  Wi-Fi fingerprinting-based indoor localization is an emerging embedded
application domain that leverages existing Wi-Fi access points (APs) in
buildings to localize users with smartphones. Unfortunately, the heterogeneity
of wireless transceivers across diverse smartphones carried by users has been
shown to reduce the accuracy and reliability of localization algorithms. In
this paper, we propose a novel framework based on vision transformer neural
networks called VITAL that addresses this important challenge. Experiments
indicate that VITAL can reduce the uncertainty created by smartphone
heterogeneity while improving localization accuracy from 41% to 68% over the
best-known prior works. We also demonstrate the generalizability of our
approach and propose a data augmentation technique that can be integrated into
most deep learning-based localization frameworks to improve accuracy.
