# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 5 Conclusions and Future Works
This manual process not only made data collection labor-intensive but also introduced the risk of deviations from natural walking behaviors, as participants had to consciously initiate and track transitions. Consequently, while the collected datasets successfully capture the intended movement variations, they may not fully reflect the spontaneous and unconstrained movement patterns observed in real-world scenarios. To address these limitations, the acquisition of room-to-room transition times will be automated, using a low-cost and energy-efficient ESP32-based localization system. The proposed system will include multiple stationary localization nodes deployed within each room, ensuring sufficient coverage for trilateration-based positioning. Additionally, a wearable device equipped with an ESP32 module, already developed in its initial version, will continuously track the user's position within the home environment by leveraging Bluetooth and/or Wi-Fi signal strengths from these nodes. By automatically monitoring the user's movement in real-time, this system will enable seamless and natural data collection, eliminating the need for manual input. This approach not only facilitates the collection of a much larger dataset with minimal effort but also ensures that the recorded transition times more accurately reflect real-world movement patterns, thereby enhancing the general applicability of the proposed feature in indoor localization. Further research will focus on expanding the dataset and the experimental setup to cover more diverse environments, including multi-floor buildings and dynamic public spaces, as well as incorporating data from a larger number of users with varying movement patterns. Additionally, the planned integration of a wearable device with an ESP32-based localization system will not only facilitate seamless and natural data collection but will also significantly increase the volume and realism of the recorded transition times, eliminating the need for manual input. These advancements will allow for a more comprehensive evaluation of deep learning approaches, which are particularly effective for handling large-scale and complex datasets. Moreover, hybrid models that combine classical ML and deep learning techniques could further improve robustness and adaptability in real-world applications. By addressing these challenges, future studies can bridge the gap between controlled experimental settings and practical deployment, ensuring that the proposed system remains efficient, scalable, and suitable for real-world indoor positioning applications. I.K.A. : conceptualization, methodology, data extraction, data curation, validation, writing--original draft. L.B. : conceptualization, methodology, data extraction, data curation, validation, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.