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.Comment: 6 pages, 7 figures, 4 tables, 16 reference