## 4 Proposed Methods  
As mentioned in [26; 27; 28; 29; 30; 31; 32; 33], RSSI signals tend to exhibit low repeatability as they vary over time. They change with time for a variety of reasons, including the hardware used, the distance between the transmitter and receiver, the measurement period, the interference from external devices, the presence and movement of people, and the surrounding environment (the type of building, any obstructions, and the materials used) [33].  
Conventional MLT methods frequently result in estimated locations that fall outside the defined test area, as demonstrated in previous studies [10; 33]. To overcome this issue, we utilized an ANN method that relies on converted distances and RSSI signals to estimate location. Additionally, we employed a refined location estimation approach based on a progressive subdivided quadrant method, allowing us to improve the accuracy of our localization system. Figure 2 illustrates the proposed system.  
Figure 1: Schematic illustration of hardware configured for experiment.  
Three processes make up the suggested system in Figure 2: (a) zone generation using PSQ, (b) zone selection, (c) and estimation of location using the B-spline surface equation. The following is a detailed description of the steps.  
Traditional supervised deep neural networks often have both offline and online phases for localization. An extensive dataset, made up of measurable data split into training and validation sets, is used to build and validate a model during the offline phase. In this instance, the ANN model applies the data directly without preprocessing by using received signal strength indicator (RSSI) values gathered from nine ground truth locations as inputs and producing the appropriate user location information. The ANN model can precisely pinpoint a mobile node using the training dataset once it has been optimized. To achieve high localization accuracy, it is necessary to gather a sizeable number of dense, labeled data samples, but doing so is expensive and time-consuming. To address this issue, we segment the data into zones (regions) and create high-resolution data labels, and then apply neural network learning to them.  
In this study, we offer a method for generating artificial data that complements genuine labeled acquisition data using a progressive segmentation methodology. To accomplish location prediction, the algorithm blends actual gathered data with artificially increased data. Furthermore, to ensure consistent data quality and coordinate prediction, the system converts the distance measurement to an equivalent FN value.  
For localization, actual data are merged with the generated signal's magnitude and translated distance. FNs and MNs were used in the experiment, which took place in a 3 \(\times\) 3 m indoor area. RSSI signals were recorded for each access point. The online location region prediction is then performed using the learned ANN model. Based on the generated data, a region is chosen, and the B-spline curve equation is applied to estimate its location. The suggested framework employs B-spline approaches and progressive segmentation techniques to efficiently estimate the location of mobile nodes in a 3 \(\times\) 3 m interior environment with little data collection.