GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices

2025-07-15

AI Summary

To address poor generalization and low accuracy in Wi-Fi RSS fingerprinting localization caused by device heterogeneity and non-Euclidean noise, this paper proposes a robust graph neural network (GNN)-based localization framework. Methodologically, it integrates graph attention mechanisms with hyperspace signal modeling to explicitly capture spatial relationships among location nodes and non-Euclidean structural noise. Key contributions include: (1) an attention-guided hyperspace vector encoding scheme and a multi-dimensional hyperspace vector encoding scheme, which jointly model the heterogeneous noise distribution of RSS measurements and inter-device discrepancies; and (2) a real-time dynamic edge construction mechanism that mitigates GNN blind spots and enhances graph structural expressiveness in dense AP environments. Extensive real-world evaluations across multiple scenarios demonstrate that the proposed method reduces mean localization error by 1.6–4.72× and worst-case error by 1.85–4.57× compared to state-of-the-art approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely used indoor localization approach due to its compatibility with mobile embedded devices. Deep Learning (DL) models improve accuracy in localization tasks by learning RSS variations across locations, but they assume fingerprint vectors exist in a Euclidean space, failing to incorporate spatial relationships and the non-uniform distribution of real-world RSS noise. This results in poor generalization across heterogeneous mobile devices, where variations in hardware and signal processing distort RSS readings. Graph Neural Networks (GNNs) can improve upon conventional DL models by encoding indoor locations as nodes and modeling their spatial and signal relationships as edges. However, GNNs struggle with non-Euclidean noise distributions and suffer from the GNN blind spot problem, leading to degraded accuracy in environments with dense access points (APs). To address these challenges, we propose GATE, a novel framework that constructs an adaptive graph representation of fingerprint vectors while preserving an indoor state-space topology, modeling the non-Euclidean structure of RSS noise to mitigate environmental noise and address device heterogeneity. GATE introduces 1) a novel Attention Hyperspace Vector (AHV) for enhanced message passing, 2) a novel Multi-Dimensional Hyperspace Vector (MDHV) to mitigate the GNN blind spot, and 3) an new Real-Time Edge Construction (RTEC) approach for dynamic graph adaptation. Extensive real-world evaluations across multiple indoor spaces with varying path lengths, AP densities, and heterogeneous devices demonstrate that GATE achieves 1.6x to 4.72x lower mean localization errors and 1.85x to 4.57x lower worst-case errors compared to state-of-the-art indoor localization frameworks.

Problem

• Improving indoor localization accuracy with non-Euclidean RSS noise modeling
• Addressing device heterogeneity in Wi-Fi fingerprinting localization
• Mitigating GNN blind spots in dense access point environments

Innovation

• Attention Hyperspace Vector enhances message passing
• Multi-Dimensional Hyperspace Vector mitigates GNN blind spot
• Real-Time Edge Construction enables dynamic graph adaptation