Channel Modeling and Experimental Validation of Odor-Based Molecular Communication Systems

Odor-based Molecular Communication (OMC) employs odor molecules to convey information, contributing to the realization of the Internet of Everything (IoE) vision. Despite this, the practical deployment of OMC systems is currently limited by the lack of comprehensive channel models that accurately characterize particle propagation in diverse environments. While existing literature explores various aspects of molecular transport, a holistic approach that integrates theoretical modeling with experimental validation for bounded channels remains underdeveloped. In this paper, we address this gap by proposing mathematical frameworks for both bounded and unbounded OMC channels. To verify the accuracy of the proposed models, we develop a novel experimental testbed and conduct an extensive performance analysis. Our results demonstrate a strong correlation between the theoretical derivations and experimental data, providing a robust foundation for the design and analysis of future end-to-end OMC systems.

💡 Research Summary

The paper tackles a critical gap in odor‑based molecular communication (OMC) by delivering rigorous analytical channel models for both bounded (duct‑like) and unbounded (open‑air) environments and validating them with a purpose‑built experimental platform. The authors first formulate the transmitter as an instantaneous point source and describe odor propagation with the three‑dimensional advection‑diffusion equation, incorporating a constant wind vector and an effective turbulent diffusion coefficient. For the unbounded case, they adopt a closed‑form Gaussian puff solution that accounts for a perfectly reflecting ground plane. For the bounded case, they solve the transverse diffusion equation under Neumann (no‑flux) boundary conditions, yielding a series‑based concentration field expressed through functions a(r,y) and b(r,z).

Because the real transmitter releases a finite‑duration pulse, the system is assumed linear time‑invariant; the impulse response is convolved with a rectangular pulse to obtain the finite‑pulse concentration fields for both environments. The receiver is modeled in two stages: a static power‑law relationship between ambient concentration and the metal‑oxide (MOX) sensor resistance, and a dynamic first‑order system that captures adsorption‑desorption kinetics with distinct rise and decay time constants. Measurement noise is treated as signal‑dependent Gaussian noise.

Experimentally, the authors construct a modular testbed: an Arduino‑controlled solenoid valve array atomizes ethanol mist at a calibrated flow rate (≈5 m s⁻¹), a plexiglass tunnel (25 cm × 25 cm cross‑section) emulates a bounded channel, and an open laboratory table serves as the unbounded scenario. Key parameters (K = 0.05 m² s⁻¹, released mass ≈0.32 mol, sensor resistance, m = ‑1.03, etc.) are obtained from calibration and literature.

Results show excellent agreement between theory and measurement. In single‑pulse tests, the bounded channel achieves a Pearson correlation of 0.9842 and a normalized RMSE of 5.4 %, while the unbounded channel reaches 0.9922 correlation and 2.73 % NRMSE. Multi‑pulse experiments reveal that inter‑symbol interference (ISI) is pronounced in the bounded case at short symbol periods due to plume confinement and sensor decay dynamics, yet the convolution‑based model captures this behavior with correlations exceeding 0.97. In contrast, the unbounded channel exhibits minimal ISI across all symbol rates, confirming that free transverse diffusion dominates plume clearance.

Noise analysis indicates that residual errors are approximately Gaussian, with slight heavy‑tail deviations in the bounded case attributable to turbulent intermittency. The study also documents sensor fatigue effects, where decay time constants increase after prolonged unbounded trials.

Overall, the work provides a comprehensive, experimentally verified framework for OMC channel modeling, integrating physical transport, sensor dynamics, and stochastic noise. This foundation enables accurate performance prediction, optimal modulation design, and reliable error‑control coding for future IoE applications employing chemical signaling. Future directions suggested include multi‑transmitter networks, more complex flow fields, and extension to diverse odorants.