Performance of multivariable traffic model that allows estimating Throughput mean values
The present paper is aimed at developing a multi-variable traffic model of a Wi-Fi data network that allows estimating throughput mean values. In order to construct the model, data corresponding to an 8-host wireless adhoc network were collected using a software package called WireShark; the networ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad de Antioquia
2013-08-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16310 |
| _version_ | 1797861683543146496 |
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| author | Cesar Hernández C. Salgado O. Salcedo |
| author_facet | Cesar Hernández C. Salgado O. Salcedo |
| author_sort | Cesar Hernández |
| collection | DOAJ |
| description |
The present paper is aimed at developing a multi-variable traffic model of a Wi-Fi data network that allows estimating throughput mean values. In order to construct the model, data corresponding to an 8-host wireless adhoc network were collected using a software package called WireShark; the network was specially designed for modeling purposes. Subsequently, the most convenient multi-variable models were estimated according to the traffic features extracted from the collected data. Results were the evaluated using a software package called STATA, leading to the establishment of significant explanatory variables for the model and its performance levels. For our Wi-Fi network, results show that the analyzed traffic exhibits selfsimilarity features. Additionally, model coefficients and their corresponding significance levels are shown in various Tables. Finally, an explanatory multivariable model consisting of four variables was produced on the basis of ordinary least-squares methodologies (with a per-cent error of 22.16). The findings suggest that the multi-variable traffic model produced in this study allows a reliable analysis of throughput mean values; however, the model is limited when predicting traffic values for data outside the selected estimation set.
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| first_indexed | 2024-04-09T22:07:04Z |
| format | Article |
| id | doaj.art-52be49d33d8f47408ed3c9fe6c96d393 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:07:04Z |
| publishDate | 2013-08-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-52be49d33d8f47408ed3c9fe6c96d3932023-03-23T12:34:27ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442013-08-016710.17533/udea.redin.16310Performance of multivariable traffic model that allows estimating Throughput mean valuesCesar Hernández0C. Salgado1O. Salcedo2Francisco José de Caldas District UniversityFrancisco José de Caldas District UniversityFrancisco José de Caldas District University The present paper is aimed at developing a multi-variable traffic model of a Wi-Fi data network that allows estimating throughput mean values. In order to construct the model, data corresponding to an 8-host wireless adhoc network were collected using a software package called WireShark; the network was specially designed for modeling purposes. Subsequently, the most convenient multi-variable models were estimated according to the traffic features extracted from the collected data. Results were the evaluated using a software package called STATA, leading to the establishment of significant explanatory variables for the model and its performance levels. For our Wi-Fi network, results show that the analyzed traffic exhibits selfsimilarity features. Additionally, model coefficients and their corresponding significance levels are shown in various Tables. Finally, an explanatory multivariable model consisting of four variables was produced on the basis of ordinary least-squares methodologies (with a per-cent error of 22.16). The findings suggest that the multi-variable traffic model produced in this study allows a reliable analysis of throughput mean values; however, the model is limited when predicting traffic values for data outside the selected estimation set. https://revistas.udea.edu.co/index.php/ingenieria/article/view/16310taffic model multi-variable model Wi-Fi networks throughput |
| spellingShingle | Cesar Hernández C. Salgado O. Salcedo Performance of multivariable traffic model that allows estimating Throughput mean values Revista Facultad de Ingeniería Universidad de Antioquia taffic model multi-variable model Wi-Fi networks throughput |
| title | Performance of multivariable traffic model that allows estimating Throughput mean values |
| title_full | Performance of multivariable traffic model that allows estimating Throughput mean values |
| title_fullStr | Performance of multivariable traffic model that allows estimating Throughput mean values |
| title_full_unstemmed | Performance of multivariable traffic model that allows estimating Throughput mean values |
| title_short | Performance of multivariable traffic model that allows estimating Throughput mean values |
| title_sort | performance of multivariable traffic model that allows estimating throughput mean values |
| topic | taffic model multi-variable model Wi-Fi networks throughput |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16310 |
| work_keys_str_mv | AT cesarhernandez performanceofmultivariabletrafficmodelthatallowsestimatingthroughputmeanvalues AT csalgado performanceofmultivariabletrafficmodelthatallowsestimatingthroughputmeanvalues AT osalcedo performanceofmultivariabletrafficmodelthatallowsestimatingthroughputmeanvalues |