# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 3 Results  
A detailed evaluation of the data obtained during measurements of the Received Signal Strength Intensity (RSSI) under varying distance and time conditions is presented. This analysis is divided into several sections, beginning with the correlation between the signal strength and distance. As expected, the results indicate that as the distance increases, the signal strength decreases. Next, the variability in the measurements is analyzed, taking into account the fluctuations observed across different days and times. This analysis helps identify potential factors affecting the accuracy of the distance estimations. Subsequently, the application of a mathematical path loss model based on linear regression is presented, describing how the signal attenuates with increasing distance. Finally, the results of the distance estimations and localization tests are examined, comparing the obtained positions with the actual locations to evaluate the effectiveness of the localization methods employed, such as trilateration. The RSSI parameters were measured using the Physics Toolbox app on an Oppo Reno 11 device with the IEEE 802.11 ax standard in the 2.4 GHz band at distances of 2, 4, 5, 6, 7 and 11 m. The receiver (the Oppo Reno 11 device) remained stationary during the measurements at these distances. The device utilized integrated sensors, such as the Wi-Fi radio frequency receiver, to capture the received signal strength. Measurements were taken at various times over a 31-day period, ensuring the collection of a comprehensive and representative dataset. A total of 186 data points were recorded for each distance (2 m,4 m, 5 m, 6 m, 7 m, and 11 m). This systematic approach allowed for a robust and consistent analysis of the data across both temporal and spatial dimensions. The test scenario consisted of a room measuring 5 m in length, 4 m in width, and 3 m in height. The walls were made of blocks and coated with plaster, and the room featured a window and a wooden door, elements that may slightly affect the propagation of wireless signals. Inside the room, several pieces of furniture, such as a fabric sofa, a wooden table, wooden chairs, and a wooden shelf, were present, which could influence the signal attenuation. No individuals were present except for the person responsible for taking the measurements, and no other significant sources of interference were detected. The environmental conditions included a constant temperature of 22 \({}^{\circ}\)C, mixed lighting (natural and artificial), and a moderate level of ambient noise. Figure 1 illustrates the measurement process, while Figure 2 shows the evolution of the RSSI over time and across different distances. The average RSSI values and their standard deviations decrease progressively with distance, showing greater variability at longer ranges, as shown in Table 1. These results demonstrate a consistent and clear trend, confirming that the RSSI values decrease as the distance between the transmitter and the receiver increases. This observation aligns with the expected inverse relationship between the signal strength and distance. Figure 1: Diagram of the RSSI measurement process using the Physics Toolbox app on the Oppo Reno 11 device. Figure 2: Average RSSI as a function of distance. The coordinates of the anchor nodes are determined using a circuit composed of an ESP32 microcontroller and a uBlox Neo-6M GPS module, with a precision of \(\pm\)2 m. This integration allows the ESP32 to receive location data from the Neo-6M module, providing precise geographical coordinates. Table 2 shows the connections between the ESP32 and the Neo-6M GPS module:  
These analyses provide a comprehensive overview of the accuracy and reliability of the methods used in this study, highlighting the factors that may influence the quality of the measurements and distance estimations. RSSI was measured over 31 days at various