Ultrasonic communication is one of the frequently discussed communication technologies for connecting in-body nanosensors with out-of-body gateway systems. Such communication has even been explored in various experimental setups. Yet, the impact of mobility remains primarily unexplored, which is critical for in-body applications. This paper aims to fill this gap by studying the impact of mobility on ultrasonic communication channels as the link between traveling nanosensors in the blood flow and external gateways fixed to the skin. We study both Doppler shift as well as signal gain both in an analytical model as well as in an extensive set of simulations. Our results indicate a significant impact of Doppler shift that needs to be compensated for real communication. We also show that the position of the nanosensors with respect to the gateway plays a very important role. Thus, we open the path for new research on protocol design for in-body to out-of-body communication using ultrasound.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.