Learning at the Nano-Scale: How to Dynamically Protect Data in Nano-Network Transmissions

Data transmission at the nano-scale faces constraints that are notably different from those of conventional wireless systems, especially in terms of communication reliability, resource availability, and security. In the Internet of Nano-Things (IoNT) environments, protecting sensitive information while preserving acceptable network performance is still an open challenge, particularly when cryptographic mechanisms introduce additional processing and communication overhead. To address such an issue, this paper proposes an IoNT framework that integrates a DNA-based bio-molecular cryptographic scheme with a machine learning (ML) module aimed at dynamically predicting suitable security configurations under changing network conditions. The proposed approach is evaluated in a healthcare scenario based on a simulated artery environment, considering different routing/MAC combinations and varying numbers of nano-devices. Results show that the integration of ML enables improved robustness under congestion conditions, reducing packet loss by approximately 33–37