The increasing pressure on aquatic ecosystems—exacerbated by climate change, pollution, and human exploitation—calls for advanced and sustainable solutions for real-time environmental monitoring. In response to this need, the EcoMonitoring project introduces an innovative system for the observation of marine, coastal, port, and lake environments, integrating multiparametric sensing and IoT networks.
Unlike traditional buoys, which are often limited in terms of accuracy, operational autonomy, and processing capability, EcoMonitoring relies on a mobile system composed of the MoBI buoy, towed by an aquatic drone. The buoy collects environmental parameters using a multiparametric probe, including temperature, pH, electrical conductivity, dissolved oxygen, turbidity, and wave motion data. The latter is analyzed in real time through dedicated algorithms that process readings from an accelerometer, gyroscope, and magnetometer to determine wave height, period, and direction.
The technological infrastructure features a dual-hardware architecture (Raspberry Pi and Arduino Mega), modular software, and a flexible data transmission system via LTE/4G or LoRa with automatic fallback. Collected data is sent to a scalable cloud backend, where it is stored in a data lake and processed using deep learning models for predictive analysis, anomaly detection, and geospatial visualization.
During the monitoring process, the buoy is towed along a predefined route and, upon reaching designated waypoints, automatically performs environmental data acquisition and transmission.
The system’s modularity and autonomous operation make it a highly adaptable solution for both scientific research and industrial applications, actively contributing to the protection of aquatic ecosystems.
Author Contributions
A.C., A.M., S.P., M.A., M.A., and D.D.G. all contributed significantly to the work. Specifically, the contributions for Methodology, Investigation, and Data Curation were performed by A.C., A.M., S.P., M.S., M.A., and D.D.G. The Writing—original draft preparation, and Writing—review and editing were also handled by A.C., A.M., S.P., M.S., M.A., and D.D.G. All authors contributed equally to the development, drafting, and testing of the methodologies, and at all stages inherent in the development and writing of the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This research activity was partially funded by the European Union—NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investment 1.5, project “RAISE—Robotics and AI for Socio-economic Empowerment” (ECS00000035).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available upon request from the corresponding author.
Conflicts of Interest
Alberto Mancosu, Alessio Chirigu and Mariella Sole were wmply by Hedya company. The remaining authors declare no conflict of interest.
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