An IoT-Enabled Smart Aquarium System for Real-Time Water Quality Monitoring and Automated Feeding

2026-01-13

AI Summary

This study addresses the limitations of conventional aquariums, which rely on manual water quality monitoring and struggle to maintain stable aquatic environments. The authors propose and implement an IoT-enabled smart aquarium system based on the ESP32 microcontroller, integrating multi-parameter sensing (pH, TDS, temperature, and turbidity) with actuation modules for real-time water quality monitoring, automated feeding, and water exchange. The system leverages the Blynk platform for cloud-based alerts and remote control. Innovatively incorporating edge computing and a configurable alert cooldown mechanism, it achieves a rapid 1.2-second response to anomalies while mitigating notification fatigue. Experimental results in a 10-liter tank demonstrate an average sensor accuracy of 96% and 97% reliability in automated functions, significantly reducing manual intervention and enhancing both practicality and system stability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Maintaining optimal water quality in aquariums is critical for aquatic health but remains challenging due to the need for continuous monitoring of multiple parameters. Traditional manual methods are inefficient, labor-intensive, and prone to human error, often leading to suboptimal aquatic conditions. This paper presents an IoT-based smart aquarium system that addresses these limitations by integrating an ESP32 microcontroller with multiple sensors (pH, TDS, temperature, turbidity) and actuators (servo feeder, water pump) for comprehensive real-time water quality monitoring and automated control. The system architecture incorporates edge processing capabilities, cloud connectivity via Blynk IoT platform, and an intelligent alert mechanism with configurable cooldown periods to prevent notification fatigue. Experimental evaluation in a 10-liter aquarium environment demonstrated the system's effectiveness, achieving 96\% average sensor accuracy and 1.2-second response time for anomaly detection. The automated feeding and water circulation modules maintained 97\% operational reliability throughout extended testing, significantly reducing manual intervention while ensuring stable aquatic conditions. This research demonstrates that cost-effective IoT solutions can revolutionize aquarium maintenance, making aquatic ecosystem management more accessible, reliable, and efficient for both residential and commercial applications.

Problem

• aquarium water quality
• real-time monitoring
• manual maintenance
• human error
• aquatic health

Innovation

• IoT-enabled smart aquarium
• real-time water quality monitoring
• edge processing
• automated feeding system
• intelligent alert mechanism