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Exploration of daily Internet data traffic generated in a smart university campus
In this data article, a robust data exploration is performed on daily Internet data traffic generated in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017). For each day of the one-year study period, Internet data download traffic and Internet data uplo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083017/ https://www.ncbi.nlm.nih.gov/pubmed/30101162 http://dx.doi.org/10.1016/j.dib.2018.07.039 |
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author | Adeyemi, Oluwaseun J. Popoola, Segun I. Atayero, Aderemi A. Afolayan, David G. Ariyo, Mobolaji Adetiba, Emmanuel |
author_facet | Adeyemi, Oluwaseun J. Popoola, Segun I. Atayero, Aderemi A. Afolayan, David G. Ariyo, Mobolaji Adetiba, Emmanuel |
author_sort | Adeyemi, Oluwaseun J. |
collection | PubMed |
description | In this data article, a robust data exploration is performed on daily Internet data traffic generated in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017). For each day of the one-year study period, Internet data download traffic and Internet data upload traffic at Covenant University, Nigeria were monitored and properly logged using required application software namely: FreeRADIUS; Radius Manager Web application; and Mikrotik Hotspot Manager. A comprehensive dataset with detailed information is provided as supplementary material to this data article for easy research utility and validation. For each month, descriptive statistics of daily Internet data download traffic and daily Internet data upload traffic are presented in tables. Boxplot representations and time series plots are provided to show the trends of data download and upload traffic volume within the smart campus throughout the 12-month period. Frequency distributions of the dataset are illustrated using histograms. In addition, correlation and regression analyses are performed and the results are presented using a scatter plot. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of the dataset are also computed. Furthermore, Analysis of Variance (ANOVA) and multiple post-hoc tests are conducted to understand the statistical difference(s) in the Internet traffic volume, if any, across the 12-month period. The robust data exploration provided in this data article will help Internet Service Providers (ISPs) and network administrators in smart campuses to develop empirical model for optimal Quality of Service (QoS), Internet traffic forecasting, and budgeting. |
format | Online Article Text |
id | pubmed-6083017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-60830172018-08-10 Exploration of daily Internet data traffic generated in a smart university campus Adeyemi, Oluwaseun J. Popoola, Segun I. Atayero, Aderemi A. Afolayan, David G. Ariyo, Mobolaji Adetiba, Emmanuel Data Brief Earth and Planetary Science In this data article, a robust data exploration is performed on daily Internet data traffic generated in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017). For each day of the one-year study period, Internet data download traffic and Internet data upload traffic at Covenant University, Nigeria were monitored and properly logged using required application software namely: FreeRADIUS; Radius Manager Web application; and Mikrotik Hotspot Manager. A comprehensive dataset with detailed information is provided as supplementary material to this data article for easy research utility and validation. For each month, descriptive statistics of daily Internet data download traffic and daily Internet data upload traffic are presented in tables. Boxplot representations and time series plots are provided to show the trends of data download and upload traffic volume within the smart campus throughout the 12-month period. Frequency distributions of the dataset are illustrated using histograms. In addition, correlation and regression analyses are performed and the results are presented using a scatter plot. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of the dataset are also computed. Furthermore, Analysis of Variance (ANOVA) and multiple post-hoc tests are conducted to understand the statistical difference(s) in the Internet traffic volume, if any, across the 12-month period. The robust data exploration provided in this data article will help Internet Service Providers (ISPs) and network administrators in smart campuses to develop empirical model for optimal Quality of Service (QoS), Internet traffic forecasting, and budgeting. Elsevier 2018-07-27 /pmc/articles/PMC6083017/ /pubmed/30101162 http://dx.doi.org/10.1016/j.dib.2018.07.039 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Earth and Planetary Science Adeyemi, Oluwaseun J. Popoola, Segun I. Atayero, Aderemi A. Afolayan, David G. Ariyo, Mobolaji Adetiba, Emmanuel Exploration of daily Internet data traffic generated in a smart university campus |
title | Exploration of daily Internet data traffic generated in a smart university campus |
title_full | Exploration of daily Internet data traffic generated in a smart university campus |
title_fullStr | Exploration of daily Internet data traffic generated in a smart university campus |
title_full_unstemmed | Exploration of daily Internet data traffic generated in a smart university campus |
title_short | Exploration of daily Internet data traffic generated in a smart university campus |
title_sort | exploration of daily internet data traffic generated in a smart university campus |
topic | Earth and Planetary Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083017/ https://www.ncbi.nlm.nih.gov/pubmed/30101162 http://dx.doi.org/10.1016/j.dib.2018.07.039 |
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