Aquarium Monitoring System Based on Internet of Things

Abstract

With the ever-increasing richness of social resources, the number of devices using the Internet of Things is also increasing. Currently, many people keep pets such as fish in their homes, and they need to be carefully taken care of. In particular, it is necessary to create a safe and comfortable environment for them and to maintain this environment continuously. An adverse environment can affect the growth of fish and may even result in their death. This study used the LinkIt 7697 module and the BlocklyDuino editor to produce a control system for a smart aquarium. The purpose of this system is to monitor the temperature, light intensity, and water level in an aquarium, as well as to provide alerts to presence of intruders; therefore, temperature, light, ultrasonic, and infrared sensing modules are used. The system has set aquarium environment thresholds, and it processes the signals obtained by the sensors to control and optimize the outputs to loads using data fusion calculations so that the aquarium has the most comfortable environment for the fish. An automatic feeder is also included in the system, and this uses a servo motor. The data from the system is uploaded to a back-end computer through the built-in Wi-Fi system of the LinkIt 7697 module. The Cloud Sandbox platform is used to display the results in real time, achieving the purpose of remote network monitoring.

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

“…The aquaculture monitoring system in previous studies [26,27] was built with a sensor device that detects the condition of the fish pond with various parameters: temperature condition, pH level, and turbidity level. Data are read through sensors, namely the temperature sensor (type RTD PT100), pH sensor (SEN 0161), and turbidity sensor (SEN 0189), and then sent to the microcontroller connected to the Wi-Fi module.…”