# Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data

arXiv: https://arxiv.org/abs/2307.05551 · HTML: https://mlsanity.com/paper/2307.05551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Transformers learn in-context by gradient descent](https://mlsanity.com/paper/2212.07677/) (2022)
- [ViFi: Virtual Fingerprinting WiFi-based Indoor Positioning via Multi-Wall Multi-Floor Propagation Model](https://mlsanity.com/paper/1611.09335/) (2016)
- [Survey on Terahertz Nanocommunication and Networking: A Top-Down Perspective](https://mlsanity.com/paper/1909.05703/) (2019)
- [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)
- [Gaussian-Induced Convolution for Graphs](https://mlsanity.com/paper/1811.04393/) (2018)
- [Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization](https://mlsanity.com/paper/2305.18493/) (2023)
- [Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey](https://mlsanity.com/paper/2202.12409/) (2022)
- [Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions](https://mlsanity.com/paper/2304.06510/) (2023)

## Built on by (most cited first, 2 of 2)

- [Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities](https://mlsanity.com/paper/2408.01239/) (2024)
- [Set Transformer Architectures and Synthetic Data Generation for Flow-Guided Nanoscale Localization](https://mlsanity.com/paper/2508.16200/) (2025)

## Abstract (arXiv)

Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices offer novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices' data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.