Source Distance Estimation in Turbulent Airflow: Exploiting Molecule Degradation Diversity

2026-04-16

AI Summary

Accurately estimating the distance to a molecular source in turbulent airflow is crucial yet highly challenging for applications such as leak detection, foraging, or mate-seeking in synthetic molecular communication. This work proposes a novel approach that leverages the differential degradation rates of distinct molecules in the atmosphere, introducing molecular degradation diversity as a new dimension for distance estimation beyond conventional reliance on concentration or temporal features alone. By integrating the relative abundance, concentration, and temporal characteristics of mixed molecules observed at the receiver, the authors develop a low-complexity, multi-feature fusion estimation algorithm. Simulations grounded in realistic turbulent airflow dynamics and molecular degradation kinetics demonstrate that the proposed method significantly enhances distance estimation accuracy under turbulence, offering a practical pathway toward real-world deployment of synthetic molecular communication systems.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also received increasing attention in synthetic molecular communication (SMC), e.g., for leakage detection. One important aspect of source localization is to estimate the distance to the molecule source, e.g., to determine whether it is worth to travel to a potential mating partner or food source, or to decide whether a leak is close enough for inspection. In this study, based on realistic simulations, we show that the diversity induced by molecule mixtures can aid source localization. In particular, when different molecule types in a mixture are subject to atmospheric degradation with different degradation rates, the relative abundance of the different species observed at the receiver enables low-complexity estimation of the source distance. Furthermore, this feature can be combined with already established concentration-based and temporal features of observed molecular signals to further increase estimation accuracy. Thereby, we show that molecule degradation diversity of molecule mixtures can help to realize one of the important envisioned SMC applications, namely source localization, even in turbulent airflow, opening new opportunities for the exploitation of SMC to solve real-world problems.

Problem

• source distance estimation
• turbulent airflow
• molecular communication
• molecule degradation
• source localization

Innovation

• molecule degradation diversity
• source distance estimation
• turbulent airflow
• molecular communication
• mixture-based sensing