# Smart Low-Cost Control System for Fish Farm Facilities
Furthermore, the controlling and monitoring devices can be installed in habitual locations, such as in the structure of the cage, but also in other forms of encapsulations and transportation, such as buoys [14] and unmanned floating vehicles [15]. The benefits of these devices, however, are reduced or nonexistent without any form of communication that allows them to receive the information with the actions to be performed. The cost and liability of deploying cables in aquaculture facilities to connect monitoring devices led to the need to use wireless communications to create a Wireless Sensor Network (WSN) to send the collected information to a database and send alert messages to the personnel or activate actuators. In order for the sensing devices to establish any form of communication that enables collaboration, it is imperative to incorporate a communication module or use an embedded board that is manufactured with it. Various wireless communication technologies have been employed for control systems in aquaculture, including GSM/GPRS [16], 3G [17], 4G [13], WiFi [15; 18], ZigBee [18; 19], LoRa [6], and Bluetooth [15]. The selection of the communication technology depends on the distance between devices, the data's characteristics and quantity, or the power consumption requirements. Once the connection is established, the devices are part of a network that can be organized in various forms. Layered architectures, such as recirculation aquaculture traceability systems [20], are some of the most frequent forms of establishing hierarchical frameworks. Other proposals employ star topologies for wireless communications where sensing devices send data to a central hub [21]. Group-based topologies [22] allow establishing communication among devices according to factors including physical closeness, type of device, energy consumption, or processing capabilities. Lastly, some architectures enable the communication of devices deployed in a line or row by relaying the information from one device to the next until it reaches a gateway or data collector [23]. Finally, performing any kind of control activity to change certain variables in the fish tanks or cages requires the information gathered by the monitoring devices to be processed by algorithms. These algorithms determine the best action to take so as to obtain the desired result. Roulette-tournament selection algorithms have been applied to determine diet formulations [24]. Moreover, the prediction and control of Dissolved Oxygen (DO) have been performed by employing a hybrid improved sparrow search algorithm [25]. The use of Artificial Intelligence (AI) is extending to all possible areas of knowledge, including aquaculture. This has led to functionalities such as detecting malfunctioning devices for water quality monitoring and control based on the use of support vector machine classifiers [16]. Furthermore, pattern identification in fish behavior was achieved by the use of object detection and the k-means clustering algorithm [26]. Designing a control system for aquaculture facilities is therefore a complex task that requires the identification of the critical factors that need to be considered by the system. Moreover, the technical requirements needed to address these factors and the limitations introduced by the characteristics of the aquatic environment need to be considered as well regarding the sensors, circuitry, encapsulation, architecture, communication technology, or processing algorithms. Furthermore, with the use of low-cost devices, these new solutions for aquaculture can be more accessible to farmers worldwide. The remainder of this article is partitioned into the following sections. The second section identifies the factors affecting fish performance and fish well-being in aquaculture facilities and analyzes the interaction between factors, highlighting the critical ones.