Abnormality Detection inside Blood Vessels with Mobile Nanomachines

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.