project . 2022 - 2026 . On going


Software enabled Fiber optic multisensing Network
Open Access mandate for Publications and Research data
European Commission
Funder: European CommissionProject code: 101093015 Call for proposal: HORIZON-CL4-2022-DIGITAL-EMERGING-01
Funded under: HE | HORIZON-RIA\HORIZON-AG Overall Budget: 3,694,300 EURFunder Contribution: 3,694,300 EUR
Status: On going
01 Dec 2022 (Started) 30 Nov 2026 (Ending)

Today’s fiber optical sensor systems show very promising performance and are well suited for numerous sensing tasks. Despite the enormous progress that has been made for fiber sensors, these systems are still more of a niche solution. Adaptive, modular sensor systems with flexibility regarding the sensing concept, fiber-based sensor type, sensor or sensor network configuration and measured quantities are highly desired but not available at this time. For example, already installed fiber infrastructure along railroad tracks or in buildings are hardly used in such systems. Nevertheless, there is an enormous potential to realize sensing networks by using the existing fiber infrastructure for both purposes, carrying data and perform sensing. Furthermore, a cloud-based digital control software defined setup, advanced signal processing with methods of machine learning, digital twin technologies and cloud-based interfacing to monitor the system performance are required and motivate further research and development in this field. There are though specific challenges to be addressed: SoFiN addresses these challenges, contributing to adaptive photonic multi-sensing systems with focus on: a)developing & testing a novel flexible, adaptive, modular, software-defined sensor platform b)studying ways to establish sensing options in existing communication fiber networks c)developing new laser types, sensor elements & sensing approaches compatible with fiber sensing d)studying concepts for ML-based signal processing and digital twin sensor system modelling e)developing machine learning tools that can handle large amounts of incoming data f)researching on ML tools that can enhance the signal-to-noise-ratio (SNR) and provide ultimate sensitivity g)developing centralized processing unit that integrates all sensory input data h)developing and implementing concepts for cloud-based system control i)performing a system test in a near to operational environment in different case studies

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