Smart Device Simulation
11 virtual IoT devices across 3 rooms with realistic circadian patterns, Gaussian noise, and rare event simulation. Temperature, motion, energy, humidity, and light.
Simulate IoT devices, process real-time data streams, run AI inference on CPU, make autonomous decisions, and control actuators — all locally, all free, all open-source.
A complete edge intelligence platform — from simulated sensors to autonomous decisions — with zero external dependencies.
11 virtual IoT devices across 3 rooms with realistic circadian patterns, Gaussian noise, and rare event simulation. Temperature, motion, energy, humidity, and light.
Data Agent validates and stores. Decision Agent evaluates strategies. Action Agent executes commands. Full message bus with performance tracking.
Three statistical methods — Z-Score, IQR, and Gradient — vote on anomalies. Requires 2+ methods to agree, reducing false positives by ~70%.
Linear regression, Simple Moving Average, and Exponential Moving Average forecasting. Multi-step predictions with confidence scores. CPU only.
React 18 with WebSocket-powered live charts, 5 navigable pages, agent pipeline visualization, and a polished dark theme design system.
20+ endpoints with Pydantic validation, auto-generated Swagger docs, CSV/JSON data export, device predictions, and bidirectional WebSocket.
Configurable threshold triggers with hysteresis to prevent actuator flapping. No-motion timeout automatically turns off unused lights.
Add custom decision strategies with 10 lines of Python. Just implement evaluate() and name — the engine handles the rest.
C++ Arduino firmware for real DHT11 temperature and PIR motion sensors. LED and buzzer actuator control. WiFi + MQTT.
An event-driven pipeline that never leaves your machine. Every component is modular, testable, and replaceable.
11 devices, 3 rooms, MQTT stream
Mosquitto topic routing
Validate + store in PostgreSQL
Rules + anomaly + prediction
Alerts + MQTT commands
Lights, fans, alarms
Live WebSocket charts
Three complete demos run automatically when you start the system. Watch AI make decisions in real-time.
Intelligent room control that adapts to occupancy and environment conditions without any human input.
Statistical monitoring that catches equipment anomalies before they become critical failures.
Continuous consumption tracking with time-of-day awareness, spike detection, and trend forecasting.
Every dependency is free, open-source, and battle-tested in production.
Everything you need to know about EdgeBrain.
git clone https://github.com/rudra496/EdgeBrain.git, cd EdgeBrain, docker compose up --build -d. Open http://localhost:3000 — that's it. Setup takes under 2 minutes.evaluate() and name methods, then register it with the decision engine. Custom strategies are evaluated alongside built-in ones and can trigger alerts and actuator commands.Unlike IoT cloud platforms, EdgeBrain runs entirely on your machine. Your data never leaves your laptop.
| Feature | EdgeBrain | Typical IoT Cloud |
|---|---|---|
| Cost | $0 forever | $10–500+/mo |
| Data Privacy | 100% local | Cloud-stored |
| Setup Time | ~2 minutes | Hours to days |
| AI Inference | Built-in (CPU) | Varies / extra cost |
| Custom Strategies | Python plugin system | Limited / locked |
| Hardware Support | ESP32 included | Vendor-specific |
| Internet Required | No | Always |
| Open Source | MIT License | Proprietary |
| Latency | < 5ms (local) | 100–500ms (cloud) |
One command. Zero cost. Full control.