Smart-Aquaculture-Monitoring-An-IoT-Solution.pptx

Aquaculture monitoring system project

1. Smart Aquaculture Monitoring: An IoT Solution A technical overview of an IoT-based system designed for real-time environmental monitoring and data-driven decision-making in aquaculture farms. Audience: Undergraduate Engineering Students & Project Supervisors | Tone: Technical and Concise

2. 1. Project Application: Real-Time Aquatic Health Monitoring Application Idea We propose an Internet of Things (IoT) system for continuous and automated monitoring of critical water quality parameters in fish ponds and aquaculture tanks. This system aims to ensure optimal conditions for aquatic life, reducing losses and increasing farm efficiency. Proposed Methodology Data Acquisition Employ submerged sensor nodes to collect metrics like pH, temperature, Dissolved Oxygen (DO), and turbidity every minute. Data Transmission Use a central gateway (e.g., Raspberry Pi) with Wi-Fi/GSM to securely transmit raw and aggregated data to a cloud platform. Analysis & Alerting Cloud platform processes data, generates trend analyses, and triggers SMS/email alerts upon threshold violations.

3. Project Objectives: Enhancing Aquaculture Sustainability Real-Time Monitoring To develop a robust system capable of continuous, accurate measurement of key water parameters (DO, pH, Temp). Proactive Alerting To implement a notification system that alerts farm personnel immediately when conditions drift outside optimal biological ranges. Data-Driven Insights To create a centralized dashboard for visualising historical data, enabling better management decisions regarding feeding and aeration. System Automation To demonstrate potential for basic actuation, such as automatically adjusting an aerator based on low Dissolved Oxygen levels.

4. 2. IoT Level Justification: Mapping the Architecture The proposed system integrates multiple architectural components, placing it across several IoT levels to achieve end-to-end functionality. Data Analytics/Decision Making Cloud Services Edge Processing Physical Devices

5. Justification by IoT Level 1 Level 1: Sensing & Actuation The project uses submerged sensors (pH, DO, Temp) for data collection and an actuator (aerator relay) for physical control. This is the foundational device layer. 2 Level 2: Data Aggregation The microcontroller (e.g., ESP32/Arduino) collects data from multiple sensors, performs local aggregation, and formats it for transmission. 3 Level 3: Gateway & Transmission A central gateway handles internet connectivity (Wi-Fi/GSM) and secure communication protocols (MQTT/HTTP) to the cloud.

6. Advanced IoT Levels in the System 1 Level 4: Data Processing & Storage Utilising a cloud platform (e.g., AWS IoT, Google Cloud) for database storage (NoSQL) and immediate stream processing of incoming sensor data. 2 Level 5: Application Layer The web dashboard/mobile application provides the user interface for monitoring metrics, viewing historical trends, and managing device settings. 3 Level 6: Decision Making (Basic) Rule-based engine on the cloud or locally handles basic automated decisions, such as triggering the aerator if DO drops below a predefined threshold (e.g., 5 mg/L).

7. 3. Hardware Component Requirements A breakdown of the essential physical components required for deploying the monitoring solution in an aquaculture environment. Sensors pH Probe, DS18B20 Temperature Sensor, Optical Dissolved Oxygen (DO) sensor, Turbidity Sensor. Controller ESP32 or ESP8266 Microcontroller with integrated Wi-Fi capability for processing and connectivity. Actuators Relay Module connected to a small air pump or aerator motor for maintaining DO levels. Communication External GSM Module (if Wi-Fi is unavailable) or integrated Wi-Fi/Bluetooth.

8. Software and Platform Requirements The digital infrastructure needed for data ingestion, processing, visualisation, and management. Programming C/C++ (for firmware development on ESP32) and Python (for cloud processing scripts and API development). IoT Platform ThingsBoard or Ubidots for device management, data ingestion, and rule engine implementation. Database Time-series database (e.g., InfluxDB) or NoSQL database (e.g., MongoDB) for efficient storage of high-frequency sensor data. Visualisation Web dashboard using React/Angular or platform-native dashboards for real-time monitoring and historical analysis.

9. Other Critical Requirements Power Supply Reliable, waterproof power source, possibly solar-powered with a backup battery for continuous operation in remote locations. Connectivity Stable Internet access (Wi-Fi or 4G/LTE) is crucial for uninterrupted data transmission to the cloud platform. Enclosure/Housing IP67/IP68 rated waterproof enclosures for all submerged sensors and the exposed microcontroller/gateway units.

10. Summary: A Complete IoT System for Aquaculture This project encompasses a full-stack IoT solution, moving from physical sensing to intelligent decision-making, offering a practical framework for sustainable and efficient aquaculture management. Hardware Sensors, Controller, Actuators Data Storage Cloud Database (NoSQL) Communication MQTT/HTTP Protocol Application Monitoring Dashboard Actuation Automated Aeration Control Deployment Robust Power & Housing The system is designed to provide immediate value through real-time feedback and long-term insights through data analytics.