## 6 Discussion  
During the literature review in the field of indoor LoRa mobile node localization research, we found that the problem of mobile node localization, especially in dynamic environments, remains an under-explored area, despite the availability of many solutions for static nodes. In [38], a deep autoencoder method was proposed to handle missing data and outliers using artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN) to predict the location based on RSSI fingerprints. An average error of 1.27 m was achieved in static environments with data splits of 70% and 30% for training and testing, respectively. In [35], an improved method based on random forest (RF) and particle swarm algorithm (PSO) was proposed, which uses a new RSSI RANGE parameter, achieving the best accuracy of 0.82 m by filtering the data. In [37], the authors presented a random neural network (RNN)-based indoor localization system based on RSSI measurement, which achieved an average error of 0.12 m. Although this model  
Figure 18: Cumulative distribution of average localization errors.  
demonstrated excellent results in their study, in our case with dynamic data collection for mobile nodes, its performance was lower, with an average localization error of 0.441 m for the best RNN model. A back-propagation neural network [BPNN]-based model with an outlier removal filter during data collection [36] achieved an average error of 0.5971 m indoors. While this approach showed promising results in their study, in our scenario, with dynamic data collection, BPNN performed worse, achieving an average localization error of 0.467 m. However, the environmental dynamics of moving objects is still not considered and is included by [41] for future implementation of mobile node position prediction. In traditional trilateration methods [32], experimental results show that LoRa localization can achieve an accuracy better than 1.6 m under line-of-sight conditions. In [62], four wireless technologies for indoor localization were compared: Wi-Fi, Bluetooth, Zigbee, and LoRa, and LoRa achieved an accuracy of 0.846 m under very noisy environments and 1.534 m under low noise conditions. In all the above works, the localization problem is solved for static nodes. Radio maps were obtained by repeatedly measuring RSSI at junctions of a predefined grid in a room.  
The results obtained in our work are comparable with the results obtained in [63; 64] using Wi-Fi and Zigbee, since these works considered mobile wireless nodes. In [63], two important methods are used to ensure accurate indoor localization based on collecting Wi-Fi RSSI fingerprints: the Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) machine learning algorithms. As a result, the average error in the algorithm with LSTM is 0.9 m, with SVM it is 1.1 m. In [64], an adaptive wireless indoor localization system (ILS) for a dynamic environment was proposed, which includes an automated database update process and a new Adaptive signal model fingerprinting (ASMF) algorithm. Table 3 shows the numerical results of the above works.  
In our study, the best GRU model achieved an average localization error of 0.384 m and the average absolute localization error of 0.246 m were achieved, which is a good result under multiple reflection conditions in the room.  
The proposed method of dividing into sectors showed better results in comparison with other works. It allows speeding up of the process of obtaining a radio map and increasing the accuracy of localization of mobile nodes using LoRa technology.