Exploration of daily Internet data traffic generated in a smart university campus

Abstract

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.

Data mining approach for predicting the daily Internet data traffic of a smart university

“…In this study, the internet traffic data of Covenant University in Nigeria over a period of one year was evaluated and analysed using predictive data mining algorithms. The data was logged using Mikrotik Hotspot Manager and FreeRADIUS, Radius Manager Web application deployed on LINUX platform as implemented by Adeyemi et al [18] through the SmartCU cluster. The dataset logged contains the Upload (in GigaBytes) and the download (in GigaBytes) internet traffic data from the 1st of January to the 19th of December when the school closed for the year in 2017.…”

“…Apart from the analysis of internet traffic for network security reasons, internet data traffic carries a lot of useful information about the originating network. The daily volumetric variation of internet traffic creates usage pattern that can be deployed for predictive analysis which will help network engineers in preparing the network adequately for anticipated heavy internet traffic so as to ensure optimal quality of service [18][19][20]. Also, the quality of packet traffic may be impaired by packet losses [21][22][23][24].…”

Internet data traffic analysis for identifying usage trends on each day of the week in a university

“…The internet traffic data of Covenant University in Nigeria for a period of one year, spanning over 51 weeks was logged during the empirical study by Adeyemi et al (2018) [21], and this provides an opportunity to carry out trend analysis on a real internet traffic data obtained practically by logging the internet traffic data using Mikrotik Hotspot Manager, FreeRADIUS, and web-based Radius Manager application. In this study, the internet upload and download traffic dataset was pre-processed and sorted according to the particular day of the week i.e.…”

“…Extensive studies have been carried out on internet traffic monitoring, and this is an indicator of its importance in network management for identifying heavy bandwidth internet traffic for controlling applications that utilize high bandwidth [11] and ultimately for ensuring high quality of service [8,[20][21][22][23]. In this study, the internet traffic data generated in a smart university in Nigeria is statistically analysed to identify usage trends on each of the seven days of the week over a period of one year.…”

Traffic model using a novel sniffer that ensures the user data privacy

“…In[12] Zhang et al evaluate the amount of UDP and TCP traffic, in terms of flows, packets and bytes. A work over internet data traffic generated in a university campus and a model for predict internet data traffic is present in[13]. Cao et al in[14] demonstrate that the number of active connections has an effect on traffic characteristics.Regarding the traffic modelling, Vicari present in[15] a model for internet traffic from the user perspective, using distribution functions applied to data.…”