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To ensure trace-ability of data at each stage of the analyses process, analysis results need to be saved and securely stored to ensure the quality of data and the ability to return to any point of the process and revisit the results developed at each stage. This data management allowed for the ability to fork the research in various directions without compromising the original data validity.

Having prepared and formalised various datasets, the initial analysis focused on the WiFi logs as introduced earlier in Section2.2and the examination of these for quantitative traits and data patterns. This involved the testing of the dataset to determine the scope of useful knowledge that could be extracted. The testing commenced by determining that the Eduroam network could accurately track and record user devices as they traversed the campus. To test this hypothesis a small sample of volunteers were canvased to allow the mining of their WiFi log data. The five volunteers had different daily routines affording the

DATE Time NAS identifier Location Room

12/12/14 08:52:27 HG-86E5DB Henry Grattan building CG12

08:52:44 HG-86E758 Henry Grattan building C105

08:53:08 HELIX STUDIO AP34 Helix Theatre AP34

08:53:28 HELIX BLUEROOM AP37 Helix Theatre AP37

08:53:48 Computing-34F5AC School of Computing L101 08:54:27 Computing-34F5D8 School of Computing L208

08:57:07 Computing LG25 School of Computing LG25

08:57:15 DCUBS QG13 Business school QG13

08:57:23 Computing-34D57C School of Computing L121 08:58:07 Computing-34D648 School of Computing L125 08:58:11 Computing-34D644 School of Computing L128

Table 4.1: Sample WiFi activity for a volunteer’s trip

opportunity to sample activity patterns for various disparate WiFi Logs. The logs of these volunteers’ WiFi activity was isolated in the log files, accessed and analysed by filtering the dataset by their unique username ids. Having shown the ability to identify students by their id’s, their logs for a number of randomly chosen dates was chosen and the actual activity was compared against the logs. A sample log extract from one of our volunteers is shown in Table4.1which tracked and logged their morning journey through the campus to their lab. The first row in this table shows their first connection to the Eduroam system and the location of that connection. Record 1 identifies this NAS location being room CG12 which is located in the Henry Grattan building, see Figure4.1. As they passed out of range of this NAS, they connect to the NAS in room C105. This is a typical journey for this volunteer and one taken most mornings. They park their car in the car park at the end of the campus and walk to the School of Computing through the access road between the Henry Grattan building and the Helix Theatre. They emerge from the access road and cross over to the Computing building, where they connect to the NAS in room L101. Having passed other NAS in the computing building (L208 & LG25) the student takes the stairs up to the first floor, at this point the nearest NAS that they connect to is in the Business School. As soon as they pass back into the spine of the Computing School building, they connect to various NAS’ on the 1st floor on their way to their Lab.

Figure 4.1: Volunteer daily route to Lab.

a sample month was carried out to determine if their recorded activity matched their actual behaviour. The findings indicated no anomalies or unexpected activities being present in the data. One finding which required further investigation was the presence of a number of entries at the same time in the same place but with different Session ids for the same student id. It was identified that volunteers sometimes access the Eduroam WiFi system using up to three separate devices. Three devices were identified by their distinct unique “calling id” and the volunteers’ “Username”. Examination of the “calling id” and compar- ing it to the MAC addresses of the volunteers’ Laptop, Phone and iPad identified the source of the entries. This would prove to be an important consideration during the programme development stage of the project.

During a more granular examination of a sample month, one volunteer generated 2,282 events and accessed 20 distinctive “NAS Identifiers”. As would have been expected the majority (1,978) of events occurred in the confines of the building they frequent the most, in this case the Computing School building. A smaller number occurred in many of the locations previously mentioned such as the routes taken to and from the car park and those involving other daily routines such as visiting the on-campus shop.

confirmed their normalised routines to be accurately reflected by the WiFi log data. They demonstrated that each individual automatically logged onto Eduroam at the nearest NAS point as they enter the campus boundary. Depending on a student’s circumstances, they may arrive at the campus on foot, by bicycle, via public transport or in private transport such as a car or a motorbike. They could arrive at various entrance points onto the campus and their WiFi device or devices will connect at the campus perimeter.