Edge-AI Home Monitoring System
- 12 Devlogs
- 12 Total hours
Building a privacy-first, edge-AI home automation hub using Linux, Flask, and real-time computer vision to orchestrate smart devices.
LG TVs in Edge AI Home Monitoring System from now!!!!!!
Working on LG TV suppourt
From now Edge-AI Home Monitoring System suppourt tapo smart bulbs!!!!
Make a telegram bot for easily security and device control
Making a sequrity system using YOLO and Telegram
Ship #1
Make a better README.md file and finalize the setup proccess
Make better readme
Make a setup script
Make an app for easy use of AI and also ad functional control for the L900 tapo ledstrip
A new modern dashboard and more devices
Performance Optimization & Frontend Integration
After intensive testing, the communication system with Android TV devices has been fully upgraded. The latency observed in command execution has been eliminated, and the project now features its own Web Dashboard.
Key Achievements:
ADB Server Persistence: Resolved the critical issue regarding the continuous restart of the ADB server. By adopting the Registry Pattern, connections are now maintained (persistent), drastically reducing response time from 5+ seconds to near-zero (real-time).
Scalability: The backend has been developed to support the simultaneous control of multiple Android TVs. The system now dynamically manages connections via a central dictionary, allowing for the addition of unlimited devices through devices.json.
Frontend Dashboard: Developed a minimal and responsive interface (HTML/JS) that provides full device control. Communication with the server is now handled via the Fetch API with proper CORS and JSON payload handling.
Robust Error Handling: Upgraded the error reporting mechanism. The server now actively monitors module status and allows for automatic reset in case of connection restoration.
Next Steps:
Integration of real-time status feedback to monitor whether a TV is ON/OFF directly on the dashboard.
Further optimization of utils.py for more secure inter-module communication.
Establishing the Backend Infrastructure for Edge AI Home Monitoring System
Today, I successfully built the core communication layer for my Edge AI Home Monitoring System. I focused on creating a robust backend using Flask, enabling the system to act as a central hub for my home devices.
Key Achievements:
API Orchestration: Developed Flask endpoints (/api/light and /api/tv) to handle state management and device control requests efficiently.
Hardware Integration: Implemented an abstraction layer for AndroidTV control, allowing the server to issue commands like power toggles, volume adjustment, and channel navigation over the network.
Dynamic Configuration: Built a utility module to handle JSON-based device mapping, ensuring the system can scale easily by reading device IPs and types from a central devices.json configuration file.
Logging System: Integrated a custom logger to monitor command execution in real-time, which is essential for debugging my edge-computing setup.
Next Steps: Now that the server is successfully running and handling commands, the next phase is to add more devices and create the frontend dashboard.
