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

TL;DR

This paper presents an analytical model for raw data in flow-guided in-body nanoscale localization, accounting for communication and energy constraints, validated against a realistic simulator to improve precision medicine applications.

Contribution

The paper introduces a novel analytical model of raw data for nanoscale localization that considers communication and energy limitations, validated against a high-fidelity simulator.

Findings

High similarity between the model and simulator data across scenarios

Model effectively captures effects of communication constraints

Model aids in designing better nanoscale localization systems

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.