Assessment of an Ultrasonic Water Stage Monitoring Sensor Operating in an Urban Stream

Evaluation of Water Level in Flowing Channels Using Ultrasonic Sensors

Monitoring flow in channels is difficult, expensive, and potentially dangerous; hence, alternatives minimizing these factors are sought and indirect methods to measure the flow based on water-level information, among others, are employed. Ultrasonic sensors along with Arduino are widely used to monitor levels in reservoirs; however, the accuracy of this method in turbulent flow regimes has not been evaluated. Therefore, in this study, we evaluated the level of open channel flows using a combination of Arduino and ultrasonic sensors whilst considering turbulence. Additionally, we statistically compared the simultaneous measurements of levels obtained using five ultrasonic sensors with those of five rulers filmed individually along an artificial channel, for four permanent and two transient regimes. The results showed that the errors in measurements increased with increasing turbulence. These errors were within the range of hydraulic measurements (<0.020 m), indicating that the procedure is valid for experienced conditions. Therefore, the combination of Arduino and ultrasonic sensors is a technically and economically viable alternative. However, calibrating and validating the sensors for distances greater than 0.400 m should be performed with care because the bench tests performed in static conditions were limitedly accurate in measuring distances greater than 0.200 m.

Processing at the Edge: A Case Study with an Ultrasound Sensor-Based Embedded Smart Device

In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle’s speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point.