# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests
### Experimental Setup  
Following the deployment cycle described in Figure 2, the indoor scenario was analyzed and the radio map generated. This process was managed according to the flowchart of Figure 4 to perform the site survey process. Remember that this process is extremely relevant, since depending on how the radio map is built, it will represent in a more reliable way the irregularities and features of the indoor electromagnetic channel [17; 18; 26]. During the calibration phase, a person walks around the scenario, with one transmitter tag fixed to his or her chest and the Android device with the site survey tool to indicate the location and control the fingerprint generation remotely, as indicated in the previous section. The Android device used to run the site survey tool was a Sony Ericsson Xperia Neo V (Android Version 4.0.4). This device was linked to the server through a WiFi network (802.11b). It should be noted that, in order to obtain a more diverse radio map, three additional people with different heights and complexions repeated the same process. The configuration parameters used to generate the fingerprints were:  
* Tag transmission power \(P_{TX}\) = 0 dBm,
* Number of beacons per tag transmission \(N\) = 20,
* Refresh transmission time \(T\) = 500 ms.  
With these settings, one fingerprint was generated approximately per second, with 20 RSS measures captured per SR. These measurements were associated with the room ID where the tag (the  
Figure 11: Deployed tripod examples. (**a**) Tripod with support to hold three DAs and one OA; (**b**) zoom on the support and antennas; (**c**) zoom on the box with encapsulated SRs inside; RP-SMA cables connect the SR to the antennas; the USB output cable connects the SRs to the server through a USB hub (which is embedded inside the box, with the SRs). user) was located at that time. A total radio map with 1095 fingerprints was created for the current indoor environment approximately in 25 min, taking different random paths/pathways inside each one of the five target rooms/areas defined in the testing scenario. Furthermore, note that missed RSS data (generated by out of range SRs) were replaced by a fixed value of \(-\)90 dBm (the sensitivity of the SR receiver) for mathematical purposes, in order to keep numerical values for the ANN in the subsequent calibration phase. In Table 1 is specified the total number of samples generated per room and the sample density per square meter. From these results, note that an average density of 2.5 samples/m\({}^{2}\) was kept, \(R_{2}\) being the room with the highest density. The site survey was repeated at different time frames throughout the day (the morning, the noon and afternoon), in order to sample the indoor channel properly. This is a recommended practice, since as mentioned before, RSS measurements are quite affected by temperature and humidity variations across the day or the dynamic changes in the environment [19]. It is also worthy to note that the site survey tool was demonstrated to be very useful for the offline stage, optimizing the radio map generation, especially in terms of manpower and consuming time and without needing additional techniques to minimize radio map generation efforts, as proposed in other works [18]; a fast generation of 50 fingerprints per minute can be reached. Furthermore, for future investigations, other additional data of interest were recorded during the site survey, such as the (\(X\),\(Y\)) coordinates (also provided by the user through the Android device) or the tag orientation (readily provided by the digital compass of the Android device). Furthermore, in order to speed up the process, a future version is currently being programmed, which will allow one to generate in parallel fingerprints by a collaborative method based on employing several tags at the same time to generate the radio map. This will enormously reduce the time consumption dedicated to the site survey. After the radio map generation, the calibration phase was managed to train the ANN.