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Scientific advancements in nanotechnology and advanced materials are pavingthe way toward nanoscale devices for in-body precision medicine; comprisingintegrated sensing, computing, communication, data and energy storagecapabilities. In the human cardiovascular system, such devices are envisionedto be passively flowing and continuously sensing for detecting events ofdiagnostic interest. The diagnostic value of detecting such events can beenhanced by assigning to them their physical locations (e.g., body region),which is the main proposition of flow-guided localization. Current flow-guidedlocalization approaches suffer from low localization accuracy and they areby-design unable to localize events within the entire cardiovascular system.Toward addressing this issue, we propose the utilization of Graph NeuralNetworks (GNNs) for this purpose, and demonstrate localization accuracy andcoverage enhancements of our proposal over the existing State of the Art (SotA)approaches. Based on our evaluation, we provide several design guidelines forGNN-enabled flow-guided localization.

Graph Neural Network-enabled Terahertz-based Flow-guided Nanoscale Localization