Enhancing Indoor Localization with Room-To Transition Time: A Multi-Dataset Study

Abstract

With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems-such as Bluetooth, ZigBee, or FM radio-Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification.