2025/02/10 by Ayse Sila Okcu, Okcu, Ayse Sila, Özgür B. Akan +1 · 1 citation
Agricultural and Biological Sciences · Engineering · #Advanced Chemical Sensor Technologies #FOS: Electrical engineering #Insect Pheromone Research and Control #Molecular Communication and Nanonetworks #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2502.07112
openalex publication_date 2025/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 \(\mathbf0.89 × 10-6\) m, outperforming machine learning (ML) inversion (\(\mathbf1.48 × 10-6\) m) and Kalman filtering (\(\mathbf1.62 × 10-6\) m). The reinforcement learning (RL) approach, while achieving a localization error of \(\mathbf3.01 × 10-6\) m, offers an inference time of \(0.147\) s, highlighting the trade-off between accuracy and computational efficiency among different methodologies.