A Robust Internet of Things-Based Aquarium Control System Using Decision Tree Regression Algorithm

Abstract

The development of the Internet of Things (IoT) has shown significant contributions to many application areas, such as smart cities, smart homes, and smart farming, including aquarium control systems. Important things in an aquarium system are the level of ammonia in the water and the temperature of the water. Other research proposes several systems to make the aquarium control system robust for the aquarium monitoring and control system. However, those systems have weaknesses; namely, the user must actively access information to the server. This paper proposes a robust aquarium control system using the decision tree regression (DTR) algorithm. The development of this system was to overcome the problem of aquarium control by remote users. An accurate and real-time system is needed to monitor the aquarium so that it does not reach dangerous and critical points, such as in the case of an increase in water temperature. We did tests by developing an aquarium system connected to a server and an application that acts as a controller. Our measurements check the delay of sending data from the sensor to the server, process delay, actuator delay, user delay, and delay in reaching the aquarium’s critical point. The measurement of the system’s robustness is by calculating the probability of the information arrival to the user in the period of the critical point compared to the time needed to reach the critical point. Furthermore, we also made an analytical model based on the probability density function of the delay covered in this system. Analytically and experimentally, we show that the system can meet the needs of aquarium monitoring and control in an IoT-based environment.

Keywords

• Internet of Things
• analytical model
• probability density function
• robustness
• aquaculture
• decision tree regression