Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization
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Abstract (arXiv)
Nanoscale devices with Terahertz (THz) communication capabilities are envisioned to be deployed within human bloodstreams. Such devices will enable fine-grained sensing-based applications for detecting early indications (i.e., biomarkers) of various health conditions, as well as actuation-based ones such as targeted drug delivery. Associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision yielded a new class of in-body localization coined under the term "flow-guided nanoscale localization". Such localization can be piggybacked on THz communication for detecting body regions in which biological events were observed based on the duration of one circulation of a nanodevice in the bloodstream. From a decades-long research on objective benchmarking of "traditional" indoor localization, as well as its eventual standardization (e.g., ISO/IEC 18305:2016), we know that in early stages the reported performance results were often incomplete (e.g., targeting a subset of relevant performance metrics), carrying out benchmarking experiments in different evaluation environments and scenarios, and utilizing inconsistent performance indicators. To avoid such a "lock-in" in flow-guided localization, in this paper we propose a workflow for standardized performance evaluation of such localization. The workflow is implemented in the form of an open-source simulation framework that is able to jointly account for the mobility of the nanodevices, in-body THz communication between with on-body anchors, and energy-related and other technological constraints (e.g., pulse-based modulation) at the nanodevice level. Accounting for these constraints, the framework is able to generate the raw data that can be streamlined into different flow-guided localization solutions for generating standardized performance benchmarks.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Mar 2023.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2023Insights from the Design Space Exploration of Flow-Guided Nanoscale Localizationcited by 3
- 2023Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Datacited by 2
- 2023Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localizationcited by 1
- 2024Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activitiescited by 0
- 2025Set Transformer Architectures and Synthetic Data Generation for Flow-Guided Nanoscale Localizationcited by 0
- 2025Set Transformer Architectures and Synthetic Data Generation for Flow-Guided Nanoscale Localization22 Aug 2025
- 2024Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities2 Aug 2024
- 2023Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization27 Sep 2023
- 2023Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data9 Jul 2023
- 2023Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization29 May 2023