Trends and patterns of broadband Internet access speed in a Nigerian university campus: A robust data exploration

Efficient broadband Internet access is required for optimal productivity in smart campuses. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. Data transmission speed and data reception speed were monitored and recorded on daily basis at Covenant University, Nigeria for a period of twelve months (January–December, 2017). The continuous data collection and logging were performed at the Network Operating Center (NOC) of the university using SolarWinds Orion software. Descriptive statistics, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple comparison post-hoc test are performed using MATLAB 2016a. Extensive statistical visualizations of the results obtained are presented in tables, graphs, and plots. Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses.


a b s t r a c t
Efficient broadband Internet access is required for optimal productivity in smart campuses. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. Data transmission speed and data reception speed were monitored and recorded on daily basis at Covenant University, Nigeria for a period of twelve months (January-December, 2017). The continuous data collection and logging were performed at the Network Operating Center (NOC) of the university using SolarWinds Orion software. Descriptive statistics, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple comparison post-hoc test are performed using MATLAB 2016a. Extensive statistical visualizations of the results obtained are presented in tables, graphs, and plots. Availability of these data will help network administrators to determine optimal network latency towards efficient deployment

Value of the data
The data provided in this data article include both peak and off-peak periods and these are valuable to the development of prediction or forecasting models for broadband communication networks in a smart campus environment [1,2].
Robust data exploration presented in this data article will facilitate effective bandwidth distribution and allocation based on need, priority, and desired Quality of Service [3][4][5].
Open access publication of these empirical data has an inherent ability to spur further evidencebased research on efficient bandwidth allocation and usage in computer networking [6][7][8].
Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses [9][10][11].

Data
Quantitative data on broadband Internet access speed in Covenant University are presented in a reusable format. The data presented are further explored to reveal useful insights that are needed for productive decision making based on statistical parameters used in [13][14][15][16][17][18]. Datasets on Internet transmission and reception speeds are extensively described by their statistical mean, median, mode, standard deviation, variance, kurtosis, Skewness, range, minimum, maximum, and sum as shown in Table 1 and Table 2 respectively. Fig. 1 and Fig. 2 show the quartiles, minimum, maximum, and outliers in the transmission data and the reception data using boxplots. Trends of broadband Internet access speed in the university were analyzed monthly and the resulting graphs for each quarter of the year 2017 are shown in Figs. 3-6. Similarly, the frequency distributions of the data are shown in Figs. 7-10.
The scatter plot shown in Fig. 11 illustrates the relationship between the data transmission speed and the data reception speed that were monitored and logged daily for a period of twelve months. A regression line, linear regression equation, and regression coefficient are made available on the scatter plot. In addition, probability distributions of the transmission speed and the reception speed were computed and the results are presented in Fig. 12 and Fig. 13 respectively. In like manner, the cumulative densities of the datasets are shown in Fig. 14 and Fig. 15. The Distribution fitting parameters for data transmission speed and data reception speed are presented in Table 3 and Table 4 respectively. The estimates and standard errors of the two datasets are given in Table 5 and Table 6.
The datasets were tested for statistical difference across the months of the year based on Analysis of Variance (ANOVA) and multiple post-hoc comparison tests. The results of the ANOVA and multiple post-hoc comparison tests for data transmission speed are presented in Table 7 and Table 8 respectively. Similarly, the results of the ANOVA and multiple post-hoc comparison tests for data reception  speed are presented in Table 9 and Table 10 respectively. Graphical representations of the results showing statistical difference in data transmission speed and data reception speed are shown in Fig. 16 and Fig. 17.

Experimental design, materials, and methods
A smart campus relies on robust and efficient broadband internet access for optimal functionality [12]. A case in point is Covenant University, Nigeria which currently has a subscription of seven Synchronous Transport Module level one (STM-1) from three Internet Service Providers (ISPs). For this massive investment to be justifiably utilized, precise knowledge of internet speed trend and pattern on both the uplink and downlink is essential. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. The data presented in this article will help in network planning towards guaranteeing desired QoS. Covenant University, an ICT-driven private university located in Nigeria, is serviced with highspeed broadband Internet by three ISPs through fiber optic communication links. Two of the ISPs utilize STM-1 with an equivalent maximum Internet speed of 310 Megabit per second (Mbps) while the third ISP provides three STM-1 with an equivalent maximum Internet speed of 465 Mbps. All fiber optic communication links terminated at the Network Operating Center (NOC), which distributes available broadband Internet access to all academic, administrative, and residential buildings in the university campus. The data transmission speed and the data reception speed were monitored and recorded on daily basis for a period of twelve months (January -December, 2017). The continuous data collection and logging were performed with the use of SolarWinds Orion software. The network monitoring tool was installed on the bare metal server in the NOC to ensure sufficient computing resources. To facilitate easy data reuse for reproducible research, empirical data obtained from the experimental process were properly sorted and preprocessed using Microsoft Excel (MS-Excel) 2013 version.