## 3 Experimental Data Acquisition  
For this paper, we used real data collected from National Taipei University of Technology (NTUT), found at 25\({}^{\circ}\)02\({}^{\prime}\)51.94\({}^{\prime}\) N latitude and121\({}^{\circ}\)31\({}^{\prime}\)54.66\({}^{\prime\prime}\) E longitude. The working environment'sinternal structures are illustrated in Figure 1. Data sources are assessed from three buildings: the second teaching building (building-1), the complex building (building-2), and the academic building (building-3) during working times. In the first building, we used four lecture rooms and one lecture hall on the third floor. In building-2, we used two floors (floor 2 and floor 3) having laboratories, meeting halls, and offices. In building-3, lecture rooms and a training room are considered. Each floor has a corridor, gates, and/or exits. The RSS and BSSID values are measured from each AP and stored in the computer, which has the following specifications: ASUS Intel(r) core i7-6700 Duo CPU @ 3.40 GHz with 16.0 GB RAM. We used the graphical processing unit (GPU) card to handle large numbers of datasets for parallel implementation of the classification and regression schemes. For data collection, we divide the working environment into 1 m \(\times\) 1 m grids for manageability of the collected datasets. We also select a 1 m distance between grids to collect more Wi-Fi signals and to make the proposed system adapt easily signal fluctuations through distance. The majority of the experimental works for data collection in indoor environments are done between 1 m \(\times\) 1 m to 2.5 m \(\times\) 2.5 m grids. In [1], authors compared the performance of their proposed method at 1 m \(\times\) 1 m, 1 m \(\times\) 2 m, 1 m \(\times\) 1.5 m, 2 m \(\times\) 2 m, and 2 m \(\times\) 2.5 m grids. They assured that data collected from 1 m \(\times\) 1 m grids helps to achieve better localization accuracy with errors less than 1 m compared to others. Authors of [4] used 1.8 m \(\times\) 1.8 m grids to collect required data. In [5], authors used 2.5 m \(\times\) 2.5 m grids. In [6], 1 m \(\times\) 1 m grids were used for data collection, and accurate results were found. In [23], data was collected in 1.7 m \(\times\) 1.7 m grids experimentally. The datasets are collected through experiment in a real-world environment. From each grid, we collected 35 RSS values through 5-s intervals, periodically. Discussions in [24; 25] showed that  
Figure 1: Structures of the indoor environments. using numerous signal values at each grid helps to make the machine easily adapt to the Wi-Fi signal fluctuations. During RSS collections, we also record BSSIDs, which help to identify the RSS values uniquely. BSSID is the Media Access Control (MAC) address of the AP. A total of 36,660 RSS values and 36,660 BSSIDs are recorded from 1047 grids. In the data collection stage, the APs are not moved or changed from their original locations. We used smartphones for data collection in each grid, because it is easy and more available. Since the working environment consists of multiple buildings, and each building has different services, users in different rooms can use both private and public Wi-Fi network services. Public AP locations are determined by the university. There are also private APs that are controlled by individuals in offices or laboratories. Usually the private APs are placed in the room, while the public APs are placed in corridors for laboratories and offices. In the lecture halls and lecture rooms, we observed that the APs are seated inside, as well as outside, the room. The use of numbers of APs is because of the complexity of the building and numbers of users. For this work, we used all reachable APs for data collection purposes to make the system is inclusive. Additionally, we used our own APs deployed in selected locations as shown in Figure 1. Wi-Fi users in different rooms or classes are not restricted as the nature of Wi-Fi is unlimited, except for assigning the private password to protect Wi-Fi usage from the APs.