Edge Computing in Distributed Acoustic Sensing: An Application in Traffic Monitoring

TL;DR

This paper presents a real-time edge computing system for traffic monitoring using distributed acoustic sensing data, applying advanced signal processing and clustering techniques to detect and track vehicles with high accuracy.

Contribution

It introduces a novel edge-based methodology combining Hough transform and DBSCAN clustering for real-time vehicle detection and speed estimation from DAS data.

Findings

High accuracy vehicle detection and classification

Effective real-time processing with tens of seconds latency

Successful structural health monitoring via DAS signals

Abstract

Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tracking vehicles using DAS data, focusing on real-time processing through edge computing. Our approach applies the Hough transform to detect straight-line segments in the spatiotemporal DAS data, corresponding to vehicles crossing the Astfjord bridge in Norway. These segments are further clustered using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm to consolidate multiple detections of the same vehicle, reducing noise and improving accuracy. The proposed workflow effectively counts vehicles and estimates their speed with only tens of seconds latency, enabling real-time traffic monitoring on the edge. To validate the system, we compare DAS data with simultaneous video footage, achieving high accuracy in vehicle detection, including the distinction between cars and trucks based on signal strength and frequency content. Results show that the system is capable of processing large volumes of data efficiently. We also analyze vehicle speeds and traffic patterns, identifying temporal trends and variations in traffic flow. Real-time deployment on edge devices allows immediate analysis and visualization via cloud-based platforms. In addition to traffic monitoring, the method successfully detected structural responses in the bridge, highlighting its potential use in structural health monitoring.