Embedded DNA Inference in In-Body Nanonetworks: Detection, Delay, and Communication Trade-Offs

TL;DR

This paper investigates DNA-based embedded inference in in-body nanonetworks, showing it can improve alarm detection stability within specific operational regimes despite certain delays and costs.

Contribution

It introduces a DNA strand-displacement-based inference method and evaluates its effectiveness compared to raw reporting and threshold reporting in nanonetworks.

Findings

Embedded inference improves detection in weak-to-moderate anomaly ranges.

EIR offers more stable local alarm dynamics than raw reporting.

EIR's advantages are limited to specific operating regimes.

Abstract

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.