@proceedings {,
	title = {A low-cost and low-burden secure solution to track small-scale fisheries},
	journal = {Conference: 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)},
	year = {2021},
	month = {11/2021},
	pages = {382-387},
	abstract = {<p>During the last decade accurate spatial and quantitative information of industrial fisheries have been increasingly given using tracking technologies and machine learning analytical algorithms. However, in most small-scale fisheries, lack of spatial data has been a recurrent bottleneck as Vessel Monitoring System and Automatic Identification System, developed for vessels longer than 12 and 15 m in length respectively, have little applicability in these contexts. It follows that small-scale vessels (\&lt; 12 m in length) remain untracked and largely unregulated, even though they account for most of the fishing fleet in operation in the Mediterranean Sea. As such, the tracking of small-scale fleets tends to require the use of novel and low cost solutions that could be addressed by small vessels often without dedicated electrical systems. In this paper we propose a scalable architecture that makes use of a low-cost LoRaWAN/cellular network to acquire and process positioning data from small-scale vessels; preliminary results of a first installation of the prototype are presented, as well as the data collected. The emergence of a such low-cost and open source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management, and cross-border marine spatial planning.</p>
},
	keywords = {cloud computing, fleet management system, maritime communication, small-scale fisheries, vessel position data},
	doi = {https://doi.org/10.1109/MetroSea52177.2021.9611622},
	url = {https://ieeexplore.ieee.org/document/9611622},
	author = {Anna Nora Tassetti and Alessandro Galdelli and Jacopo Pulcinella}
}
