esme
dc.date.accessioned2026-04-04T13:34:52Z
dc.date.available2026-04-04T13:34:52Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractThe analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p and ``N as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye [123E111]
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkiye with Project ID: 123E111.
dc.identifier.doi10.1016/j.nancom.2024.100532
dc.identifier.issn1878-7789
dc.identifier.issn1878-7797
dc.identifier.orcid0000-0003-1355-9420
dc.identifier.scopus2-s2.0-85202340113
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.nancom.2024.100532
dc.identifier.urihttps://hdl.handle.net/11616/109461
dc.identifier.volume42
dc.identifier.wosWOS:001305627300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofNano Communication Networks
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectMobile molecular communication
dc.subjectOptimization of optimization
dc.subjectDiffusion
dc.subjectMulti-verse optimization
dc.subjectGrey wolf optimization
dc.subjectDistribution functions
dc.titleOptimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu