## 2 Related Work  
Recent studies have demonstrated the effectiveness of integrating machine learning techniques with LoRa technologies to address indoor localization tasks. In the study [34], various ML methods, such as support vector regression (SVR), spline models, decision trees, and ensemble learning, were applied to solve the RSSI-based localization problem in LoRa networks. However, these models primarily address static localization settings and do not dynamically adapt to real-time environmental changes, reducing their effectiveness for applications involving mobile devices. In [35], a localization algorithm for indoor environments based on LoRa fingerprinting and particle swarm optimization is proposed. The suggested fingerprinting algorithm utilizes multiple wireless signal sources to establish a correlation between RSSI values and coordinates. The initial position is determined using trilateration to enhance accuracy and is further refined with a Bayesian algorithm. Whileeffective for controlled, predefined setups, the method's reliance on extensive preprocessing can limit adaptability, as expanding the setup would require significant computational resources and processing time to maintain accuracy. The study [36] introduces a filter to eliminate outliers, leveraging log-normal shadowing and a back-propagation neural network (BPNN) to predict unknown locations. Although this approach improves localization accuracy, it relies on the quality of pre-collected data and outlier filtering, and the use of the Log-normal Shadowing Model reduces its effectiveness in complex multi-path environments, limiting its robustness in dynamic conditions. In [37], the authors developed a low-power intelligent indoor localization system based on RSSI data from LoRaWAN networks, employing random neural networks (RNNs). [PERSON] et al. proposed a fingerprint-based localization system for indoor and outdoor environments that incorporates interpolation techniques and a denoising autoencoder to eliminate missing and noisy data in LoRa networks [38]. Using deep learning models, such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), the system demonstrates superior localization accuracy compared to traditional machine learning methods. In [39], [PERSON] et al. applied interpolation methods using a random forest algorithm to reduce the time and effort required for fingerprint data collection. They compared the classic pattern matching algorithm with the minimum Euclidean distance algorithm, finding that the random forest algorithm achieved superior results in minimizing maximum estimation errors, while the minimum Euclidean distance algorithm provided higher accuracy. However, these studies are less effective in dynamically changing environments during mobile node movement. The model in [37] relies on predefined anchor placements and requires substantial effort to adapt to shifting configurations, the fingerprint-based system in [38] depends on interpolated data, which necessitates frequent updates to maintain accuracy, and the approach in [39], although effective in reducing data collection time using random forest interpolation, also requires adjustments in dynamic scenarios. The study [40] introduced a high-precision indoor localization system based on LoRaWAN, measuring both RSSI and SNR from multiple gateways. This method involves transmitting a signal to the gateway, which collects signal data and forwards it to a centralized system for location estimation using ML methods based on neural networks (NNs) to detect or estimate position changes through signal strength variation. While precise, the reliance on SNR as a metric may be limited in direct line-of-sight (LOS) conditions, where SNR often remains stable over short distances and may not effectively capture dynamic changes in mobile node positions. In [41], a deep learning-based indoor localization model was introduced, utilizing LoRa and RSSI fingerprinting.