## 3 Research Methodology
### Signal Acquisition  
In order to address the subject of an accurate indoor positioning system, raw data collection, and preprocessing are significant steps for location fingerprinting. This study investigates a collection of data that were measured as RSSI values based on an IEEE 802.11b/g wireless card. RSSI data were acquired at the main basement level of the Faculty of Engineering and Built Environment at the Universit Kebangsan Malaysia (UKM) [33]. In January 2014, data were collected on a single day without interruption. While the research area was regular in shape and did not present any obstacles, the boundaries were quite heterogeneous, with research laboratories, furnished offices, lecture halls, structural pillars, and people occasionally crossing the place. The research area and the schedule of the data acquisition were purposely chosen in order to reproduce the normal conditions of a typical working day. This way, disturbances in the signals were taken into account as they were received and measured.  
Figure 1 shows a sketch of the research area and its irregular boundaries. A regular grid including 96 anchor points encompasses the unobstructed part, in which WLAN and WSN signals were recorded regardless of people and materials transiting through the experimental area. For experimental RSS data collection, we selected a regular grid of 96 anchor points for the creation of the fingerprinting database, in an array of 6 \(\times\) 16 points, as a consequence of the chosen horizontal and vertical separation between anchor points, respectively, 1 m and 1.5 m in the directions parallel to the main walls. The height of each anchor point was strictly kept at the value of 1 m in order to avoid variations in the locally measured signal intensities and to remove the height as a possible sensitive variable [34].  
A Lenovo G580 laptop with a core i5 processor and Windows 7 operating system was used as a measurement setup. For the collection of RSS data samples, an Atheros AR9285 onboard wireless adaptor based on 802.11n was added. An \"inSSIDer\" open WiFi Scanner software (version 4.2) was used, to which some adaptations were executed in order to create the log files of the measured RSS signals with respect to the visible MAC address and the time stamp.  
The signal intensities in dB from three available WiFi and four installed WSN signal sources were recorded simultaneously 300 times for one minute, i.e., every 0.2 s, in each of the 96 anchor points. Only three signals from the WSN were considered for the database, as one of the access points was faulty and provided random, unreliable values. The average of each set of 300 values was used to build the signal intensity database. The stability of all six available signals was checked through the standard deviation of each set, which was satisfactorily within 5% of the average value for all anchor points. The acquired data were organized and saved in a matrix of 96 \(\times\) 8 elements. The WiFi data were stored in columns 1-3; the WSN data were placed in columns 4-6; and columns 7 and 8 contained the spatial coordinates of the 96 anchor points. In order to utilize the appropriate signal weighting, which provided a significant reduction in the positioning error in our previous work [32], it was strictly necessary to identify and keep the same signal sequence in all matrix columns. At the same time, the meaning of this research is fundamentally dependent on the constant positions of all access points and of any other signal source.