# Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks

arXiv: https://arxiv.org/abs/2601.22322 · HTML: https://mlsanity.com/paper/2601.22322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI](https://mlsanity.com/paper/2312.07609/) (2023)
- [UBiGTLoc: A Unified BiLSTM-Graph Transformer Localization Framework for IoT Sensor Networks](https://mlsanity.com/paper/2601.10743/) (2026)
- [A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments](https://mlsanity.com/paper/2405.09960/) (2024)
- [Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees](https://mlsanity.com/paper/2505.01810/) (2025)

## Built on by (most cited first, 1 of 1)

- [GeoFlow: Real-Time Fine-Grained Cross-View Geolocalization via Iterative Flow Prediction](https://mlsanity.com/paper/2603.21943/) (2026)

## Abstract (arXiv)

Indoor localization is a critical enabler for a wide range of location-based services in smart environments, including navigation, asset tracking, and safety-critical applications. Recent graph-based models leverage spatial relationships between Wire-less Fidelity (Wi-Fi) Access Points (APs) and devices, offering finer localization granularity, but fall short in quantifying prediction uncertainty, a key requirement for real-world deployment. In this paper, we propose Spatially-Adaptive Conformal Graph Transformer (SAC-GT), a framework for accurate and reliable indoor localization. SAC-GT integrates a Graph Transformer (GT) model that captures network's spatial topology and signal strength dynamics, with a novel Spatially-Adaptive Conformal Prediction (SACP) method that provides region-specific uncertainty estimates. This allows SAC-GT to produce not only precise two-dimensional (2D) location predictions but also statistically valid confidence regions tailored to varying environmental conditions. Extensive evaluations on a large-scale real-world dataset demonstrate that the proposed SAC-GT solution achieves state-of-the-art localization accuracy while delivering robust and spatially adaptive reliability guarantees.