# MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion

arXiv: https://arxiv.org/abs/2411.17781 · HTML: https://mlsanity.com/paper/2411.17781/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Graph Neural Network for Large-Scale Network Localization](https://mlsanity.com/paper/2010.11653/) (2020)
- [Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments](https://mlsanity.com/paper/2105.08837/) (2021)
- [Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks](https://mlsanity.com/paper/2112.12478/) (2021)
- [Traffic Prediction and Fast Uplink for Hidden Markov IoT Models](https://mlsanity.com/paper/2209.05939/) (2022)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Federated Learning based Hierarchical 3D Indoor Localization](https://mlsanity.com/paper/2303.00450/) (2023)
- [Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization](https://mlsanity.com/paper/2403.00665/) (2024)
- [A Novel GCN based Indoor Localization System with Multiple Access Points](https://mlsanity.com/paper/2104.10342/) (2021)
- [Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization](https://mlsanity.com/paper/2204.05184/) (2022)
- [A Meta-learning based Generalizable Indoor Localization Model using Channel State Information](https://mlsanity.com/paper/2305.13453/) (2023)
- [Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization](https://mlsanity.com/paper/2201.12656/) (2022)
- [Multi-Modal Recurrent Fusion for Indoor Localization](https://mlsanity.com/paper/2203.00510/) (2022)
- [Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning](https://mlsanity.com/paper/2205.11428/) (2022)
- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach](https://mlsanity.com/paper/2108.02257/) (2021)
- [A Graph-based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation](https://mlsanity.com/paper/2207.08646/) (2022)

## Built on by (most cited first, 3 of 3)

- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)
- [Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning](https://mlsanity.com/paper/2502.01268/) (2025)
- [Graph Neural Networks for Jamming Source Localization](https://mlsanity.com/paper/2506.03196/) (2025)

## Abstract (arXiv)

Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received signal strength indicator measurements with inertial measurement unit data to enhance localization accuracy. Our proposed GNN architecture, featuring dynamic edge construction (DEC), captures the spatial relationships between access points and underlying data patterns. MetaGraphLoc employs a meta-learning framework to adapt the GNN model to new environments with minimal data collection, significantly reducing calibration efforts. Extensive evaluations demonstrate the effectiveness of MetaGraphLoc. Data fusion reduces localization error by 15.92%, underscoring its importance. The GNN with DEC outperforms traditional deep neural networks by up to 30.89%, considering accuracy. Furthermore, the meta-learning approach enables efficient adaptation to new environments, minimizing data collection requirements. These advancements position MetaGraphLoc as a promising solution for indoor localization, paving the way for improved navigation and location-based services in the ever-evolving Internet of Things networks.