# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 1 Introduction  
Recent advances in wireless communication technology have increased the demand for LBS (location-based services) that provide location-specific information. The positioning technology of LBS is applied to various fields such as navigation, inventory management, access security, crowd management, and disaster rescue [1; 2; 3; 4; 5]. Location positioning can be categorized into outdoor and indoor positioning based on an individual's location. Outdoor locations can be accurately determined mainly using GPS (global positioning system) [6]. However, GPS-based positioning encounters disruptions in underground or enclosed spaces and exhibits an error of approximately tens of meters, making it unsuitable for indoor positioning [7; 8]. To achieve precise indoor positioning, it is necessary to utilize wireless signals with high accuracy [9]. There are various positioning techniques for indoor environments, such as geomagnetism [10], UWB (ultra--wideband) [11; 12], BLE (bluetooth low energy), and Wi-Fi signals. The technique of indoor positioning using geomagnetism involves constructing a map of the magnetic field distribution to determine location. The advantage is that no additional equipment is required, but the disadvantage is that it requires preliminary work to create the map. Moreover, its application becomes challenging in environments with local magnetic disturbances, such as those encountered in subway systems, leading to variations in magnetic field distribution. UWB wireless communication technology demonstrates high accuracy in indoor positioning systems because it operates in ultra-wideband frequencies with high penetration characteristics. However, considerations such as the elevated installation cost and potential electromagnetic interference risks due to a wide bandwidth shouldbe addressed. BLE or Wi-Fi-based indoor positioning techniques exhibit low precision and accuracy because of vulnerability to interference such as reflection, refraction, diffraction, and scattering. Nevertheless, its economic advantages, low computational complexity, and high availability make advantages for efficient indoor positioning. Indoor positioning techniques using BLE or Wi-Fi include fingerprinting techniques [13] and trilateration technique [14; 15]. Indoor positioning through fingerprinting involves collecting signal patterns at regular intervals within the desired region, constructing a signal distribution map, and estimating the location based on the similarity between the collected and measured signal patterns [13]. With advancement of artificial intelligence, AI-based approaches have been proposed for fingerprinting-based indoor positioning [16; 17]. Learning-based indoor positioning techniques such as machine learning and deep learning utilize signal data collected from sensors at various reference points to train an optimal model connecting measured values and estimated final positions [18]. AI-based fingerprinting techniques excel at identifying complex, non-linear patterns, resulting in improved positioning accuracy compared to traditional fingerprinting approaches [17]. However, the burden of pre-work and the increase in computational complexity proportional to the region to be placed cause disadvantages. Additionally, AI-based fingerprinting depends heavily on pre-collected signal patterns, making it difficult to adapt to changing environmental conditions, requiring extensive signal re-collection and map updates [11; 19; 20]. Trilateration estimates the receiver location by calculating the distances from the desired location to three Aps (access points) [15]. The distance between the AP and the receiver can be estimated by a propagation model based on the received RSSI (received signal strength indicator). The RSSI propagation model for indoor positioning mainly adopts an LDPLM (log-distance path loss model) [21; 22; 23; 24]. Trilateration-based positioning has low computational requirements and minimal pre-work burden.