Himanshu Tyagi,
Lovink Tyagi,
Ayush Singh,
Anjali,
Arpna Saxena,
- Research Scholar, Department of Master of Computer Applications, Ajay Kumar Garg Engineering College, Uttar Pradesh, India
- Research Scholar, Department of Master of Computer Applications, Ajay Kumar Garg Engineering College, Uttar Pradesh, India
- Research Scholar, Department of Master of Computer Applications, Ajay Kumar Garg Engineering College, Uttar Pradesh, India
- Research Scholar, Department of Master of Computer Applications, Ajay Kumar Garg Engineering College, Uttar Pradesh, India
- Assistant Professor, Department of Master of Computer Applications, Ajay Kumar Garg Engineering College, Uttar Pradesh, India
Abstract
To manage aquaculture effectively, we need to monitor water quality constantly. This helps prevent the depletion of dissolved oxygen, which can lead to fish suffocating. Such monitoring is especially critical in intensive pond systems. If we ignore this and fail to take action quickly, we risk losing all our fish, resulting in high costs that could have been avoided. This paper introduces a low-cost Internet of Things (IoT) based aerator monitoring and management system designed for real-time water quality management in small-scale and rural fisheries. The system uses a NodeMCU ESP8266 microcontroller integrated with sensors for temperature, pH, dissolved oxygen, turbidity, and water level to collect continuous data. The sensor data is sent wirelessly to a cloud platform, where users can visualize it in real time. Alerts can be generated to signal any discrepancies between expected and observed values, and historical analysis can also be conducted. An automated aeration mechanism activates when dissolved oxygen levels fall below preset limits, ensuring stable oxygen availability while minimizing unnecessary energy use. The sensors performed well during testing in freshwater, providing stable wireless communication and measurements that matched laboratory results. This approach streamlines the monitoring of significant changes in water quality, leading to more effective responses. Furthermore, it is affordable, easily expandable, and energy-efficient, making it a smart and eco-friendly option for aquaculture management.
Keywords: IoT, aquaculture, water quality monitoring, dissolved oxygen, automated aeration, NodeMCU ESP8266
[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]
References
- Ya’acob N, Dzulkefli NNSN, Yusof AL, Kassim M, Naim NF, Aris SSM. Water quality monitoring system for fisheries using Internet of Things (IoT). IOP Conf Ser Mater Sci Eng. 2021;1176:012016. doi:10.1088/1757-899X/1176/1/012016.
- Akhter F, Siddiquei HR, Alahi MEE, Mukhopadhyay SC. Recent advancement of the sensors for monitoring water quality parameters in smart fisheries farming. Computers. 2021;10(3):26. doi:10.3390/computers10030026.
- Manoj M, Dhilip Kumar V, Arif M, Bulai ER, Bulai P, Geman O. State of the art techniques for water quality monitoring systems for fish ponds using IoT and underwater sensors: a review. Sensors (Basel). 2022;22(6):2088. doi:10.3390/s22062088. PMID:35336256.
- Tsai KL, Chen LW, Yang LJ, Shiu HJ, Chen HW. IoT based smart aquaculture system with automatic aerating and water quality monitoring. J Internet Technol. 2022;23(1):177–184.
- Garcia M, Trambulo EM, Pajarillo J, Apsay MRB, Tenorio JE, Chua MG. Development of dissolved oxygen monitoring system for fish ponds. In: IEEE 3rd International Conference on System Engineering and Technology; 2013. p. 83–88. doi:10.1109/ICSEngT.2013.6650148.
- Chislock MF, Doster E, Zitomer RA, Wilson AE. Eutrophication: causes, consequences, and controls in aquatic ecosystems. Nat Educ Knowl. 2013;4(4):10.
- Khan PW, Byun YC. Optimized dissolved oxygen prediction using genetic algorithm and bagging ensemble learning for smart fish farm. IEEE Sens J. 2023;23(13):15153–15164. doi:10.1109/JSEN.2023.3278719.
- Huang YP, Khabusi SP. Artificial intelligence of things (AIoT) advances in aquaculture: a review. Processes. 2025;13(1):73. doi:10.3390/pr13010073.
- Li W, Wu H, Zhu N, Jiang Y, Tan J, Guo Y. Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Inf Process Agric. 2021;8(1):185–193. doi:10.1016/j.inpa.2020.02.002.
- Enehizena ON, Ogbeide KO, Farhaoui Y, Imoize AL. ZigBee wireless sensor network design for environmental monitoring. In: The International Conference on Artificial Intelligence and Smart Environment. Cham: Springer Nature Switzerland; 2024. p. 498–505.
- Lambrou TP, Anastasiou CC, Panayiotou CG, Polycarpou MM. A low-cost sensor network for real-time monitoring and contamination detection in drinking water distribution systems. IEEE Sens J. 2014;14(8):2765–2772. doi:10.1109/JSEN.2014.2316414.
- Sheela MS, Chand SR, Gopalakrishnan S, Choubey SB, Gopianand M, Hephzipah JJ. Empowering aquarists: a comprehensive study on IoT-enabled smart aquarium systems for remote monitoring and control. Babylon J Internet Things. 2024;2024:33–43. doi:10.58496/BJIoT/2024/005.
- Abinaya T, Ishwarya J, Maheswari M. A novel methodology for monitoring and controlling of water quality in aquaculture using Internet of Things (IoT). 2019 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India. 2019. p. 1–4. doi:10.1109/ICCCI.2019.8821988.
Research & Reviews: A Journal of Embedded System & Applications
| Volume | 14 | |
| Issue | 02 | |
| Received | 04/07/2026 | |
| Accepted | 31/08/2026 | |
| Published | 20/09/2026 | |
| Publication Time | 78 Days |