Climate changes have a tremendous impact on coastal and littoral areas, strongly affected by seaquakes and floods. Moreover, global warming causes a drastic change on the biodiversity of rivers, seas, lakes, including in biodiversity hotspots and protected areas, such as the Venice Lagoon in Italy. A similar impact is caused by pollutants: this called for a largescale long-term action that aims to monitor aquatic environmental parameters in order to predict, manage and mitigate these effects. Yet, coastal systems are highly heterogeneous in space and variable over short (daily), medium and long (seasonal, interannual) timescales, making reliable but affordable monitoring a challenging task. This paper proposes to automate this process with the use of a low-power sustainable integrated underwater and above water Internet of Things sensor network, able to collect water measurements in a cloud database and make them available to researchers to monitor the status of a certain area and develop their predictions models. Simulation results highlight how Low-Power Wide-Area Networks can support the data collection from a dense sensor deployment.

The underwater acoustic channel is remarkably dependent on the considered scenario and the environmental conditions. In fact, channel impairments differ significantly in shallow water with respect to deep water, and the presence of external factors such as snapping shrimps, bubbles, rain, or ships passing nearby, changes of temperature, and wind strength can change drastically the link quality in different seasons and even during the same day. Legacy mathematical models that consider these factors exist, but are either not very accurate, like the Urick model, or very computationally demanding, like the Bellhop ray tracer. Deterministic models based on lookup tables (LUTs) of sea trial measurements are widely used by the research community to simulate the acoustic channel in order to verify the functionalities of a network in certain water conditions before the actual deployment. These LUTs can characterize the link quality by observing, for instance, the average packet error rate or even a time varying packet error rate computed within a certain time window. While this procedure characterizes well the acoustic channel, the obtained simulation results are limited to a single channel realization, making it hard to fully evaluate the acoustic network in different conditions. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experiment data, and compare its performance with the other channel models available in the DESERT Underwater network simulator.

The features of the underwater acoustic channel are remarkably dependent on the considered scenario; for instance, the link quality differs significantly in shallow water with respect to deep water, and series of events such as the presence of rain or ships passing nearby, changes of temperature and wind strength, can change drastically the channel conditions observed in a certain area in different seasons and even during the same day. Mathematical models that consider these parameters exist, but are either very computationally demanding, like the Bellhop ray tracer, or not sufficiently accurate, like the Urick model that often exhibits optimistic results. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experimental data and compare its performance with the other channel models available in the DESERT Underwater network simulator.