Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

• Published in: IEEE access Vol. 8; pp. 101209 - 101223
• Main Authors: Luccio, Diana Di, Riccio, Angelo, Galletti, Ardelio, Laccetti, Giuliano, Lapegna, Marco, Marcellino, Livia, Kosta, Sokol, Montella, Raffaele
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Bathymeters; Boats; Coastal waters; Crowdsourcing; Data collection; Farms; food quality; Generators; Industrial plants; Internet of Things; marine data crowdsourcing; Marine environment; Marine technology; Model accuracy; mussel farm; Numerical models; Ocean dynamics; Oceans; Pollutants; Sea currents; Sea measurements; Sensors; Spatial distribution; The Internet of Floating Things; Tracers; Water quality; Workflow
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2996778

While the everything as a sensor is a typical data gathering pattern in the Internet of Things (IoT) applications in contexts such as smart cities, smart factories, and precision agriculture, among others, the use of the same technique in the coastal marine environment is still not explored at full potential. Nevertheless, when it comes to maritime scenarios, the application of IoT and networks of distributed sensors and actuators are still limited, even though the development of marine electronics and extreme network technologies are present for decades also in this area. In this paper, we first introduce the concept of the Internet of Floating Things (IoFT), which extends the IoT to the maritime scenario. Next, we present our latest implementation of the DYNAMO (Distributed leisure Yachts sensor Network for Atmosphere and Marine Observations) system, a framework for coastal data collection from sensors and devices deployed in marine equipment. To demonstrate the importance of IoFT data collection in the real-world environmental science context, we consider a scientific workflow for coastal water quality. The selected application focuses on predicting the spatial and temporal pattern of sea pollutants and their possible presence and time of persistence in the proximity of mussel farm areas in the Bay of Pozzuoli in Italy. The pollutants are simple Lagrangian particles, so the ocean dynamics play an important role in the simulation. Our results show that integrating crowdsourced bathymetry data in the workflow numerical model setup improves the accuracy of the final results, allowing for a more detailed spatial distribution pattern of the sea current driving the Lagrangian tracers.