IoT-Based Water Quality Monitoring System: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop an IoT-based solution for real-time monitoring of water quality parameters to ensure safe and clean water supply.
Scope of the system: Applicable for water treatment plants, municipal corporations, industries, and environmental monitoring agencies.
2. Requirements Analysis
Functional Requirements:
· - Monitor parameters such as pH, temperature, turbidity, and dissolved oxygen.
· - Transmit real-time data to a central server or cloud platform.
· - Generate alerts for any deviations from standard water quality levels.
· - Provide a dashboard for data visualization and reporting.
Non-Functional Requirements:
· - Scalability to cover multiple water sources.
· - Reliability for uninterrupted monitoring and data transmission.
· - Secure data handling and communication.
3. System Design
Architecture:
· - IoT-based architecture with sensors, microcontrollers, and cloud integration.
· - Use of wireless communication protocols like Wi-Fi, LoRa, or Zigbee.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of sensor data collection, processing, and alert generation.
· - Level 1: Detailed processes for sensor readings, data transmission, and dashboard updates.
Database Design:
· - Tables: Sensor Readings, Alerts, Quality Standards, Historical Data.
4. Technology Stack
Hardware:
· - Sensors: pH sensor, turbidity sensor, temperature sensor, dissolved oxygen sensor.
· - Microcontrollers: Arduino, Raspberry Pi, or ESP32 for data acquisition and transmission.
Software:
· - IoT platforms: ThingSpeak, Blynk, or AWS IoT Core for data management.
· - Backend: Python (Flask/Django) or Node.js for server-side processing.
Frontend:
· - Web dashboards using React, Angular, or Vue.js for visualization.
· - Mobile apps using Flutter or React Native for remote access.
5. Implementation
Sensor Integration:
· - Calibrate sensors for accurate readings.
· - Connect sensors to microcontrollers for data acquisition.
Data Transmission:
· - Use MQTT or HTTP protocols to send data to the cloud server.
· - Ensure efficient data compression to reduce bandwidth usage.
Data Analysis and Alerts:
· - Analyze sensor data against predefined thresholds.
· - Generate alerts via SMS, email, or mobile app notifications for anomalies.
Dashboard Development:
· - Design user-friendly interfaces for real-time monitoring and historical analysis.
· - Include features like graph plotting, trend analysis, and export options.
6. Security
Encrypt data during transmission using SSL/TLS.
Implement secure authentication for accessing the dashboard and APIs.
Ensure data integrity by verifying sensor readings.
7. Testing
Unit Testing: Validate individual modules like sensor readings and data transmission.
Integration Testing: Ensure smooth interaction between sensors, microcontrollers, and cloud systems.
System Testing: Test the complete system under real-world conditions for reliability and accuracy.
Performance Testing: Evaluate the system's ability to handle high data volumes and multiple sensors.
8. Deployment
Deploy sensors and microcontrollers at water sources.
Host the software platform on a reliable cloud server.
Provide user documentation and training for stakeholders.
9. Maintenance and Updates
Regularly calibrate sensors for consistent accuracy.
Monitor system logs to identify and resolve issues promptly.
Update the system with advanced algorithms and additional features.
10. Appendix
Glossary of terms.
References and additional resources.