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.
Highlights
River discharge observations are essential for hydrological studies and related practical applications, accurate measurements in diverse flow regimes and in difficult-to-access locations are still unfeasible with standard instrumentation [1,2].In recent years, several contributions have demonstrated that image-based analysis approaches for estimating the flow surface velocity are promising alternatives to traditional measurement [3]
The in situ processing strategy developed in this paper that leverages a possibly solar powered embedded system with limited connectivity is displayed and, on the right, the current solution based on cloud processing and a high-speed data connection is depicted
For a given video sequence, we report both the average velocity and standard deviation computed by averaging values obtained from Equation (3) with the baseline Optical Tracking Velocimetry (OTV) algorithm [25] and its optimized counterpart proposed in this paper
Summary
River discharge observations are essential for hydrological studies and related practical applications, accurate measurements in diverse flow regimes and in difficult-to-access locations are still unfeasible with standard instrumentation [1,2]. While it was proved that this innovative image-based approach is reliable and affordable, state-of-the-art algorithms recently evaluated in [12], have some severe constraints that still hamper their widespread diffusion They are rather computational demanding, and this fact dictates the use of power-hungry architectures, preventing in most cases their practical deployment in situ due to energy and cost requirements. In situ river flow monitoring with a low-power system would mitigate most of the above-mentioned criticalities and encourage the diffusion of image-based methodologies in diverse environments To this end, in this paper, we propose a cost-effective solution based on a lightweight embedded system that yields an accuracy equivalent to state-of-the-art solutions with a fraction of their power budget. Such a low-budget solution would foster its deployment in developing countries often even more deeply affected by the limitations of power-hungry and expensive systems
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