Abnormality Detection Inside Blood Vessels with Mobile Nanomachines

Motivated by the numerous healthcare applications of molecular communication within Internet of Bio-Nano Things (IoBNT) paradigm, this paper 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 paper, each CN performs abnormality detection with certain probabilities of detection and false alarm. The CNs report their local decisions to an 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. The FC employs the OR and AND logic-based fusion rules to make the final decision after decoding the local decisions using the sub-optimal detectors based on the approximation of the log-likelihood ratio. For the aforementioned system, probabilities of detection, and false alarm at the FC are derived. Finally, simulation results are presented to validate the derived analytical results, which provide important insights.