Graph Neural Networks as an Enabler of Terahertz-Based Flow-Guided Nanoscale Localization Over Highly Erroneous Raw Data

• Published in: IEEE journal on selected areas in communications 2024-08, Vol.42 (8), p.1992-2008
• Main Authors: Calvo Bartra, Gerard, Lemic, Filip, Pascual, Guillem, Perez Rodas, Aina, Struye, Jakob, Delgado, Carmen, Costa Perez, Xavier
• Format: Article
• Language: English
• Subjects: Communication; Constraint modelling; Data storage; Energy consumption; Energy harvesting; Energy storage; flow-guided localization; Graph neural networks; Implants; Localization; Location awareness; Nanoscale devices; Nanotechnology devices; Neural networks; precision medicine; Software; Terahertz communications; terahertz nanocommunication; Tumors; Wireless communication; Wireless communications
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2024.3399257

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 hold promise in precision medicine, offering 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 intuitively 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 communication and 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, reinforced by the adaptability and resilience of Heterogeneous Graph Transformers (HGTs), 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.