# VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization

arXiv: https://arxiv.org/abs/2302.09443 · HTML: https://mlsanity.com/paper/2302.09443/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [On the Relationship between Self-Attention and Convolutional Layers](https://mlsanity.com/paper/1911.03584/) (2019)
- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning](https://mlsanity.com/paper/2107.01192/) (2021)
- [QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2104.07521/) (2021)

## Built on by (most cited first, 7 of 7)

- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization](https://mlsanity.com/paper/2312.10312/) (2023)
- [CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization](https://mlsanity.com/paper/2311.06361/) (2023)
- [SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks](https://mlsanity.com/paper/2407.11091/) (2024)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction](https://mlsanity.com/paper/2403.07621/) (2024)
- [ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization](https://mlsanity.com/paper/2603.19594/) (2026)

## Abstract (arXiv)

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.