## 1 Introduction  
Surface flow monitoring from images is a promising yet well-known technique to face the problem of estimating river flow velocity. River velocity measurements typically require relevant human and financial efforts for organizing sporadic campaigns that, in addition, cannot be carried out, for safety reasons, during flood events, when such information is more vital. Therefore, automatic measuring systems are pivotal to avoid cumbersomely demanding and expensive procedures carried out by specialized human operators. Radar systems are still costly and cannot monitor the entire width of medium-large rivers. Conversely, automatic image-based methods would seamlessly enable continuous monitoring with clear advantages, among many, for costs and for the frequency and spatial density of measurements in case multiple devices are deployed along the river. In open channels, streamflow monitoring is typically conducted through the velocity-area method, which is based on discrete integration of discharge from flow velocity and depth measurements sampled throughout the channel cross section. This mandates the use of permanent installations of instrumentation powered through the grid. For instance,acoustic Doppler current profilers (ADCPs), pulsed doppler radars, and ultrasonic water level meters are among the most popular systems for streamflow measurements. In recent years, several research groups have been investigating on innovative monitoring approaches with image analysis, and some companies have been providing off-the-shelf image-based systems for river flow monitoring (www.tenevia.com, accessed on 28 July 2021, www.photrack.ch, accessed on 28 July 2021). However, a thorough assessment of the power consumption characteristics of such systems is not available. Furthermore, commercially-available systems exploit proprietary software, and involve centralized supervisors at the companies to store and manage data. For the reasons outlined, this topic has been deeply investigated in the literature, and the Optical Tracking Velocimetry (Source code available at: [[https://github.com/fabiotosi9](https://github.com/fabiotosi9)]([https://github.com/fabiotosi9](https://github.com/fabiotosi9)) 2/Optical-Tracking-Velocimetry, accessed on 28 July 2021) technique [1] seems particularly promising. This technique allows for achieving state-of-the-art results in a fraction of the time required by other methods without the need for wideband communication infrastructures for off-load processing. Moreover, its lightweight computational structure also enables deployment on standard and cheap embedded devices such as those belonging to the Raspberry Pi family [2], as reported in [3]. Therefore, it appears at hand the opportunity to design cheap and self-powered monitoring systems capable of continuous monitoring in situ without any constraint regarding the available infrastructures as sketched in Figure 1. It is worth highlighting that, with such a configuration, a simple low bandwidth text messaging system would suffice to communicate to a centralized server the outcome of the velocity estimation procedure, with a simple SMS, instead of transmitting the entire, heavy images to be then processed elsewhere, with significant implications concerning cost and scalability if a distributed network of gauge-cameras is deployed. With this goal in mind, in this paper, at first, we propose an additional simplification to the OTV algorithm to reduce further its computational requirements without sacrificing its accuracy. Then, we define some self-powered setups based on cheap and standard solar  
Figure 1: Sketch of the envisioned solar-powered measurement node made up of standard off-the-shelf devices placed in a remote monitoring site. The outcome computed in situ by the node is optionally sent to a centralized gathering system through standard text messaging such as a simple SMS requiring a shallow bandwidth communication channel.