## 1 Introduction  
The rapid expansion of IoT (Internet of Things) technologies has transformed many sectors by enabling a large number of networking devices to connect and exchange data autonomously, leading to the proliferation of location-based services that are highly relevant to indoor environments [1]. Global Positioning Systems remain the predominant solution for outdoor localization, but they are not well suited to indoor settings due to signal attenuation caused by building materials and complex layouts [2]. As a result, various alternative indoor positioning techniques have emerged, including WiFi, ZigBee, Bluetooth, ultra-wideband (UWB), and cellular networks such as LTE and 5G [3, 4]. However, each of these technologies has its limitations when applied to the challenges of small-scale indoor positioning for IoT applications. While UWB offers high positioning accuracy, it is often impractical for deployment due to its susceptibility to interference and high signal processing requirements [5]. Bluetooth Low Energy (BLE) has emerged as another widely adopted solution for indoor localization due to its availability and low power consumption. BLE beacons are relatively inexpensive to deploy and provide reasonable accuracy for most applications [6]. Recent research has improved BLE-based localization by using advanced filtering and machine learning algorithms, resulting in reduced error rates and enhanced robustness in dynamic environ ments [7]. Nevertheless, BLE suffers from limitations, such as signal instability caused by multipath propagation and interference, which can affect the localization performance [8]. On the other hand, the wide availability of WiFi infrastructure, coupled with low deployment costs, means that WiFi-based solutions have emerged as promising solutions for indoor positioning. Position estimation techniques using wireless signals typically rely on propagation characteristics such as Time of Arrival (TOA) and Angle of Arrival (AOA), and signal fingerprinting methods. TOA and AOA methods determine position based on the travel time or angle of the wireless signal, respectively, but they have inherent limitations. TOA-based systems can be adversely affected by multipath propagation, which occurs when signals reflect off walls and other obstacles, leading to inaccuracies in time-based distance measurements [9]. Additionally, TOA techniques require precise time synchronization between transmitters and receivers, which may be challenging to achieve in real-world scenarios. Similarly, AOA techniques depend on determining the angle at which signals arrive at the receiver. AOA systems require multiple antennas to calculate the angle accurately, and the complexity of implementation increases with the number of antennas used [10]. AOA is also susceptible to significant errors in multipath environments, where reflected signals can cause incorrect angle estimations. WiFi fingerprinting uses the unique patterns of WiFi signal strengths from multiple access points (APs) to identify a location. In this technique, the location's characteristic is associated with the received signal strength (RSS) vector emitted from different WiFi APs. WiFi fingerprinting generally consists of two phases: \"fingerprinting\" and localization phase (similarly, offline and online phases). During the \"fingerprinting\" phase, RSS data are collected at known locations to create a database of fingerprints. In the localization phase, the real-time signal strengths are matched against this database to estimate the user's location. Fingerprinting approaches use pre-collected signal strength measurements from reference points, which are less influenced by signal propagation anomalies. Moreover, the wide availability of WiFi infrastructure, coupled with low deployment costs, makes WiFi-based fingerprint localization a preferred option for many IoT use cases [11; 12; 13].