VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization
This addresses the challenge of reliable indoor localization for smartphone users in embedded applications, representing a strong specific gain.
The paper tackles the problem of smartphone heterogeneity reducing Wi-Fi fingerprinting-based indoor localization accuracy by proposing VITAL, a vision transformer neural network framework, which improves localization accuracy from 41% to 68% over prior works.
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.