🐟 Smart Aquarium Monitor

📌 Project Introduction

An intelligent fish tank monitoring system based on the MQTT protocol, which real-time monitors environmental parameters (such as temperature, water level, light intensity, dissolved oxygen, TDS, etc.) and stores data in an SQLite database. Real-time and historical data are visualized through a web interface.

✅ Core Features

Real-time Monitoring：Supports multi-parameter monitoring of temperature, water level, light intensity, dissolved oxygen, TDS, etc.
Data Transmission：Implements communication between devices and the database via the MQTT protocol.
Data Storage：Stores historical data in an SQLite database.
Visual Interface：Displays real-time data and historical trend charts through a web interface.
Modular Design：Independent publisher/subscriber scripts for easy expansion.

🛠️ Environment Requirements

Python：3.6 or higher
Dependent Libraries：Automatically installed via requirements.txt

[ziqi Qin]

▶️ Aquarium Sensor Data Monitoring System Operation Guide

I. Environment Preparation (Windows System)

Clone the Project (if obtained from repository)

git clone <https://github.coventry.ac.uk/CUH405CMD2425FEBJUL/C2T8.git>  
cd C2T8

Create and Activate Virtual Environment

python -m venv venv  
venv\\Scripts\\activate  # Windows activation command

Install Dependencies

pip install -r requirements.txt  # If requirements.txt exists  
pip install flask flask-sqlalchemy paho-mqtt requests  # Basic dependencies

II. Project Initialization

Initialize Database (first-time run)
```
python -m src.db_ops.init_db  
```
✅ A aquarium.db file will be generated in the instance/ directory upon success.

(Optional) Database Cleanup Task

python src/db_ops/cleanup.py  # Periodically clean old data

III. Service Startup Flow (requires multiple terminal windows)

1. Start Backend API Service (core data processing)

python -m src.query_db

Function: Listens to MQTT data and provides API interfaces (port 8080).
Verification: Access http://127.0.0.1:8080 to display a welcome message.

2. Start MQTT Bridge Service (data forwarding)

python -m src.mqtt_bridge

Function: Connects to the MQTT broker and forwards sensor data to the backend.

3. Start Sensor Simulators (take light as an example, others are similar)

# Light sensor data publishing  
python -m src.light_publisher

Commands for other sensors:

# Temperature  
python -m src.temperature_publisher  
python -m src.temperature_subscriber  
# Water level  
python -m src.WaterLevel_Publisher  
python -m src.WaterLevel_Subscriber  
# Dissolved oxygen  
python -m src.OxygenLevel_publisher  
python -m src.OxygenLevel_subscriber  
# TDS  
python -m src.tds_pub_impro  
python -m src.tds_subscriber

4. Start Frontend Web Application (data visualization)

python -m src.db_ops.app

Function: Provides a frontend interface (port 8080).
Access: Open a browser and enter http://127.0.0.1:8080.

IV. Data Verification and Query

Frontend Interface Verification
- Access http://127.0.0.1:8080 and select the sensor type to view real-time data charts.

API Interface Query (access URL directly)

# Light data  
<http://127.0.0.1:8080/query_history?sensor_type=light>  
# Temperature data  
<http://127.0.0.1:8080/query_history?sensor_type=temperature>  
# TDS data  
<http://127.0.0.1:8080/query_history?sensor_type=tds>

Query with time range:

<http://127.0.0.1:8080/query_history?sensor_type=light&start_time=2025-06-01&end_time=2025-06-30>

Direct Database Query (for debugging)

sqlite3 instance/aquarium.db  
SELECT * FROM light;  # Light table  
SELECT * FROM tds;    # TDS table  
.quit  # Exit the database

V. Common Issue Solutions

Database not generated
- Ensure python -m src.db_ops.init_db is executed, and check if paths in src/db_ops/init_db.py are correct.
Frontend shows TemplateNotFound
- Confirm to start the frontend from the project root directory: python -m src.db_ops.app, and check if templates/index.html exists.
MQTT connection timeout
- Modify src/mqtt_bridge.py or sensor scripts to use a public MQTT broker (e.g., broker.emqx.io):
```
client.connect("broker.emqx.io", 1883, 60)  
```
Data not displayed
- Check if subscriber scripts (e.g., light_subscriber.py) correctly parse messages and insert into the database. Add log debugging:
```
print(f"Received data: {payload}")  # Add in on_message  
```

VI. Service Operation Flow Summary

Start the backend API service (query_db.py) first.
Then start the MQTT bridge service (mqtt_bridge.py).
Start sensor simulators (as needed).
Finally, start the frontend application (db_ops.app).

Follow this order to ensure smooth data flow, and the frontend will display sensor data in real time.

📂 Project Structure

projectRoot/
├── src/                  
│   ├── db_ops/          # Database operations
│   │   ├── __init__.py  
│   │   ├── app.py       # Flask application
│   │   ├── database.py  # Database initialization
│   │   └── models.py    # Data models
│   ├── publishers/      # MQTT data publishers
│   ├── subscribers/     # MQTT data subscribers
│   ├── query_db.py      # Query data from the database
│   └── mqtt_bridge.py   # MQTT bridge
├── instance/            # Database storage
│   └── aquarium.db      
├── templates/           # Frontend templates
│   └── index.html       
├── requirements.txt     # Dependencies
├── init.py     
└── README.md            # Project documentation

📬 Contact Information

For questions or suggestions, please submit via GitHub Issues.

✨ Project Highlights

Lightweight Deployment：Requires only Python and SQLite, no complex configuration needed.
Scalability：Supports adding more sensor types for extended monitoring.
Visualization Support：Directly view trend charts through the web interface.

CUH405CMD2425FEBJUL/C2T8

About

Resources

Stars

Watchers

Forks

Releases

Contributors 3