# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 5 Conclusions and Future Works  
This study introduces room-to-room transition time as a novel feature for improving smartphone-based indoor localization. The experiments conducted on three distinct datasets, collected from different residential environments with varying user demographics, demonstrate that the inclusion of transition time significantly enhances the performance of ML models. Compared to our preliminary study [20], which first introduced the concept of using room-to-room transition time as a feature for indoor localization, this research presents several significant advancements. The earlier work laid the foundation for incorporating temporal information into indoor positioning but was limited in scope, leaving a more extensive analysis across diverse datasets and ML models for future exploration. Building on this groundwork, we collected new datasets covering different home layouts and users, significantly increasing the diversity of the training data. Additionally, a wider range of ML algorithms was tested, including Ensemble methods and Neural Networks, allowing for a more comprehensive evaluation of the proposed feature. Furthermore, unlike the manual data-collection process in the preliminary study, this work automated Wi-Fi fingerprinting, using an ESP32-based hardware setup, ensuring more consistent and efficient data acquisition. These enhancements, along with the demonstrated performance gains, reinforce the novelty and broader applicability of the proposed feature in indoor localization. The experimental results indicate that incorporating temporal and spatial features, such as transition time, adds significant value to indoor localization systems. The performance improvements observed across multiple datasets and spatial configurations underline the robustness and generalizability of the proposed feature. The proposed feature enables models to better capture the dynamics of human movement within indoor environments, leading to more accurate and reliable predictions, despite variations in home structure. The findings provide a strong foundation for future research and practical applications, particularly in developing scalable, low-cost, and high-accuracy indoor positioning systems for smart environments. Our study highlights the significance of room-to-room transition time in room-level indoor positioning. Instead of determining the (x, y) coordinates of an object or person within an indoor environment, a room-level approach is often more practical and meaningful, especially in spaces like homes, offices, or hospitals, which are structurally divided into rooms and corridors. In many applications, knowing whether an individual or device is inside a specific room is sufficient, rather than pinpointing an exact location within that room. For instance, in a hospital setting, tracking medical devices, doctors, or patients does not require precise (x, y) coordinates; instead, identifying the specific room where they are located is both more useful and operationally efficient. Although fingerprinting and WLAN networks were used in this study, we foresee that the proposed room-to-room transition time information could be easily integrated with localization methods other than fingerprinting (triangulation trilateration, proximity, and pedestrian dead reckoning) and with other wireless technologies, such as FM radio, WMAN, or cellular networks, to improve the performance of existing methods and to propose new methods. One of the most challenging aspects of this study was data collection, particularly the acquisition of a sufficient number of room-to-room transition time samples. Walking repeatedly between rooms to manually record transition times was a time-consuming, tedious, and physically demanding process. Although Wi-Fi signal acquisition was automatedin this study, a major limitation remains: the manual recording of transition times.