Embedded DNA Inference in In-Body Nanonetworks: Detection, Delay, and Communication Trade-Offs
For designers of in-body nanonetworks, this work identifies a limited operating regime where embedded DNA inference is useful, rather than a general advantage, providing guidance on when to use such methods.
The paper studies whether simple embedded DNA-based inference at nanonodes can improve alarm transmission in in-body molecular nanonetworks. Simulations show that embedded inference reporting (EIR) improves detection over raw reporting and threshold reporting in a bounded weak-to-moderate anomaly range, with competitive communication cost, but does not dominate globally.
In-body molecular nanonetworks promise early abnormality detection close to the source of biochemical events, but their communication capabilities are severely constrained by slow diffusion-based signaling and unstable alarm traffic. We study whether simple embedded DNA-based inference at the nanonode can improve alarm transmission to an external gateway. We compare raw reporting (RR), single-marker threshold reporting (TR), and embedded inference reporting (EIR) under a communication-oriented abstraction of DNA strand-displacement-based computation with marker gating, edge-triggered alarming, hysteretic state transitions, temporally correlated marker dynamics, diffusion-based alarm transport, and leaky gateway evidence integration. The simulations identify a bounded EIR success regime in the weak-to-moderate anomaly range: EIR can improve detection relative to RR and TR while remaining competitive in event-driven communication cost, especially relative to RR. The gain does not come from uniformly lower activity, but from more stable local alarm dynamics. EIR does not dominate globally; TR often remains cheaper when abnormalities are present, and EIR incurs additional local delay. These results point to a limited operating regime in which EIR is useful, rather than to a general advantage across settings.