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.

翻译：体内分子纳米网络有望在生化事件源头附近实现早期异常检测，但其通信能力受限于缓慢的扩散信号传输与不稳定的警报流量。本研究探讨纳米节点上基于DNA的简易嵌入式推理能否改善向外部网关的警报传输。我们采用面向通信的抽象模型，将原始报告、单标记阈值报告与嵌入式推理报告进行对比，该模型基于DNA链置换计算，包含标记门控、边沿触发报警、迟滞状态转换、时间相关标记动力学、扩散警报传输及泄漏网关证据整合。仿真实验在弱至中等异常范围内界定了嵌入式推理的成功边界：与原始报告和阈值报告相比，嵌入式推理能在保持与事件驱动通信成本竞争力（尤其优于原始报告）的同时提升检测性能。这一优势并非源于整体更低的通信活动，而是由于更稳定的本地警报动力学。嵌入式推理未在全球范围内占据主导优势：异常存在时阈值报告往往更具成本效益，且嵌入式推理会引入额外的本地延迟。这些结果表明嵌入式推理仅在有限的操作范围内有效，而非具备跨场景的普适优势。