  Smartphone-based indoor localization has emerged as a cost-effective and
accurate solution to localize mobile and IoT devices indoors. However, the
challenges of device heterogeneity and temporal variations have hindered its
widespread adoption and accuracy. Towards jointly addressing these challenges
comprehensively, we propose STELLAR, a novel framework implementing a
contrastive learning approach that leverages a Siamese multi-headed attention
neural network. STELLAR is the first solution that simultaneously tackles
device heterogeneity and temporal variations in indoor localization, without
the need for retraining the model (re-calibration-free). Our evaluations across
diverse indoor environments show 8-75% improvements in accuracy compared to
state-of-the-art techniques, to effectively address the device heterogeneity
challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2
years of temporal variations, showcasing its robustness and adaptability.
