## 5 Conclusions  
In this paper, a low-cost WSN for aquaculture monitoring has been shown. The system can control the changes in water parameters, tank state and fish behavior during the feeding process. The monitored water parameters are the temperature, conductivity, turbidity, and the presence of oil layer over the water. The tank state parameters monitored by the system are the illumination, the water level, and the presence of workers. Finally, the fish behavior is monitored with the fish swimming depth sensor and velocity sensor, and the sensor that let us know the amount of feed falling. The topology and architecture have been detailed. Low-cost sensors have been designed, calibrated, and deployed. Smart algorithms were designed to diminish the use of energy in the data transference from the node to the database. The sensors shown in this paper can be used to improve the efficiency of the aquaculture and the cost of the entire described system is less than 90 \(\upepsilon\). The sensors are composed of simple electronic components. All the sensors have been calibrated and their suitability for the aquaculture monitoring has been exposed. The greatest efforts have been accomplished with the fish presence sensors and with the feed falling sensor. With the fish presence sensor, several tests were done. First, we performed tests to evaluate the suitability of LDRs to detect the presence of fish were done. Then, different tests were done to determine the presence of fish in different scenarios to calculate the fish depth and fish \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Purpose** & & **Item** & **Unitary Cost (\(\upepsilon\))** & **Units** & **Cost (\(\upepsilon\))** \\ \hline \multirow{8}{*}{Sensors} & \multirow{8}{*}{Water quality} & Temperature & NTCLE413E2103F520L & 0.62 & 1 & 0.62 \\ \cline{3-6}  & & Conductivity & - & 0.48 & 1 & 0.48 \\ \cline{3-6}  & & Turbidity & TSHG6200 IR LED & 0.75 & 1 & 0.75 \\  & & BPW83 IR photodetector & 0.38 & 1 & 0.38 \\ \cline{3-6}  & & Oil layer & VLHW4100 White LED & 0.36 & 1 & 0.36 \\  & & BPW41N IR photodetector & 0.52 & 1 & 0.52 \\ \cline{3-6}  & & Water level & GP2Y0A02 YK0F level sensor & 15.23 & 1 & 15.23 \\ \cline{3-6}  & & Workers presence & TSHG6200 IR LED & 0.76 & 1 & 0.76 \\  & & BPW83 IR photodetector & 0.38 & 1 & 0.38 \\ \cline{3-6}  & & Light & NORPS-12 LDR & 1.76 & 1 & 1.76 \\ \cline{3-6}  & & Fish presence & NSL 19M51 LDR & 0.55 & 30 & 16.5 \\ \cline{3-6}  & & Feed falling & Jtron OV7670 300 KF VGA & 5.35 & 1 & 5.35 \\ \cline{3-6}  & & Humidity & HCZ-D5-A Humidity sensor & 1.45 & 3 & 4.35 \\ \hline \multirow{8}{*}{Node} & \multirow{8}{*}{Memory system} & Node compatible with Arduino & 6.7 & 3 & 20.1 \\  & & Micro SD Card reader & 2.19 & 3 & 6.57 \\ \cline{1-1}  & & Micro SD Card 4 Gb & 0.82 & 3 & 2.46 \\ \cline{1-1} \cline{2-6}  & Transmission system & ESP8266 WiFi module & 1.58 & 3 & 4.74 \\ \cline{1-1} \cline{2-6}  & & CD74 HC4067 16-Channel MUX & 0.45 & 2 & 0.90 \\ \cline{1-1} \cline{2-6}  & Multiplexors & 74 LVC1G3157 Single-Pole & 0.45 & 1 & 0.45 \\ \cline{1-1} \cline{2-6}  & & Other & Resistances & 0.5 & 10 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cost of the developed system for each tank. velocity. With the feed falling sensor, tests in real scenarios were done to gather images of the water drainage system with and without feed. The results show that with the blue band of the picture it is possible to distinguish the presence of feed with \(\sum\)RI. Regarding future work, we will estimate the data network requirements such as the number of APs needed in the production rooms. Moreover, we will study the way to send the alarm messages to the workers directly. In terms of sensors, we expect to create a low-cost dissolved oxygen sensor. In addition, with the implantation of this system, it will be possible to apply data mining techniques and artificial intelligence to predict the feed needed in each tank.