In this study the performance of the Internet of Fish (IoF) concept, a real-time acoustic positioning and fish monitoring system, was assessed in a commercial marine fish farm in Norway. Central to the IoF concept is the Synchronisation and LoRa Interface Module (SLIM), which is a battery operated surface unit that provides distributed time synchronisation and LPWAN support to a submerged digital acoustic receiver. Six SLIM/acoustic receiver pairs were placed inside a fish cage with acoustically tagged fish at a link-length of 200 m from a centralised gateway. All nodes achieved a Packet Error Rate of less than 8% and a position accuracy of 1.5 m.
Aquaculture is one of the fastest growing food producing industries in the world and is believed to be instrumental in filling the future global supply-demand gap in aquatic food (FAO., 2016). Raising fish in large floating net-based sea-cages have proven as a competitive option due to its flexibility, robustness and cost effectiveness (Føre et al., 2017), despite the generally harsh marine environment and technological and operational challenges it poses to the aquaculture industry. For instance, more than two million tons of Atlantic salmon are produced annually using this farming concept (Liu et al., 2016). The ability to monitor fish behaviour is important, as it is a key element in determining the stress and welfare conditions experienced by the fish in a farm situation (Oppedal, Dempster, and Stien, 2011). In addition, quantifying the movement patterns of fish is critical to understand feeding behaviours, resource utilisation and animal-environment interactions in cages (Espinoza et al., 2011; Biesinger et al., 2013). Acoustic telemetry is fish monitoring concept where individual animals are equipped with miniature electronic devices called transmitter tags that contain sensors and an acoustic modem for wireless underwater data transmission (see Føre, Alfredsen, and Gronningsater (2011) for a more thorough description of the contents of acoustic transmitter tags). This method has been used to observe detailed movement patterns of individual fish by employing source localisation algorithms (Pincock and Johnston, 2012). Previous applications of this approach include tracking of both wild (Espinoza et al., 2011; Biesinger et al., 2013) and farmed fish (Rillahan et al., 2009). Since farmed fish are generally restricted by the confines of the cages, their movement patterns are restricted to be within a much smaller volume than free swimming wild fish. This suggests that it is possible to realise automated positioning systems for aquaculture applications that are more precise than those developed for wild fish monitoring. Considering the large biomass, cage volumes and expected future growth trends in the marine finfish aquaculture industry, a remote monitoring system that can provide input to the day-to-day farm decisions is an essential requirement for realising the benefits and advances of the Precision Fish Farming (PFF) concept (Føre et al., 2017).
