Journal of Engineering Advancements Vol. 03(04) 2022, pp 155-161 https://doi.org/10.38032/jea.2022.04.003 *Corresponding Author Email Address: skt047@gmail.com Published by: SciEn Publishing Group Design of a Smart Biofloc Monitoring and Controlling System using IoT Rumana Tasnim 1 , Abu Salman Shaikat 1,* , Abdullah al Amin 1 , Molla Rashied Hussein 2 , Md Mizanur Rahman 1 1 Department of Mechatronics Engineering, World University of Bangladesh (WUB), Dhaka-1205, Bangladesh 2 Department of Computer Science & Engineering, University of Asia Pacific (UAP), Dhaka-1205, Bangladesh Received: September 20, 2022, Revised: December 12, 2022, Accepted: December 14, 2022, Available Online: December 26, 2022 ABSTRACT In this paper, an IoT based real-time monitoring and controlling system have been designed and developed for an eco-friendly aquaculture system namely a biofloc fish farm. Currently, technology has a vital role in improving aquaculture production which leads to attaining sustainable development. The microorganisms in the biofloc fish tank are utilized for detoxifying the toxic waste materials by recycling as well as transforming them into fish food e.g. protein cells. Hence, it not only manages good water quality in the biofloc system but also produces additional feed for the fish. Water quality monitoring of biofloc fish tanks is a significant aspect to guarantee a better environment for producing fish. This paper focuses on developing an IoT based device for biofloc fish tanks to monitor various water quality parameters as well as control water temperature and air pump. Using this device, users can monitor the water quality data received from sensors and control the actuators accordingly from any remote location through the graphical user interface (GUI). Keywords: Aquaculture, Monitoring, Controlling, Biofloc, Turbidity, Temperature. This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License. 1 Introduction Biofloc aquaculture has been proliferating around the world in recent times. This system allows producing an increased amount of food from the smaller area of land with fewer inputs. In this system, the cost of fish feeds is decreased since the toxic waste nutrients are converted and consumed by fish later on. The use of aggregates of bacteria, algae, or protozoa enhances water quality, ensures waste treatment, and prevents disease in the aquaculture system. The traditional fish farm has a number of issues namely temperature instability, nutrition, water pollution, high maintenance charge, etc. In fish farming, biofloc technology transforms the conventional fish farming process into another infrastructure that utilizes the leftover food by converting it into bacterial biomass. Crab et al. [1] conducted a review of the advantageous effects of the biofloc process and addressed a number of challenges for further research. Hargreves et al. [2] referred to biofloc technology as a technique to enhance ecological control over production. Researchers and engineers designed a variety of monitoring and controlling systems for the fish farm over recent years. Phawa et al. [3] designed and developed an automated biofloc fish farming system. For measurement of the water temperature, a temperature sensor has been used. Arduino board is used for processing and controlling a heater that is placed in the tank. Furthermore, Mahanjan et al. [4] developed an e-monitoring system for the automated fish farming process. This system is integrated with IoT supported system for monitoring the fish farming process where PH sensors, Temperature sensors, TDS sensors, and Gas sensors were used. Noor et al. [5] developed a PIC microcontroller based automatic fish feeding system. The rotational speed of the DC motor controls the pellets in the automated fish feeding system. Another research by Tasin et al. [6] focussed on IoT based monitoring and assessing the quality of river water for saving the ecosystem. The researchers developed an Artificial Neural Network (ANN) based automated device with the help of IoT. Parra et al. [7] developed a wireless sensor network based approach to monitor both the fish behavior as well as water quality in an aquaculture tank using a low-cost sensor system. Their proposed system is capable of monitoring the status of the fish tank, the velocity and depth of feed falling and fish swimming along with the water quality parameters as well. A. Ramya et al. [8] developed another IoT based automated system that can not only monitor the fish farm but also assists the fish farm owners to maintain the fish feeding process. Shaari et al. [9] proposed an integrated system that combines both the automatic feed dispensing as well as distribution process. Kayalvizhi et. al. [10] proposed an automated device integrating sensors such as TDS sensor, pH sensor, Dissolved oxygen and Ultrasonic sensor, and controllers like Raspberry Pi and Arduino board. Chen et al. [11] conducted a thorough experiment on some water quality parameters namely pH, temperature, and dissolved oxygen developing an automated monitoring system that could work wirelessly via the Zigbee sensor network. Further research work by H Hendri et al. [12] demonstrated an automatic fish feeding approach where the turbidity level of water is tested and Arduino Mega was used as a controller. Research by Phillip G Lee et al. [13] focussed on developing bio-filters for monitoring water parameters. The authors made use of video cameras inside the fish tank to check the status and growth of the fish. Garcia-Pineda et al. [14] developed a cost effective automated fish feeding control system using a set of sensors. Bhakti et al. [15] highlighted analyzing water quality by measuring temperature, TDS, turbidity, and pH with various sensors and Raspberry Pi controllers. Mozumder et al. [16] proposed a smart IoT based biofloc monitoring system that uses a number of sensors that collect, store, and analyze the sensed data by using the decision regression tree model for forecasting the water condition. Rashid et al. [17] presented an IoT based water quality prediction system using sensors and carried out a machine learning based analysis for tracing water quality and sending notifications to the user as well. Yang et al. [18] developed a smart prototype system for