## 2 Related Work  
In traditional IEEE 802.15.4 signal-based indoor positioning systems, RSSI signals are always distorted by multipath effects. [PERSON] et al., in their research, investigated two factors: density of access points (APs) and the influence of using an outdated signature map. They proposed several approaches to generate a radio frequency distribution map [8]. Quadtree search and fractal direction entropy weighting (QSFDEW) is a brand new indoor fingerprint locating algorithm that was developed by [PERSON] et al. [9]. This method incorporates the benefits of QSFDEW algorithms, improving the indoor location's accuracy as well as speed. The QSFDEW algorithm's implementation is an inventive method for enhancing indoor fingerprint locations. A RSSI-based indoor locating system that uses a multilateration (MLT) technique for logistics applications was proposed by [PERSON] et al. [10]. The authors used a sensor network for food transportation logistics and completed both simulations and live experiments. When six fixed nodes (FNs) were used, [PERSON] et al. [10] showed that their system could deliver a localization error of less than 1 m. Additionally, tests using a module compliant with IEEE 802.15.4 revealed location errors of less than 1.8 m.  
Previous studies, however, only take into account the accuracy of location estimation in a space where fixed nodes are placed close together while estimating the location by calculating the distance of RSSI signals in a vast space as a logarithmic function. The fingerprint method also has the drawback of requiring a full scan and learning the entire area's location information. A location system utilizing artificial neural networks (ANNs) was suggested in [11] as a solution to this issue. The research findings in [11] demonstrated that ANNs can greatly enhance localization performance in indoor contexts with multipath effects in a localization system based on Wi-Fi RSSI data. Further advancements are required due to the prediction uncertainty of ANNs, which can lower the trustworthiness of localization outcomes. Findings in [12; 13; 14; 15] offer a deep neural network (DNN)-based localization system as a means of creating a more trustworthy localization system. In complex indoor environments, DNN-based localization systems have the capability to accurately determine and improve the positioning of objects or individuals. The primary challenges of DNN-based localization systems, however, are the training difficulties of DNNs and the need for a substantial amount of training data. A convolutional neural network (CNN) is suggested as an alternative to enhance localization performance in [16]. CNN-based localization techniques deliver precise building and floor predictions using RSSI time series from WLAN access points. A CNN-based localization system can be implemented with few resources, but it is challenging because of the memory required for activation at the network layer. For localization systems based on Wi-Fi RSSI signal, a novel time series semi-supervised learning technique is proposed in [17]. Compared to the current Wi-Fi RSSI signal-based localization method, the semi-supervised learning algorithm produces results for locations that are more accurate. The extreme learning machine (ELM) [18] that is described in this study is used in a Wi-Fi positioning system to lessen the effort required to calibrate Wi-Fi fingerprint maps. The Wi-Fi fingerprint maps are created more quickly and with less labor due to the ELM's quick learning rates, which also enhance the performance of localization. But in terms of precision and accuracy, the localization outcomes of ELM-based systems can fall short of deep learning-based systems [19]. Recurrent neural networks (RNN) and long short-term memory (LSTM) models are used to create localization systems in [20; 21; 22]. Additionally, [23] provides a Wi-Fi indoor localization system based on deep learning models that surpasses the current Wi-Fi RSSI signal-based localization system.