Real-time monitoring of fishway performance using a customizable sensor network architecture

In today's world, real-time data collection and processing is essential for the management of natural environments, research, and the implementation of adaptive management strategies. However, the available sensors and sensor network architectures often are expensive and offer limited customization options, limiting their use.
The EU-funded Smart Fishways project (Grant agreement ID: 101032024), has addressed this issue by developing a custom sensor architecture to assess the impact of hydrological variability on fishways and create a new generation of fishways that are capable of self-deciding their optimal management and configuration. The developed sensor network architecture is a key component of this project, and the aim of this work is to showcase the system as it has direct applicability in the study of other natural environments.
The network follows a star architecture with independent, custom-made nodes (e.g., environmental sensors, water quality sensors, depth sensors), remotely connected via radio communication and autonomously managed by a central gateway. The open software architecture of the system allows for nodes to work independently as loggers, and the gateway is a fully functional Linux system, making it highly customizable and equipped with edge computing capabilities such as running custom Python scripts and machine learning algorithms. Furthermore, the network is cost-effective and customizable, making it an ideal solution for other monitoring applications such as air and water quality, wildlife monitoring, and many other environmental monitoring needs.
To date, the sensor network has successfully demonstrated its use in optimizing the timing of maintenance on fishways in real-time, and in detecting hydraulic configurations that maximize fish passage. Additionally, it enables the development of adaptive management strategies that optimize fish passage and improve the overall effectiveness of fishways.