# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 1 Introduction  
Device localization is a fundamental technology in applications such as environmental monitoring, logistics, navigation, and wireless sensor networks. The ability to determine positions in real time enables a wide range of functionalities, from asset tracking in warehouses to indoor navigation [1]. However, the limitations of the Global Positioning System (GPS), such as signal attenuation in enclosed environments and interference in dense urban areas, highlight the need for complementary solutions [2]. Among these alternatives, the Received Signal Strength Indicator (RSSI) has emerged as a widely used technique in wireless sensor networks due to its simplicity and low cost [3]. This technique is based on the relationship between the signal strength and the distance to the receiver, using propagation models such as free space and log-normal path loss to adjust the measurements. The free-space model offers several advantages, including a simple and straightforward formula, which facilitates quick calculations. It serves as the foundation for other propagationmodels and provides accurate results under ideal conditions, such as satellite links. Additionally, it is independent of external factors like obstacles or weather conditions. However, it also has significant limitations. For instance, it does not accurately represent real-world environments, as it ignores obstacles and reflections. It requires a clear line of sight, and the signal attenuation increases exponentially with distance. On the other hand, the log-normal path loss model has the advantage of accounting for scattering, reflections, and diffraction, making it more realistic. It is applicable in urban, rural, and indoor environments, fits empirical data better, and allows for modeling signal fluctuations through random variability. Nevertheless, it also has drawbacks, such as greater mathematical complexity, the need for experimental data for calibration, and the potential to overestimate path loss in open environments. Although environmental factors such as physical obstacles and interference can affect its accuracy, RSSI remains a viable option, especially when combined with advanced localization algorithms [4; 5]. The system proposed in this work employs anchor nodes integrating ESP32 microcontrollers [6] and NEO-6M [7] GPS modules to obtain precise geographic coordinates and the Oppo Reno \(11\) phone [8] as a wireless sensor. Based on these coordinates and RSSI measurements, the distances between unknown nodes and anchor nodes are calculated using path loss models adapted to the environment [9; 10; 11; 12]. These distances are processed using algorithms such as trilateration, Maximum Likelihood Estimation (MLE), and the Min-Max method, which have been extensively validated in the literature [13]. Trilateration determines the location of an unknown node by finding the intersection of circles defined by the distances to three anchor nodes [14], while the Min-Max method employs a geometric approach based on the intersection of the square areas around the anchor nodes [15; 16]. On the other hand, the Maximum Likelihood algorithm minimizes the mean squared error between the estimated and actual distances, achieving a greater accuracy in complex environments [17]. Recent studies have shown that RSSI-based systems can achieve accuracies of up to two meters under controlled conditions [18]. However, interference, multipath propagation, and signal variability remain significant challenges [19]. Advanced techniques such as Kalman filtering, channel diversity, and machine learning have been explored to improve the robustness and accuracy against these issues [20; 21; 22]. These solutions have diverse applications, ranging from smart factories to optimizing the logistics flows in warehouses. In the context of the Internet of Things (IoT), RSSI-based systems enable the creation of flexible and scalable sensor networks, adaptable to various scenarios and operational requirements [23; 24; 25].