# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 2 Related Work  
In recent years, indoor positioning applications, which have attracted attention both in academia and industry, have offered solutions to many problems, such as target tracking, inventory management, room-level location, and machine fault detection [21]. Since GPS sensors cannot be used in indoor positioning, due to signal fading, various sensors are used to ensure accuracy and reliability [22]. Although there are many positioning systems associated with modern technology today, these systems aim to minimize positioning error, since systems with low cost and without the use of any special devices are preferred. Indoor positioning methods can be divided into two groups: \"special sensor systems\" and \"ambient sensor systems\" [23]. Although the development of special sensor systems and placement of these systems in the environment or on the human body have advantages, such as minimizing the position estimation error, they also have disadvantages, such as installation and hardware costs, user adaptation to the new system, and the need for installation whenever the user needs it. Ambient sensor systems, on the other hand, are more suitable for individual and general-purpose use as they do not require installation costs and additional hardware, and they are also available on devices (smartphones, tablets, etc.) that the user can carry at any time [24]. For this reason, in order to achieve indoor positioning accuracy at minimum cost, there are various studies in the literature that generally use the existing sensors of the smartphones [25; 26; 27; 28; 29; 30; 31; 32; 33]. Among these studies, WLAN, Wi-Fi, BLE, and FM radio are used as wireless signals for fingerprint-based indoor positioning. The RSSIs of these signals are called fingerprints and are given as features to artificial intelligence models. In addition to RSSI, other features, such as Channel Impulse Response (CIR) and Channel State Information (CSI), can be used. Flexible and easy to deploy, fingerprint-based systems are considered as potential solutions for cost-effective indoor localization. In addition to the Wi-Fi and Bluetooth sensors of the smartphone, various sensors, such as accelerometer, magnetometer, barometer, light, and gyroscope, are used to extract features [34; 33]. Although various signal strengths received from the different sensors are used as a feature, to the best of our knowledge the use of transition time information between rooms has not been encountered, except in our previous study [20]. In a study similar to the calculation of room-room transition time, [PERSON] et al. [35] analyzed the spatial geometric distribution, time sequence, and statistical features within different sliding windows from Global Navigation Satellite System (GNSS) measurements on smartphones, to estimate the Indoor/Outdoor (IO) times between indoor and outdoor transitions with high accuracy. After this feature-extraction process, they proposed a new high-accuracy IO detection model by calculating the detection result with the Hidden Markov Model (HMM), using an Ensemble model based on stacking and filtering. In their experimental results, up to 94.53% accuracy of IO transition detection was achieved.  
Table 1 summarizes the sensors, features, and classification methods used in fingerprint-based indoor positioning studies in the literature. In our previous study [20], Wi-Fi signals were collected manually, using a mobile application. A key distinction in this study was the automation of the data-collection process through a hardware setup, eliminating the need for manual collection. Additionally, the dataset was expanded, and better performance was achieved by incorporating Ensemble and Regression-based methods alongside traditional ML techniques.