SmartScape: Distributed Strain Sensing on Dublin City Telecom Fibre to Monitor Urban and Subsurface Dynamics for Smart City Applications

Rapid urban growth in Dublin is placing increasing pressure on transport systems, construction activity, and environmental management, creating a clear need for high-resolution observations of how the city operates at both surface and subsurface levels. This study presents the initial stage of a new project that explores the feasibility of using existing optical telecommunication infrastructure as a large-scale urban sensing platform through Distributed Strain Sensing (DSS). DSS converts optical fibres into dense seismic arrays by measuring strain-rate perturbations caused by ground vibrations, offering a cost-efficient approach to city-scale monitoring. This can have a potentially transformative impact on smart and sustainable city management, offering new data insights into urban dynamics while leveraging existing city-owned fibre infrastructure.

We report on a first pilot deployment on a dark ~80 km fibre ring crossing the city centre, residential neighbourhoods, surface tram lines, and an underground tunnel. A FEBUS-A1 interrogator was installed at a data centre in Dublin’s north side and operated for 23 days. Several acquisition configurations were tested, with the most stable setup recording ~60 km of fibre at 500 Hz sampling and 20 m gauge length for a continuous 10-day period. Remote access enabled iterative optimisation of acquisition parameters during the experiment.

The analysis presented here is preliminary and focuses on assessing data quality, signal content, and key technical limitations. Initial observations indicate that the DSS array captures clear signatures of moving vehicles with different velocities, rail-related activity, and teleseismic signals, including the October 10^th M7.4 Mindanao, Philippines event. Signal quality progressively degrades beyond ~30 km from the interrogator, where noise becomes dominant, highlighting challenges associated with attenuation, coupling, and urban noise in long fibre links.

Ongoing work focuses on developing denoising and source-identification strategies, including cross-correlation approaches and unsupervised machine-learning, alongside accurate georeferencing of fibre channels onto detailed urban maps. These analyses will be integrated with independent datasets such as traffic records from Dublin City Council and existing environmental acoustic noise maps. Rather than delivering operational products, this study is intended to establish a robust baseline on data quality, signal content, and interpretability, defining what information can realistically be extracted from urban DSS deployments in Dublin at this early stage.