Enabling Image-Based Streamflow Monitoring at the Edge
Open Access
- 25 June 2020
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 12 (12), 2047
- https://doi.org/10.3390/rs12122047
Abstract
Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at variable flow regimes. Nonetheless, to tackle their computational and energy requirements, offload processing and high-speed internet connections in the monitored environments, which are often difficult to access, is mandatory hence limiting the effective deployment of such techniques in several relevant circumstances. In this paper, we advance and simplify streamflow velocity monitoring by directly processing the image stream in situ with a low-power embedded system. By leveraging its standard parallel processing capability and exploiting functional simplifications, we achieve an accuracy comparable to state-of-the-art algorithms that typically require expensive computing devices and infrastructures. The advantage of monitoring streamflow velocity in situ with a lightweight and cost-effective embedded processing device is threefold. First, it circumvents the need for wideband internet connections, which are expensive and impractical in remote environments. Second, it massively reduces the overall energy consumption, bandwidth and deployment cost. Third, when monitoring more than one river section, processing “at the very edge” of the system efficiency improves scalability by a large margin, compared to offload solutions based on remote or cloud processing. Therefore, enabling streamflow velocity monitoring in situ with low-cost embedded devices would foster the widespread diffusion of gauge cameras even in developing countries where appropriate infrastructure might be not available or too expensive.Keywords
Funding Information
- Italian Ministry of Education, University and Research ("Departments of Excellence-2018" Program, DIBAF-Department of University of 422 Tuscia, Project ”Landscape 4.0 – food, wellbeing and environment”)
This publication has 29 references indexed in Scilit:
- Experimental methods for river discharge measurements: comparison among tracers and current meterHydrological Sciences Journal, 2011
- Integrating cross-correlation and relaxation algorithms for particle tracking velocimetryExperiments in Fluids, 2010
- Stream discharge using mobile large‐scale particle image velocimetry: A proof of conceptWater Resources Research, 2008
- Experimental System for Real-Time Discharge Estimation Using an Image-Based MethodJournal of Hydrologic Engineering, 2008
- Development of a non‐intrusive and efficient flow monitoring technique: The space‐time image velocimetry (STIV)International Journal of River Basin Management, 2007
- Large-scale particle image velocimetry for flow analysis in hydraulic engineering applicationsJournal of Hydraulic Research, 1998
- OpenMP: an industry standard API for shared-memory programmingIEEE Computational Science and Engineering, 1998
- Unsteady surface-velocity field measurement using particle tracking velocimetryJournal of Hydraulic Research, 1995
- Particle-Imaging Techniques for Experimental Fluid MechanicsAnnual Review of Fluid Mechanics, 1991
- Determining optical flowArtificial Intelligence, 1981