Automated Home Security System: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop a smart home security system that automates threat detection and alerting.
Scope of the system: Applicable for residential and small business setups to enhance safety and convenience.
2. Requirements Analysis
Functional Requirements:
· - Real-time monitoring using cameras and sensors.
· - Motion detection and face recognition capabilities.
· - Mobile notifications for security breaches.
· - Remote control for locking/unlocking doors.
· - Emergency alert system for fire, gas leaks, or intrusion.
Non-Functional Requirements:
· - High reliability and responsiveness.
· - Secure data handling and storage.
· - User-friendly interface.
3. System Design
Architecture:
· - IoT-based system with interconnected cameras, sensors, and a central hub.
· - Mobile or web application for remote monitoring and control.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of data flow from sensors and cameras to user dashboards.
· - Level 1: Detailed processes for detection, alert generation, and remote control.
Database Design:
· - Tables: Users, Events, Alerts, Device Status.
4. Technology Stack
Hardware:
· - Microcontroller (e.g., Arduino, Raspberry Pi) for controlling sensors and cameras.
· - Sensors for motion detection, smoke, and gas.
· - Cameras with night vision and face recognition capabilities.
Frontend:
· - Mobile or web app using React Native, Flutter, or Angular.
Backend:
· - Python (Flask/Django) or Node.js for API development.
· - Integration with cloud platforms for data storage and real-time processing.
Database:
· - SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase).
Cloud Services:
· - AWS IoT, Google Cloud IoT, or Azure IoT for hosting and connectivity.
5. Implementation
Device Integration:
· - Connect sensors and cameras to the central hub using protocols like MQTT or Zigbee.
· - Calibrate devices for accurate readings and performance.
Threat Detection:
· - Implement algorithms for motion detection and face recognition.
· - Use AI models for analyzing camera feeds and sensor data.
User Interface:
· - Provide dashboards for real-time monitoring and historical event logs.
· - Enable remote control for security devices like locks and alarms.
Alerts and Notifications:
· - Set up mobile notifications for detected threats or emergencies.
· - Allow users to configure alert preferences.
6. Security
Encrypt data during storage and transmission.
Implement secure authentication and access control.
Comply with data privacy laws and best practices.
7. Testing
Unit Testing: Validate individual modules like motion detection and face recognition.
Integration Testing: Ensure seamless communication between devices, backend, and frontend.
System Testing: Test the system for accuracy and reliability in real-world scenarios.
Performance Testing: Evaluate system scalability and response time under high usage.
8. Deployment
Deploy the system on cloud platforms for scalability and reliability.
Provide installation guides and user manuals for hardware setup.
Set up monitoring tools for system performance tracking.
9. Maintenance and Updates
Monitor system logs and address issues promptly.
Regularly update the application and firmware for enhanced functionality and security.
Incorporate user feedback to improve user experience.
10. Appendix
Glossary of terms.
References and additional resources.