Scientific advancements in nanotechnology and advanced materials are paving
the way toward nanoscale devices for in-body precision medicine; comprising
integrated sensing, computing, communication, data and energy storage
capabilities. In the human cardiovascular system, such devices are envisioned
to be passively flowing and continuously sensing for detecting events of
diagnostic interest. The diagnostic value of detecting such events can be
enhanced by assigning to them their physical locations (e.g., body region),
which is the main proposition of flow-guided localization. Current flow-guided
localization approaches suffer from low localization accuracy and they are
by-design unable to localize events within the entire cardiovascular system.
Toward addressing this issue, we propose the utilization of Graph Neural
Networks (GNNs) for this purpose, and demonstrate localization accuracy and
coverage enhancements of our proposal over the existing State of the Art (SotA)
approaches. Based on our evaluation, we provide several design guidelines for
GNN-enabled flow-guided localization.Comment: 6 pages, 5 figures, 1 table, 15 references. arXiv admin note: text
overlap with arXiv:2305.1849