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.

翻译：纳米技术与材料科学的当代研究进展植根于纳米器件作为一种多功能工具的出现，其融合了传感、计算、无线通信、数据存储与能量收集。这些器件为血流内的疾病诊断、治疗与监测提供了新途径。确保对具有诊断意义的事件进行精确定位——这构成了流引导体内纳米级定位概念的基础——将为检测到的事件提供额外的诊断价值。纳米器件生成的原始数据对此定位至关重要，其包含事件检测指示器以及自纳米器件上次通过心脏以来所经过的时间。纳米器件的能量限制导致间歇性运行与不可靠通信，从本质上影响了这些数据。这提出了全面建模该数据特征的需求。这些缺陷也对现有流引导定位方法的可行性产生了深远影响，这些方法难以应对环境的复杂性。我们的首要贡献在于提出了用于流引导定位的原始数据分析模型，剖析了通信与能量能力如何影响纳米器件的数据输出。该模型充当了关键桥梁，弥合了流引导定位的理想化假设与实际挑战。为应对这些实际挑战，我们还将图神经网络（GNNs）整合到流引导定位范式中。GNNs擅长捕捉纳米器件感知事件定位所固有的复杂动态交互。我们的研究结果凸显了GNNs不仅能够提高定位精度，还能将覆盖范围扩展至整个血流系统的潜力。