Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System
Tassetti, Anna Nora; Galdelli, Alessandro; Pulcinella, Jacopo; Mancini, Adriano; Bolognini, Luca;
During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socioeconomic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats–for which space and power onboard are often limited–as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. 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.
2022 - Journal article
Sensors (Basel) 22 (2022). doi:10.3390/s22030839
Keywords: Cloud computing, fishery management, Maritime communications, Small-scale fisheries, Vessel positional data