Smell of Source: Learning-Based Odor Source Localization with Molecular Communication

Abstract

Odor source localization is a fundamental challenge in molecular communication, environmental monitoring, disaster response, industrial safety, and robotics. In this study, we investigate three major approaches: Bayesian filtering, machine learning (ML) models, and physics-informed neural networks (PINNs) with the aim of odor source localization in a single-source, single-molecule case. By considering the source-sensor architecture as a transmitter-receiver model we explore source localization under the scope of molecular communication. Synthetic datasets are generated using a 2D advection-diffusion PDE solver to evaluate each method under varying conditions, including sensor noise and sparse measurements. Our experiments demonstrate that Physics-Informed Neural Networks (PINNs) achieve the lowest localization error of \(0.89 10^-6\) m, outperforming machine learning (ML) inversion (\(1.48 10^-6\) m) and Kalman filtering (\(1.62 10^-6\) m). The reinforcement learning (RL) approach, while achieving a localization error of \(3.01 10^-6\) m, offers an inference time of \(0.147\) s, highlighting the trade-off between accuracy and computational efficiency among different methodologies.

Tasks

Reproductions