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.

The objective of the present study is to achieve a better understanding of the possible rotation of embayed beaches using shoreline position data from two beaches on the central Tyrrhenian Sea (Italy) during a 64-year period. With this aim, this study tests the utility of Differential Global Positioning System (DGPS) and GPS RTK (Real Time Kinematic) dual-frequency navigation system for the in situ surveys, low-altitude aerial imagery collected by Unmanned Aerial Vehicle (UAV), and satellite polarimetric Synthetic Aperture Radar (SAR) measurements. The dataset consists of aerial photographs and orthophotos, DGPS, and remote surveys performed by UAV and SAR platform in 2018 along with wave data coming from the Ponza buoy. The results of the field data analysis explained a part of the beach variations in terms of coastline rotation around a virtual pivotal point. This rotation has been correlated with the wave directional shift in the recent period. The results of the comparison between the different techniques gave the possibility to investigate the limitations of remote survey methods for the identification of shoreline rotation.

This paper presents a Coastal Vulnerability Assessment (CVA) of a microtidal beach located on the Ionian Sea in Calabria region (southern Italy) in order to examine the influence of the different run-up equations on CVA score and propose mitigation measures for the most vulnerable parts of the beach. The coastal area has been severely eroded by extreme wave storms, which have also damaged important archaeological structures located on a nearby cliff. A typical 1 year return period (T_r) storm, associated with the recent criticalities, was chosen to test the different run-up formulas (Holman (1986), Mase (1989) Stockdon et al. (2006) and Poate et al. (2016)) on a number of beach profiles in order to check the sensitivity of the CVA calculation with regard to the different run-up equations. The obtained results provide evidence that different run-up levels often give rise to different CVA scores. Based on vulnerability results, some mitigation measures have been proposed for the beach in front of the archaeological area, based on submerged detached breakwater and an adherent gabion wall for the cliff defence.