# Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities

arXiv: https://arxiv.org/abs/2408.01239 · HTML: https://mlsanity.com/paper/2408.01239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 2 Aug 2024.

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

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Toward Location-aware In-body Terahertz Nanonetworks with Energy Harvesting](https://mlsanity.com/paper/2101.01952/) (2021)
- [Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization](https://mlsanity.com/paper/2303.07804/) (2023)
- [In-body Bionanosensor Localization for Anomaly Detection via Inertial Positioning and THz Backscattering Communication](https://mlsanity.com/paper/2108.13747/) (2021)
- [Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data](https://mlsanity.com/paper/2307.05551/) (2023)
- [Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization](https://mlsanity.com/paper/2309.16034/) (2023)

## Abstract (arXiv)

Flow-guided localization using in-body nanodevices in the bloodstream is expected to be beneficial for early disease detection, continuous monitoring of biological conditions, and targeted treatment. The nanodevices face size and power constraints that produce erroneous raw data for localization purposes. On-body anchors receive this data, and use it to derive the locations of diagnostic events of interest. Different Machine Learning (ML) approaches have been recently proposed for this task, yet they are currently restricted to a reference bloodstream of a resting patient. As such, they are unable to deal with the physical diversity of patients' bloodstreams and cannot provide continuous monitoring due to changes in individual patient's activities. Toward addressing these issues for the current State-of-the-Art (SotA) flow-guided localization approach based on Graph Neural Networks (GNNs), we propose a pipeline for GNN adaptation based on individual physiological indicators including height, weight, and heart rate. Our results indicate that the proposed adaptions are beneficial in reconciling the individual differences between bloodstreams and activities.