# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 1 Introduction  
The rapid advancement of wireless network technologies has received significant interest in the practical application of location-based services for both indoor and outdoor environments. While GPS-based localization services are extensively used outdoors with high accuracy, their efficacy indoors is compromised, due to obstructions like walls and doors leading to weak signal reception [1]. Hence, alternative methods, such as fingerprinting [2, 3], triangulation [4], trilateration [4], proximity [5], and pedestrian dead reckoning [6] are utilized for indoor environments [7]. Compared to other methods, fingerprinting-based techniques require less hardware, and they are capable of performing localization and navigation using signal strength values from various wireless network technologies, such as FMradio [8], Wi-Fi [9], Bluetooth [10], Radio Frequency Identification Device (RFID) [11], Ultra Wide Band (UWB) [12], and ZigBEE [13]. Among these technologies, Wi-Fi fingerprint-based applications are widely used in public areas, such as museums, shopping malls, and parking lots, so positioning can be done with these services using only a mobile device without the cost of additional hardware. While alternatives, such as UWB and BLE, have higher sensitivity and efficiency, the need for infrastructure makes their widespread use in specific scenarios impractical. As another alternative, although FM radio is widespread and offers wide coverage, its application for fine-grained indoor localization is limited [14]. FM signals are typically designed for long-range communication and are not optimized to capture the small-scale spatial variations required to distinguish closely located indoor spaces. The relatively low frequency of FM signals results in larger wavelengths that are less sensitive to obstacles and environmental changes that are critical for room-level discrimination in indoor environments [15]. Wireless Local Area Networks (WLANs), on the other hand, operate at higher frequencies and provide rich spatial diversity, making them more suitable for precise indoor localization [16]. Wi-Fi is a wireless communication technology employed in WLAN, and it generally operates in the frequency range between 2.4 GHz and 5.8 GHz [17; 18]. Current Wi-Fi technology does not provide direct information about the distance between the access point and the signal receiver, but the Wi-Fi signal strength level indicates the distance and proximity of the signal receiver to the access point. The Basic Service Set Identifier (BSSID), which is a unique identifier, is used to obtain information about the MAC address associated with the Wi-Fi signal strength [19]. In this context, the signal strengths corresponding to each BSSID were taken into account in our study. In Wi-Fi-based fingerprinting methods for indoor localization, an offline radio map is first generated by collecting Received Signal Strength Indicator (RSSI) data from the target environments. During the online phase, the real-time location of the target device is estimated by leveraging previously measured RSSI data. In this study, a sensor fusion approach is proposed by integrating Wi-Fi signals with room-to-room transition time, which represents the time taken to move from one room to another, measured using the mobile device clock as a sensor. This feature is incorporated into Wi-Fi-based fingerprinting systems to enhance room-level localization accuracy by utilizing ML models. This study builds upon our preliminary work [20], which proposed room-to-room transition time as a novel feature for indoor localization. While the previous study introduced the concept, it was limited to a single dataset and a narrower experimental setup. In this work, we expand the evaluation by testing the feature across multiple datasets from different residential environments and systematically analyzing its impact on classification performance, using a broader range of ML models.