Yapay Sinir Ağları İle Kablosuz Yerel Alan Ağlarında Veri Trafiği Optimizasyonu

Yıl 2019, Cilt: 22 Sayı: 3, 737 - 747, 01.09.2019

Cemal Koçak Hacı Bayram Karakurt

https://doi.org/10.2339/politeknik.443219

Cited By: 3

Öz

Gelişen teknoloji ile birlikte kablosuz yerel alan ağlarında
(Wireless Local Area Network-WLAN)  hizmet
kalitesi (Quality of Service-QoS)  istekleri de artmaktadır. Yapılan birçok
çalışmada WLAN’larda hizmet kalitesini artırmak için çeşitli metotlar ve farklı
algoritmalar kullanılmıştır. WLAN’larda ortama erişim kontrol (Medium Access control-
MAC) katmanında hizmet kalitesini etkileyen RTS Eşik Değeri (RTSED), Parçalama
Eşik Değeri (PED) ve Arabellek Boyutu (AB) hizmet kalitesini direk
etkilemektedir. WLAN’larda hizmet kalitesini artırmak için kullanılan kanal
kullanım durumu, alınan veri trafiği ve gönderilen veri trafiği ağdaki performansını
etkileyen önemli parametrelerdir. Bu çalışmada WLAN’larda Yapay Sinir Ağları (Artificial
Neural Networks-ANN) kullanılarak RTSED, PED ve AB parametreleri optimize
edilip, alınan veri trafiği ve gönderilen veri trafiğinin ideal değerleri elde
edilmiştir. Riverbed Modeler simülasyon aracı kullanılarak 11 düğümlü ve 27 farklı
giriş değerleri seçilerek kanal kullanım durumu elde edilmiştir. Alınan veri
trafiği ile gönderilen veri trafiği sonuç değerleri ANN Modellemesi ile WLAN’larda
performansın tahminini sağlanmıştır.

Anahtar Kelimeler

Kablosuz yerel alan ağları, yapay sinir ağları, alınan veri trafiği

Data Traffic Optimization in Wireless Local Area Networks with Artificial Neural Networks

Öz

In recent years,
quality of service (QoS) has been rapidly increasing in wireless local area
networks (WLANs) with the increasing technology. In many studies, various methods and different
algorithms are used to improve the quality of service in WLANs. RTS Threshold
Value (RTSTV), Fragmentation Threshold Value (FTV) and Buffer Size (BS) are affect
service quality directly at MAC (Medium Access Control) layer in WLAN. Channel
utilization, data traffic received and data traffic sent parameters are
important improve quality of service in WLANs. In this study, RTSED, PED and AB
parameters were optimized by using Artificial Neural Networks (ANN) in WLAN and
ideal values of received data traffic and received data traffic were obtained. Using the Riverbed Modeler simulation tool, 11 nodes
and 27 different input values were selected to obtain channel utilization
status. With the ANN Modeling of the results of the data traffic sent and data
traffic received provides the estimation of the performance in the WLAN. It is
observed that the average squared error value of 1000 epoch training result is
less than 10^-6, and that the test and estimation abilities are
larger than 10^-6. According to this value, it means that the
improved YSA model can not memorize, it can establish a relation between input
and output data. Thus, it is proved that the best learning values are
obtained with these results obtained by the developed model.

Anahtar Kelimeler

Wireless local area network, artificial neural networks, data traffic received, data traffic sent

Kaynakça

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