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. 2018 Jul 27;20:30–52. doi: 10.1016/j.dib.2018.07.039

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

Oluwaseun J Adeyemi a, Segun I Popoola b,⁎, Aderemi A Atayero b, David G Afolayan a, Mobolaji Ariyo a, Emmanuel Adetiba a,b,c
PMCID: PMC6083017  PMID: 30101162

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.

Keywords: Smart campus, Internet Protocol, Internet data traffic, Nigerian university, Smart education

Specifications Table

Subject area Engineering
More specific subject area Information and Communication Engineering
Type of data Tables, graphs, figures, and spreadsheet file
How data was acquired 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 an open source software, FreeRADIUS, Radius Manager web application, and Mikrotik Hotspot Manager.
Data format Raw, analyzed
Experimental factors Internet data download traffic and Internet data upload traffic were monitored and logged for only nineteen (19) days in December, 2017 because the university proceeded to end-of-year break afterward.
Experimental features Descriptive statistics, boxplot representations, time series plots, frequency distributions, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple post-hoc test are performed to explore the dataset provided in this data article. All statistical computations were done using the Machine Learning and Statistics toolbox in MATLAB 2016a software.
Data source location The dataset on Internet data traffic provided in this article were collected at Covenant University, Canaanland, Ota, Nigeria (Latitude 6.6718° N, Longitude 3.1581° E)
Data accessibility A comprehensive dataset is provided in Microsoft Excel spreadsheet file and attached assupplementary materialto this data article for easy research utility and validation
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Value of the data

  • •

    The data provided in this data article can be used to accurately predict Internet data traffic in a smart campus environment. Predictions of Internet data traffic will help network engineers to improve the Quality of Service (QoS) of computer networks and also ensure efficient utilization of the networks in a smart university campus [1], [2].

  • •

    Availability of dataset on Internet data traffic obtained from real scenarios will facilitate more empirical research in the areas of computer networking and Internet traffic engineering [3], [4].

  • •

    This dataset is made available to give correct facts and figures on Internet data traffic in a Nigerian university campus that is driven by Information and Communication Technologies (ICTs) [5], [6].

  • •

    Free access to daily Internet data traffic of a period of one year will facilitate the development of empirical prediction models that can be used by Internet Service Providers (ISPs) and Internet subscribers in a smart university campus for effective network planning and traffic forecasting [7], [8], [9], [10], [11], [12].

  • •

    Robust data exploration that is performed in this data article will help the university network administrators to gain useful insights about the traffic peak and off-peak periods. Also, the descriptive statistics, frequency and probability distribution plots, correlation analysis, ANOVA test and multiple post-hoc test results will give better understanding of the relationships between the Internet data download traffic and the Internet data upload traffic in a smart campus [13], [14], [15].

1. Data

Ubiquitous access to reliable Internet services is pivotal to achieving sustainable smart education in university campuses [16], [17], [18]. Accurate Internet data traffic prediction models are required for computer network planning and forecasting to guarantee efficient Quality of Service (QoS) in enterprise computer networks and applications. However, computer network planning are usually carried out based on theoretical formulations and simulations due to paucity of empirical data from real life scenarios. In this data article, a robust data exploration is performed on daily Internet data traffic in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017).

For each month, descriptive statistics of daily Internet data download traffic and daily Internet data upload traffic are presented in tables. The mean, median, mode, standard deviation, variance, kurtosis, Skewness, range, minimum, maximum, and sum of the daily Internet data traffic download and upload for January–December, 2017 are presented in Tables 1 and 2 respectively.

Table 1.

Descriptive statistics of daily IP data download traffic in Terabytes (TB).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean 2.28 2.30 2.88 2.72 2.41 2.23 1.89 1.20 3.15 3.20 3.17 2.33
Median 2.60 2.40 2.90 2.60 2.20 2.20 1.90 0.92 3.25 3.20 3.00 2.40
Mode 3.40 2.00 2.90 2.50 2.00 2.00 2.10 0.82 3.50 3.00 3.00 2.40
Standard Deviation 1.21 0.66 0.79 0.52 0.82 0.49 0.71 0.80 0.40 0.49 0.69 0.97
Variance 1.46 0.44 0.63 0.27 0.66 0.24 0.51 0.64 0.16 0.24 0.48 0.94
Kurtosis 1.61 4.38 5.12 3.80 3.18 3.67 5.58 6.03 2.63 1.78 5.56 3.96
Skewness −0.47 −1.16 0.48 −0.16 0.95 0.81 0.87 2.00 −0.69 0.11 0.76 0.75
Range 3.51 2.74 4.20 2.60 3.30 2.00 3.70 3.30 1.50 1.60 3.80 4.09
Minimum 0.19 0.36 1.20 1.30 1.20 1.50 0.60 0.50 2.20 2.40 1.40 0.81
Maximum 3.70 3.10 5.40 3.90 4.50 3.50 4.30 3.80 3.70 4.00 5.20 4.90
Sum 70.81 64.36 89.40 81.60 74.80 67.00 58.46 37.20 94.40 99.10 95.00 44.21
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Table 2.

Descriptive statistics of daily IP data upload traffic in Terabytes (TB).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean 0.29 0.43 0.49 0.48 0.36 0.31 0.28 0.19 0.58 0.65 0.64 0.33
Median 0.32 0.47 0.47 0.50 0.29 0.31 0.29 0.15 0.59 0.64 0.65 0.28
Mode 0.14 0.07 0.15 0.14 0.14 0.20 0.06 0.06 0.40 0.69 0.23 0.14
Standard Deviation 0.16 0.14 0.14 0.09 0.18 0.06 0.11 0.14 0.06 0.06 0.12 0.17
Variance 0.02 0.02 0.02 0.01 0.03 0.00 0.01 0.02 0.00 0.00 0.02 0.03
Kurtosis 1.71 3.55 5.43 8.61 3.49 2.45 2.55 5.07 4.47 2.89 7.23 6.52
Skewness −0.24 −1.04 0.19 −2.07 1.19 0.18 −0.07 1.80 −1.16 0.16 −0.39 1.74
Range 0.51 0.56 0.73 0.47 0.66 0.25 0.48 0.52 0.27 0.28 0.77 0.72
Minimum 0.03 0.07 0.15 0.14 0.14 0.20 0.06 0.06 0.40 0.53 0.23 0.14
Maximum 0.54 0.63 0.88 0.60 0.80 0.44 0.54 0.58 0.67 0.80 1.00 0.86
Sum 8.96 12.17 15.12 14.27 11.26 9.23 8.66 5.94 17.29 20.10 19.06 6.24
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2. Experimental design, materials and methods

A robust data exploration was performed on daily Internet data traffic 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 an open source software, FreeRADIUS, Radius Manager web application, and Mikrotik Hotspot Manager. FreeRADIUS software was installed in Linux Operating System (OS) for authentication, authorization, and accounting services. Radius Manager Web application was used to add users, to edit and create cards, and to harvest data in a more user-friendly format. Mikrotik Hotspot Manager was used to integrate the smart campus network to the enterprise edge. Statistical computations were done using the Machine Learning and Statistics toolbox in MATLAB 2016a software. Boxplot representations of the daily download traffic and the daily upload traffic for the 12-month period are shown in Figs. 1 and 2 respectively.

Fig. 1.

Fig. 1

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Boxplot representation of daily data download traffic in Terabytes (TB).

Fig. 2.

Fig. 2

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Boxplot representation of daily data upload traffic in Terabytes (TB).

3. Data exploration

Time series plots are provided to show the trends of data download and upload volume within the smart campus throughout the 12-month period. Fig. 3, Fig. 4, Fig. 5, Fig. 6 show the trends in data download traffic for the first, second, third, and fourth quarters of year 2017 respectively. Similarly, the patterns of data upload traffic for the first, second, third, and fourth quarters of year 2017 are shown in Fig. 7, Fig. 8, Fig. 9, Fig. 10 respectively. Frequency distributions of the dataset are illustrated using histograms. Fig. 11, Fig. 12, Fig. 13, Fig. 14 show the histogram distributions of the data traffic volume for first, second, third, and fourth quarters of 2017.

Fig. 3.

Fig. 3

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(a)–(c). Download traffic volume in first quarter, 2017.

Fig. 4.

Fig. 4

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(a)–(c). Download traffic volume in second quarter, 2017.

Fig. 5.

Fig. 5

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(a)–(c). Download traffic volume in third quarter, 2017.

Fig. 6.

Fig. 6

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(a)–(c). Download traffic volume in fourth quarter, 2017.

Fig. 7.

Fig. 7

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(a)–(c). Upload traffic volume in first quarter, 2017.

Fig. 8.

Fig. 8

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(a)–(c). Upload traffic volume in second quarter, 2017.

Fig. 9.

Fig. 9

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(a)–(c). Upload traffic volume in third quarter, 2017.

Fig. 10.

Fig. 10

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(a)–(c). Upload traffic volume in fourth quarter, 2017.

Fig. 11.

Fig. 11

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(a)–(f). Frequency distributions of data download and upload traffic in first quarter, 2017.

Fig. 12.

Fig. 12

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(a)–(f). Frequency distributions of data download and upload traffic in second quarter, 2017.

Fig. 13.

Fig. 13

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(a)–(f). Frequency distributions of data download and upload traffic in third quarter, 2017.

Fig. 14.

Fig. 14

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(a)–(f). Frequency distributions of data download and upload traffic in fourth quarter, 2017.

Correlation and regression analyses are performed to establish a linear relationship the data download traffic and data upload traffic. The relationship yielded a correlation coefficient (R) of 0.8791. A linear regression equation that represent the relationship is provided in the scatter plot shown in Fig. 15. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of the dataset are also computed. PDF and CDF models of Normal, Logistic, Non-parametric, Rician, Weibull and Nakagami distributions were used to fit the empirical data as shown in Figs. 16 and 17. The CDF model distribution fittings of the dataset are shown in Figs. 18 and 19. Distribution fitting parameters for download data traffic (January–December, 2017) based on the six distribution models are presented in Table 3. Estimates and standard errors of download data traffic distribution parameters for the six models are presented in Table 4. Similarly, the distribution fitting parameters for upload data traffic (January–December, 2017) based on the six distribution models are presented in Table 5. Estimates and standard errors of download data traffic distribution parameters for the six models are presented in Table 6.

Fig. 15.

Fig. 15

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Scatter plot of data download traffic and data upload traffic.

Fig. 16.

Fig. 16

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Download data traffic distribution fittings using PDF models.

Fig. 17.

Fig. 17

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Upload data traffic distribution fittings using PDF models.

Fig. 18.

Fig. 18

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Download data traffic distribution fittings using CDF models.

Fig. 19.

Fig. 19

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Upload data traffic distribution fittings using CDF models.

Table 3.

Distribution fitting parameters for download data traffic (January–December, 2017).

Normal Logistic Rician Weibull Nakagami
Log Likelihood −473.562 −477.028 −472.879 −475.457 −485.289
Domain −∞<y<∞ −∞<y<∞ 0<y<∞ 0<y<∞ 0<y<∞
Mean 2.483 2.524 2.482 2.477 2.462
Variance 0.859 0.919 0.858 0.8313 0.958
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Table 4.

Estimates and standard errors of download data traffic distribution parameters.

Normal
 Logistic
 Rician
 Weibull
 Nakagami
Parameter Approx Std Err Approx Std Err Approx Std Err Approx Std Err Approx Std Err
µ 2.483 0.049 2.524 0.049 2.249 0.061 2.776 0.052 1.680 0.116
σ 0.927 0.035 0.528 0.023 0.991 0.043 2.958 0.127 7.020 0.288
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Table 5.

Distribution fitting parameters for upload data traffic (January–December, 2017).

Normal Logistic Rician Weibull Nakagami
Log Likelihood 86.969 75.92 93.032 90.011 86.668
Domain −∞<y<∞ −∞<y<∞ 0<y<∞ 0<y<∞ 0<y<∞
Mean 0.420 0.423 0.421 0.420 0.417
Variance 0.0359 0.041 0.035 0.035 0.038
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Table 6.

Estimates and standard errors of upload data traffic distribution parameters.

Normal
 Logistic
 Rician
 Weibull
 Nakagami
Parameter Approx Std Err Approx Std Err Approx Std Err Approx Std Err Approx Std Err
µ 0.420 0.010 0.422 0.010 0.345 0.017 0.473 0.011 1.211 0.082
σ 0.189 0.007 0.112 0.004 0.216 0.012 2.375 0.104 0.212 0.010
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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 results of the ANOVA test and the multiple post-hoc test conducted on download data traffic are presented in Tables 7 and 8 respectively. Likewise, the results of the ANOVA test and the multiple post-hoc test conducted on upload data traffic are presented in Tables 9 and 10 respectively. The multiple post-hoc comparison results for download data traffic and upload data traffic are depicted graphically in Figs. 20 and 21.

Table 7.

ANOVA test results for download data traffic.

Source of Variation Sum of Squares Degree of Freedom Mean Squares F Statistic Prob>F
Columns 116.44 11 10.59 19.41 3.16×10−30
Error 185.93 341 0.55
Total 302.37 352
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Table 8.

Multiple post-hoc test results for download data traffic.

Groups Compared Lower limits for 95% confidence intervals Mean Difference Upper limits for 95% confidence intervals p-value
Jan Feb −0.6435 −0.0144 0.6148 1.0000
Jan Mar −1.2126 −0.5997 0.0133 0.0619
Jan Apr −1.0538 −0.4358 0.1822 0.4732
Jan May −0.7416 −0.1287 0.4842 0.9999
Jan Jun −0.5672 0.0509 0.6689 1.0000
Jan Jul −0.2145 0.3984 1.0113 0.6053
Jan Aug 0.4713 1.0842 1.6971 0.0000
Jan Sep −1.4805 −0.8625 −0.2445 0.0003
Jan Oct −1.5255 −0.9126 −0.2996 0.0001
Jan Nov −1.5005 −0.8825 −0.2645 0.0002
Jan Dec −0.7457 −0.0426 0.6604 1.0000
Feb Mar −1.2144 −0.5853 0.0438 0.0971
Feb Apr −1.0555 −0.4214 0.2127 0.5702
Feb May −0.7435 −0.1143 0.5148 1.0000
Feb Jun −0.5689 0.0652 0.6993 1.0000
Feb Jul −0.2164 0.4128 1.0419 0.5907
Feb Aug 0.4694 1.0986 1.7277 0.0000
Feb Sep −1.4822 −0.8481 −0.2140 0.0008
Feb Oct −1.5273 −0.8982 −0.2691 0.0002
Feb Nov −1.5022 −0.8681 −0.2340 0.0005
Feb Dec −0.7455 −0.0283 0.6890 1.0000
Mar Apr −0.4541 0.1639 0.7819 0.9994
Mar May −0.1420 0.4710 1.0839 0.3327
Mar Jun 0.0325 0.6505 1.2686 0.0288
Mar Jul 0.3851 0.9981 1.6110 0.0000
Mar Aug 1.0709 1.6839 2.2968 0.0000
Mar Sep −0.8808 −0.2628 0.3552 0.9658
Mar Oct −0.9258 −0.3129 0.3000 0.8827
Mar Nov −0.9008 −0.2828 0.3352 0.9423
Mar Dec −0.1461 0.5570 1.2601 0.2857
Apr May −0.3109 0.3071 0.9251 0.9007
Apr Jun −0.1364 0.4867 1.1097 0.3072
Apr Jul 0.2162 0.8342 1.4522 0.0006
Apr Aug 0.9020 1.5200 2.1380 0.0000
Apr Sep −1.0497 −0.4267 0.1964 0.5217
Apr Oct −1.0948 −0.4768 0.1412 0.3264
Apr Nov −1.0697 −0.4467 0.1764 0.4458
Apr Dec −0.3144 0.3932 1.1007 0.8096
May Jun −0.4384 0.1796 0.7976 0.9986
May Jul −0.0858 0.5271 1.1400 0.1754
May Aug 0.6000 1.2129 1.8258 0.0000
May Sep −1.3518 −0.7338 −0.1157 0.0059
May Oct −1.3968 −0.7839 −0.1709 0.0017
May Nov −1.3718 −0.7538 −0.1357 0.0039
May Dec −0.6170 0.0861 0.7891 1.0000
Jun Jul −0.2705 0.3475 0.9655 0.7972
Jun Aug 0.4153 1.0333 1.6514 0.0000
Jun Sep −1.5364 −0.9133 −0.2903 0.0001
Jun Oct −1.5815 −0.9634 −0.3454 0.0000
Jun Nov −1.5564 −0.9333 −0.3103 0.0001
Jun Dec −0.8010 −0.0935 0.6140 1.0000
Jul Aug 0.0729 0.6858 1.2987 0.0136
Jul Sep −1.8789 −1.2609 −0.6428 0.0000
Jul Oct −1.9239 −1.3110 −0.6980 0.0000
Jul Nov −1.8989 −1.2809 −0.6628 0.0000
Jul Dec −1.1441 −0.4410 0.2620 0.6587
Aug Sep −2.5647 −1.9467 −1.3286 0.0000
Aug Oct −2.6097 −1.9968 −1.3838 0.0000
Aug Nov −2.5847 −1.9667 −1.3486 0.0000
Aug Dec −1.8299 −1.1268 −0.4238 0.0000
Sep Oct −0.6681 −0.0501 0.5679 1.0000
Sep Nov −0.6431 −0.0200 0.6031 1.0000
Sep Dec 0.1123 0.8198 1.5273 0.0084
Oct Nov −0.5879 0.0301 0.6481 1.0000
Oct Dec 0.1669 0.8699 1.5730 0.0031
Nov Dec 0.1323 0.8398 1.5473 0.0059
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Table 9.

ANOVA test results for upload data traffic.

Source of Variation Sum of Squares Degree of Freedom Mean Squares F Statistic Prob > F
Columns 7.38 11 0.67 43.58 2.03 × 10−58
Error 5.25 341 0.02
Total 12.63 352
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Table 10.

Multiple post-hoc test results for upload data traffic.

Groups Compared Lower limits for 95% confidence intervals Mean Difference Upper limits for 95% confidence intervals p-value
Jan Feb −0.2513 −0.1456 −0.0399 0.0004
Jan Mar −0.3018 −0.1988 −0.0958 0.0000
Jan Apr −0.2904 −0.1866 −0.0827 0.0000
Jan May −0.1771 −0.0741 0.0289 0.4391
Jan Jun −0.1224 −0.0186 0.0852 1.0000
Jan Jul −0.0933 0.0097 0.1126 1.0000
Jan Aug −0.0056 0.0974 0.2004 0.0843
Jan Sep −0.3911 −0.2873 −0.1835 0.0000
Jan Oct −0.4622 −0.3593 −0.2563 0.0000
Jan Nov −0.4499 −0.3461 −0.2422 0.0000
Jan Dec −0.1573 −0.0392 0.0789 0.9953
Feb Mar −0.1589 −0.0532 0.0525 0.8930
Feb Apr −0.1474 −0.0409 0.0656 0.9843
Feb May −0.0342 0.0715 0.1772 0.5413
Feb Jun 0.0205 0.1270 0.2336 0.0055
Feb Jul 0.0496 0.1553 0.2610 0.0001
Feb Aug 0.1374 0.2431 0.3488 0.0000
Feb Sep −0.2482 −0.1416 −0.0351 0.0009
Feb Oct −0.3193 −0.2136 −0.1079 0.0000
Feb Nov −0.3070 −0.2004 −0.0939 0.0000
Feb Dec −0.0141 0.1064 0.2269 0.1456
Mar Apr −0.0916 0.0122 0.1161 1.0000
Mar May 0.0217 0.1247 0.2277 0.0044
Mar Jun 0.0764 0.1802 0.2840 0.0000
Mar Jul 0.1055 0.2085 0.3114 0.0000
Mar Aug 0.1932 0.2962 0.3992 0.0000
Mar Sep −0.1923 −0.0885 0.0153 0.1862
Mar Oct −0.2634 −0.1605 −0.0575 0.0000
Mar Nov −0.2511 −0.1473 −0.0434 0.0002
Mar Dec 0.0415 0.1596 0.2777 0.0006
Apr May 0.0086 0.1124 0.2163 0.0206
Apr Jun 0.0633 0.1680 0.2726 0.0000
Apr Jul 0.0924 0.1962 0.3000 0.0000
Apr Aug 0.1801 0.2840 0.3878 0.0000
Apr Sep −0.2054 −0.1007 0.0039 0.0723
Apr Oct −0.2765 −0.1727 −0.0689 0.0000
Apr Nov −0.2642 −0.1595 −0.0548 0.0000
Apr Dec 0.0285 0.1473 0.2662 0.0030
May Jun −0.0483 0.0555 0.1594 0.8460
May Jul −0.0192 0.0838 0.1868 0.2473
May Aug 0.0686 0.1715 0.2745 0.0000
May Sep −0.3170 −0.2132 −0.1093 0.0000
May Oct −0.3881 −0.2851 −0.1822 0.0000
May Nov −0.3758 −0.2719 −0.1681 0.0000
May Dec −0.0832 0.0349 0.1530 0.9983
Jun Jul −0.0756 0.0283 0.1321 0.9992
Jun Aug 0.0122 0.1160 0.2198 0.0139
Jun Sep −0.3734 −0.2687 −0.1640 0.0000
Jun Oct −0.4445 −0.3407 −0.2368 0.0000
Jun Nov −0.4321 −0.3275 −0.2228 0.0000
Jun Dec −0.1395 −0.0206 0.0983 1.0000
Jul Aug −0.0152 0.0878 0.1907 0.1862
Jul Sep −0.4008 −0.2969 −0.1931 0.0000
Jul Oct −0.4719 −0.3689 −0.2659 0.0000
Jul Nov −0.4596 −0.3557 −0.2519 0.0000
Jul Dec −0.1670 −0.0489 0.0693 0.9721
Aug Sep −0.4885 −0.3847 −0.2809 0.0000
Aug Oct −0.5597 −0.4567 −0.3537 0.0000
Aug Nov −0.5473 −0.4435 −0.3396 0.0000
Aug Dec −0.2548 −0.1366 −0.0185 0.0086
Sep Oct −0.1758 −0.0720 0.0319 0.5017
Sep Nov −0.1635 −0.0588 0.0459 0.7988
Sep Dec 0.1292 0.2481 0.3669 0.0000
Oct Nov −0.0906 0.0132 0.1170 1.0000
Oct Dec 0.2019 0.3200 0.4382 0.0000
Nov Dec 0.1880 0.3069 0.4257 0.0000
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Fig. 20.

Fig. 20

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Graphical representation of multiple post-hoc test result for download data traffic.

Fig. 21.

Fig. 21

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Graphical representation of multiple post-hoc test result for download data traffic.

Acknowledgements

This work is carried out under the IoT-Enabled Smart and Connected Communities (SmartCU) Research Cluster. This research is fully sponsored by Covenant University Centre for Research, Innovation and Development (CUCRID), Covenant University, Ota, Nigeria.

Footnotes

Appendix A

Supplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2018.07.039.

Transparency document

Transparency data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2018.07.039.

Appendix A. Supplementary material

Supplementary material.

mmc1.xlsx (26.7KB, xlsx)

.

Transparency document. Supplementary material

Supplementary material.

mmc2.docx (17.9KB, docx)

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