BSc IT Project Guide: IoT Data Management on Cloud
1. Introduction
The IoT Data Management on Cloud project aims to develop a system to collect, manage, and analyze data from IoT devices using cloud-based platforms. This enables scalable, real-time processing and visualization of sensor data for monitoring and decision-making.
2. Objective
- Develop a cloud-based solution to gather and store IoT
data.
- Implement tools for real-time data analysis and visualization.
- Ensure secure and scalable data transmission and storage.
- Provide meaningful insights from IoT data for users or administrators.
3. Tools and Technologies
- Programming Language: Python, JavaScript
- Cloud Platforms: AWS (IoT Core, Lambda, DynamoDB), Google Cloud, or Azure
- Frontend: React or Angular
- Backend: Node.js or Flask
- Database: DynamoDB, Firebase, or MongoDB Atlas
- Visualization: Grafana, Chart.js, or D3.js
4. System Architecture
The architecture includes IoT devices sending data to cloud-based endpoints, where data is processed using serverless functions or backend APIs. Data is stored in cloud databases and visualized on dashboards accessible via web interfaces.
5. Modules
- Device Communication Module
- Data Ingestion and Storage Module
- Real-time Processing and Alert System
- Visualization and Dashboard Module
- User Authentication and Access Control
6. Implementation Steps
1. Set up IoT device emulators or real devices to collect
data.
2. Connect devices to the cloud platform and configure data ingestion
pipelines.
3. Store incoming data in a scalable cloud database.
4. Develop dashboards to display data insights.
5. Implement access control and security measures.
6. Test and deploy the application.
7. Future Scope
This system can be extended to support advanced analytics using AI/ML models, integration with third-party APIs, and additional security compliance for enterprise applications.