Indoor localization using router-to-router RSSI and transfer learning for dynamic environments

Abstract

With the increasing demand for indoor localization, received signal strength indicator (RSSI)-based fingerprint localization has gained widespread attention due to its low equipment costs. Traditional methods only use RSSI data collected from user devices to train localization models, but the coarse granularity of RSSI often limits accuracy. Additionally, changes in the environment, such as door opening and closing or furniture rearrangements, can render these models ineffective. While resource-intensive and time-consuming, data re-collection and model retraining are essential for capturing updated signal characteristics after environment changes, ensuring the model remains accurate and effective. To enhance localization accuracy, we expand on traditional approaches by incorporating RSSI data measured between wireless routers as additional fingerprint features, achieving nearly a 20% accuracy improvement. Furthermore, we address the challenges of dynamic environments by introducing a multi-task domain-adversarial transfer learning method, which extracts consistent features before and after environment changes. Transfer learning allows us to leverage knowledge from the environment before the change, thereby reducing the need for data re-collection after the change. Experiment results from simulated, real-world, and open dataset environments confirm the effectiveness of the proposed method in dynamic indoor localization. Our approach reduced the mean error distance (MED) by 35%, 44%, and 28%, respectively, with only 16%, 20%, and 17% of the data re-collected.