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Contemporary research advances in nanotechnology and material science arerooted in the emergence of nanodevices as a versatile tool that harmonizessensing, computing, wireless communication, data storage, and energyharvesting. These devices offer novel pathways for disease diagnostics,treatment, and monitoring within the bloodstreams. Ensuring preciselocalization of events of diagnostic interest, which underpins the concept offlow-guided in-body nanoscale localization, would provide an added diagnosticvalue to the detected events. Raw data generated by the nanodevices is pivotalfor this localization and consist of an event detection indicator and the timeelapsed since the last passage of a nanodevice through the heart. The energyconstraints of the nanodevices lead to intermittent operation and unreliablecommunication, intrinsically affecting this data. This posits a need forcomprehensively modelling the features of this data. These imperfections alsohave profound implications for the viability of existing flow-guidedlocalization approaches, which are ill-prepared to address the intricacies ofthe environment. Our first contribution lies in an analytical model of raw datafor flow-guided localization, dissecting how communication and energycapabilities influence the nanodevices' data output. This model acts as a vitalbridge, reconciling idealized assumptions with practical challenges offlow-guided localization. Toward addressing these practical challenges, we alsopresent an integration of Graph Neural Networks (GNNs) into the flow-guidedlocalization paradigm. GNNs excel in capturing complex dynamic interactionsinherent to the localization of events sensed by the nanodevices. Our resultshighlight the potential of GNNs not only to enhance localization accuracy butalso extend coverage to encompass the entire bloodstream.

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