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Smartphone-based indoor localization has emerged as a cost-effective andaccurate solution to localize mobile and IoT devices indoors. However, thechallenges of device heterogeneity and temporal variations have hindered itswidespread adoption and accuracy. Towards jointly addressing these challengescomprehensively, we propose STELLAR, a novel framework implementing acontrastive learning approach that leverages a Siamese multi-headed attentionneural network. STELLAR is the first solution that simultaneously tacklesdevice heterogeneity and temporal variations in indoor localization, withoutthe need for retraining the model (re-calibration-free). Our evaluations acrossdiverse indoor environments show 8-75% improvements in accuracy compared tostate-of-the-art techniques, to effectively address the device heterogeneitychallenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2years of temporal variations, showcasing its robustness and adaptability.

STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization