## 1 Introduction  
Research on location-based services (LBS) for use in the home, industrial automation, and healthcare has seen increased interest because of the growth of the Internet of Things (IoT) [1].  
Location estimation technology is increasingly important in the IoT service domain, especially in services like unmanned vehicles, intelligent transportation systems, and drones. Numerous services have been made available to enhance the convenience of individuals, and currently, location-based services are functioning effectively. Services known as LBSs give wireless network users pertinent information about a user's location. A crucial element of LBSs that has been considered in the academic and industrial sectors is an accurate and affordable localization mechanism. The GPS technique has typically been used to resolve this problem in outdoor settings, but due to the obstruction, attenuation, or reflection of satellite signals in enclosed spaces, this method is not appropriate [2]. Finding a reliable and affordable indoor localization system is therefore a continuing issue in this field. LBSs are used in a variety of settings to accurately determine outdoor positions using GPS technology. These systems, however, encounter limitations when it comes to detecting signals indoors, making it challenging to estimate interior location. Various LBSs, including wireless personal area networks (WPAN) [3; 4; 5], wireless local area networks (WLAN) [4],and radio-frequency identification (RFID) [5], use words like dependability and distance to estimate interior locations. Wi-Fi is typically used in indoor location methods [6]. However, recent advancements in lighting, compactness, cheap cost, energy, and compatibility have spurred ongoing research on indoor WPAN sites, such as BLE and Zigbee [7]. The use of the received signal strength indicator (RSSI), which monitors the power level of the received signal, is a popular method in LBSs for completing tasks such as calculating robot placement in warehouses and tracking construction project personnel. Due to its low power consumption and sophisticated signal processing capabilities, RSSI-based location estimation technology has received a great deal of attention and has been thoroughly studied in the context of wireless sensor networks [2]. Additionally, the majority of wireless devices now come equipped with built-in RSSI circuits, eliminating the need for additional hardware and lowering the cost and power usage of the system.  
The primary objective of this paper is to improve the precision of positioning in confined areas, specifically outperforming the transferrable sensor capabilities provided by IEEE 802.15.4 standard sensors in open spaces. Additionally, we investigate efficient methods for interpretating fluctuations in radio wave intensity emitted from stationary nodes, which exhibit a logarithmic decrease and are not directly correlated with distance. We use a multi-layer perceptron (MLP)-based technique using the B-spline curved equation to determine the final coordinates and to achieve more refined position tracking.  
We propose a novel RSSI signal-based positioning system that utilizes RSSI subregionalization, neural networks, and B-spline methods to address the accuracy issues of the raw signals used in these studies.