Matched Filter-Based Molecule Source Localization in Advection-Diffusion-Driven Pipe Networks with Known Topology

TL;DR

This paper introduces a novel matched filter-based framework for localizing molecule sources in complex pipe networks using a mixture of inverse Gaussians model, demonstrating high accuracy even under noisy conditions.

Contribution

It presents the first source localization method for complex pipe networks with known topology, leveraging the MIGHT model and addressing realistic challenges like noise and unknown release times.

Findings

Outperforms random guessing in source localization at low SNRs.

Achieves error-free localization under high SNR and sampling rates.

Enables reliable cluster-level localization at lower SNRs and sampling rates.

Abstract

Synthetic molecular communication (MC) has emerged as a powerful framework for modeling, analyzing, and designing communication systems where information is encoded into properties of molecules. Among the envisioned applications of MC is the localization of molecule sources in pipe networks (PNs) like the human cardiovascular system (CVS), sewage networks (SNs), and industrial plants. While existing algorithms mostly focus on simplified scenarios, in this paper, we propose the first framework for source localization in complex PNs with known topology, by leveraging the mixture of inverse Gaussians for hemodynamic transport (MIGHT) model as a closed-form representation for advection-diffusion-driven MC in PNs. We propose a matched filter (MF)-based approach to identify molecule sources under realistic conditions such as unknown release times, random numbers of released molecules, sensor noise, and limited sensor sampling rate. We apply the algorithm to localize a source of viral markers in a real-world SN and show that the proposed scheme outperforms randomly guessing sources even at low signal-to-noise ratios (SNRs) at the sensor and achieves error-free localization under favorable conditions, i.e., high SNRs and sampling rates. Furthermore, by identifying clusters of frequently confused sources, reliable cluster-level localization is possible at substantially lower SNRs and sampling rates.