Age-of-Information-Based Performance of Ultrasonic Communication Channels for Nanosensor-to-Gateway Communication

The successful connection between in-body nanosensors and gateway devices outside the human body enables the identification of diseases, before symptoms even appear. In this paper, we investigate the timely detection of abnormalities using nanosensors flowing in the blood vessels and reporting to external wearable devices. We develop analytic solutions to evaluate the information freshness using the average peak age of information (PAoI) metric. We model the mobility of nanosensors in the blood flow as a random process through a Markov chain. The communication capabilities with external devices to report detected abnormalities are modeled as a linear time-variant (LTV) channel. Besides, for communication with external devices, we study the impact of the mobility of nanosensors when using ultrasonic waveforms and integrate it with the PAoI formulation. In addition to a previously proposed analytical model, we perform large-scale simulations using Blood Voyager Simulator (BVS)) and the network simulator ns-3. As primary metrics, we use the bit error rate (BER), the packet error rate (PER), and the average PAoI. The results give clear insights into the impact of the position of the external monitor. We also illustrate that local communication performance almost does not influence the average PAoI.