A Prediction Model For Performance Analysis in Wireless Mesh Networks

Year 2016, Volume: 6 Issue: 3, 1241 - 1250, 05.11.2016

Şafak Durukan Odabaşı Ergün Gümüş

Abstract

Analysis of computer networks is an important
study field that must be handled carefully in order to make communication
systems work properly. Efficient evaluation and remodelling of system according
to factors affecting the performance is required. For this aim, many techniques
have been proposed, so far. However, machine learning methods are getting more
preferable than others with their cost-effective and faster solutions. In this
study, generalized regression neural networks (GRNNs) approach was employed in
order to predict the output, packets dropped of a sample DMesh network
simulation. The simulation is driven by parameters such as number of nodes,
number of gateways, number of channels used, and traffic density. It was
observed that parameters: traffic density and number of channels used, have a
direct impact on error rate of the regression model. The high variance
explained values show that GRNN approach can represent real characteristics of
DMesh architecture.

Keywords

Mesh networks, source management, prediction model, generalized regression neural networks, performance analysis

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