Chemo Hydrodynamic Transceivers for the Internet of Bio-Nano Things, Modeling the Joint Propulsion Transmission trade-off

TL;DR

This work tackles the challenge of jointly controlling propulsion and molecular signaling in mobile IoBNT transceivers. It derives an end-to-end stochastic channel where actuation affects both the mean signal and the channel noise, yielding a signal-dependent variance that scales quartically with control. The key result is a non-monotonic SNR with an explicit optimal actuation intensity $I_{opt}$ that scales approximately linearly with link distance, along with a quantified Estimation Gap versus standard Brownian mobility models. The findings offer physical-layer guidelines for actuation envelopes, symbol durations, and distance-aware control to enable reliable mobility-aware IoBNT protocols and closed-loop nanorobotic swarm management.

Abstract

The Internet of Bio-Nano Things (IoBNT) requires mobile nanomachines that navigate complex fluids while exchanging molecular signals under external supervision. We introduce the chemo-hydrodynamic transceiver, a unified model for catalytic Janus particles in which an external optical control simultaneously drives molecular emission and active self-propulsion. Unlike common abstractions that decouple mobility and communication, we derive a stochastic channel model that captures their physicochemical coupling and shows that actuation-induced distance jitter can dominate the received-signal variance, yielding a fundamental trade-off: stronger actuation increases emission but can sharply reduce reliability through motion-induced fading. Numerical results reveal a unimodal reliability profile with a critical actuation level beyond which the signal-to-noise ratio collapses, and an optimal control level that scales approximately linearly with link distance. Compared with Brownian-mobility baselines, the model exposes a pronounced estimation gap: neglecting active motility noise can underestimate the bit error probability by orders of magnitude. These findings provide physical-layer guidelines for mobility-aware IoBNT protocol design and closed-loop control of nanorobotic swarms.

Figures (8)

• Figure 1: Closed-loop IoBNT architecture in which the external command $I_{\text{ext}}(t)$ simultaneously governs propulsion $U(t)$ and molecular transmission, making channel statistics dependent on transceiver motility.
• Figure 2: Multi-scale modeling roadmap: $I_{\text{ext}}(t)$ drives micro-scale surface reaction and slip, setting propulsion $U(t)$ and emission $q(t)$. Propulsion enters meso-scale stochastic dynamics, yielding a time-varying effective diffusion $D_{\text{eff}}(t)$. At the macro scale, $q(t)$ and $D_{\text{eff}}(t)$ determine channel statistics and the received signal $Y(t)$ with signal-dependent variance.
• Figure 3: Cross-sectional schematic of the chemo-hydrodynamic transceiver. The external input $I_{\text{ext}}(t)$ modulates the reaction flux $k(\theta)$ on the catalytic hemisphere ($\theta<\alpha$), creating a concentration gradient $\nabla c$ that drives phoretic slip $\mathbf{v}_s$ and net propulsion $\mathbf{U}(t)$. The same surface reaction also sets the emission rate $q(t)$ of information molecules, coupling mobility and communication.
• Figure 4: Schematic representation of the one dimensional channel geometry. The axial position of the transceiver $x(t)$ is modeled as a stochastic variable characterized by a probability density function that is dynamically determined by the control input. The receiver node is located at a fixed coordinate $x=d$.
• Figure 5: Model verification: analytical PDFs (solid) versus simulation histograms for $I\in\{10,40,70,100\}$. The widening distribution with increasing $I$ indicates dominant active-motion noise in water.