Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization

TL;DR

The paper addresses the challenge of localizing events by flow-guided in-body nanoscale devices under energy harvesting and communication constraints. It develops an analytical raw-data model where the observed data X = $(n_1T_1 + \cdots + n_rT_r + Q, b)$ combines deterministic region travel times with zero-mean Gaussian noise, and accounts for compound iterations and detection/transmission dynamics via $P_{det}$ and $P_{trans}$. The model’s outputs are validated against a high-fidelity Blood Voyager + TeraSim simulator across 24 body regions using Mann-Whitney tests, ECDF differences, and KL divergence, showing high similarity (differences largely within a few percent and KL divergence < 0.04 in many cases). This work supports more robust, potentially patient-adaptive flow-guided localization pipelines and provides a foundation for handling physiological variability in in-body nanoscale sensing networks.

Abstract

Advancements in nanotechnology and material science are paving the way toward nanoscale devices that combine sensing, computing, data and energy storage, and wireless communication. In precision medicine, these nanodevices show promise for disease diagnostics, treatment, and monitoring from within the patients' bloodstreams. Assigning the location of a sensed biological event with the event itself, which is the main proposition of flow-guided in-body nanoscale localization, would be immensely beneficial from the perspective of precision medicine. The nanoscale nature of the nanodevices and the challenging environment that the bloodstream represents, result in current flow-guided localization approaches being constrained in their communication and energy-related capabilities. The communication and energy constraints of the nanodevices result in different features of raw data for flow-guided localization, in turn affecting its performance. An analytical modeling of the effects of imperfect communication and constrained energy causing intermittent operation of the nanodevices on the raw data produced by the nanodevices would be beneficial. Hence, we propose an analytical model of raw data for flow-guided localization, where the raw data is modeled as a function of communication and energy-related capabilities of the nanodevice. We evaluate the model by comparing its output with the one obtained through the utilization of a simulator for objective evaluation of flow-guided localization, featuring comparably higher level of realism. Our results across a number of scenarios and heterogeneous performance metrics indicate high similarity between the model and simulator-generated raw datasets.

Figures (7)

• Figure 1: Flow-guided in-body nanoscale localization framework.
• Figure 2: Probability distribution for two arbitrary regions with different traveling times (60,67), with probabilities: $P_{det}=0.7, P_{trans}=0.7$.
• Figure 3: MSE of generated frequencies with respect to the probability distribution as a function of nanonodes.
• Figure 4: ECDF comparison between model and simulator for $P_{trans}=0.4$ (left) and $P_{det}=0.4$ (right) in thorax.
• Figure 5: Iteration times obtained with event bit $b=1$ for the head region.