This thesis develops a theoretical framework to evaluate the monitoring capability of IoBNT networks. We consider a scenario in which nanosensors passively flow in the bloodstream and detect biomarkers associated with potential diseases, reporting their detections to external gateways on the skin that host a monitoring device. The nanosensors thus realize an artificial point-to-point communication channel between the disease region and the monitor: some packets reach the destination directly, while others are lost through vessel paths that bypass the gateway. We evaluate the network's monitoring capability over this artificial channel using the \ac{AoI} concept, which jointly integrates sample generation (at the disease region), carrying (nanosensor travel through vessels), and delivery (nanosensor-to-gateway) as random events. These are modeled through (i) a Markov model that follows cardiovascular physiology and (ii) channel models of reported nanocommunication technologies. We compute the Markov transition probabilities using a cardiovascular simulator built as a low-complexity electric circuit model of the human vessels. For the nanosensor-to-gateway link, we model two well-known schemes: ultrasonic and terahertz channels. Integrating these components within the \ac{AoI} framework, we report information freshness via the average \ac{PAoI} metric. Under realistic physiological and communication assumptions, fresh information appears on the monitor within tens of seconds. The network is therefore suitable for monitoring tissue-level processes such as bacterial infections, while more adequate architectures are needed to monitor cellular-scale processes, which occur on timescales below tens of seconds.