Abnormality Detection inside Blood Vessels with Mobile Nanomachines

TL;DR

This paper proposes a novel molecular communication system within blood vessels using mobile nanomachines for abnormality detection, deriving detection probabilities and validating results through simulations.

Contribution

It introduces a new model for abnormality detection with mobile nanomachines in blood vessels, including fusion rules and analytical probability derivations.

Findings

Derived detection and false alarm probabilities for the system.

Validated analytical results with simulations.

Provided insights into molecular communication in blood flow.

Abstract

Motivated by the numerous healthcare applications of molecular communication within Internet of Bio-Nano Things (IoBNT), this work addresses the problem of abnormality detection in a blood vessel using multiple biological embedded computing devices called cooperative biological nanomachines (CNs), and a common receiver called the fusion center (FC). Due to blood flow inside a vessel, each CN and the FC are assumed to be mobile. In this work, each of the CNs perform abnormality detection with certain probabilities of detection and false alarm by counting the number of molecules received from a source, e.g., infected tissue. These CNs subsequently report their local decisions to a FC over a diffusion-advection blood flow channel using different types of molecules in the presence of inter-symbol interference, multi-source interference, and counting errors. Due to limited computational capability at the FC, OR and AND logic based fusion rules are employed to make the final decision after obtaining each local decision based on the optimal likelihood ratio test. For the aforementioned system, probabilities of detection and false alarm at the FC are derived for OR and AND fusion rules. Finally, simulation results are presented to validate the derived analytical results, which provide important insights.