# Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System +
## 4 Discussion
On the other hand, it is worth emphasizing that the proposed solution transmits vessel locations using protocols endowed with security protocols. Unlike the AIS system, confidentiality is ensured in LoRaWAN and cellular networks through the use of the Public Key Infrastructure (PKI) scheme. Authenticity and integrity in LoRaWAN are satisfied by design using two session keys encrypted with 128-bit AES. Each packet encrypted with AES-CTR carries a frame counter (to prevent packet replication) and a message integrity code computed with AES-CMAC (to prevent packet tampering) [57; 58]. The cellular network, from 3G onwards and using the 3 GPP protocol, guarantees data integrity and allows secure authentication thanks to its three main components: user equipment, a mobile management entity, and a home-subscriber server [59]. Instead, AIS provides low levels of authenticity and integrity of transmitted data. While 2G/3G networks suffer from Denial-of-Service (DoS) attacks and an increasing number of subscribers, in 4G communication the availability increases thanks to restrictions on the number of connections into the core network and the new key hierarchy in the Evolved Packet System (EPS) [60]. The availability in LoraWAN and tAIS is lower than in cellular-based solutions (low and medium levels, respectively). Moreover, due to different frequency bands and national regulations, LoRaWAN is not universally compatible [61]. The cost of these technologies is very different (Table 3). LoRaWAN was developed for low-cost IoT solutions of about a few dozen Euros; the cost of cellular technology is in the middle, while both AIS classes cost considerably more. The experimental results demonstrate that the system guarantees a stable data acquisition ensuring a consistent reconstruction of the fishing patterns of the monitored vessel. In the pre-processing analysis, no errors in the acquisition rate, as well as no transmission gaps or erroneous pings (e.g., no points on land), occurred. Supported by these results, we developed specific routines to define trips and describe related fishing trip behavior. Different AIS/VMS analyzing methods have been inherited to identify trips using harbor polygons and/or time thresholds [62; 63], and they have been promisingly tested with SSF positional data [33]. However, digitizing polygons could become challenging--and related methods of trip reconstruction could fail--particularly when ports have particular shapes (e.g., with approach channels), tracking systems are widely adopted and/or a census \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Technology**} & \multicolumn{2}{c}{**Cost**} \\ \cline{2-3}  & **Hardware** & **Data Traffic** \\ \hline LoRaWAN & EUR 5–30 & - \\ \hline Cellular & EUR 10–80 & EUR cent/KB \\ \hline \multirow{2}{*}{AIS} & EUR 200–400 (Class B) & - \\  & EUR 800–5000 (Class A) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Costs of the technological solutions used. of ports does not exist. Moreover, small-scale vessels could leave/return to hot-spots that are not defined as harbors. Already in our case study, defining a wider a priori geofence has made the related alerts useless due to vessel movements near the edge of the polygon. For such a reason, expecting to equip a large number of vessels operating in different areas, we only relied on system parameters, such as the number of satellites and the power of the battery, to better identify the starting and shutdown of engine ignition and to define trips. This prevents a manual editing/reshaping of harbor polygons and fosters automation of data processing. The resulting trips allow to draft a preliminary fishing behavior of the monitored vessel that generally performs two trips per day, in line with other studies on small-scale fisheries using passive gear in the study area [15].