First Principles IoT Engineering
A comprehensive smart home automation platform built from the ground up, featuring custom hardware integrations, reverse-engineered RF protocols, and sophisticated automation workflows. This project demonstrates the same first-principles approach applied to professional AI systems.
Intercepted proprietary 433MHz RF signal using RTL-SDR software-defined radio, analyzed waveform patterns, reverse-engineered the protocol, then built a custom ESP32-based transmitter with CC1101 module integrated via ESPHome into Home Assistant. Required understanding of digital communication techniques, RF modulation, and embedded systems programming.
Flashed 10+ Sonoff devices with open-source Tasmota firmware, enabling local MQTT control, custom configurations, and freedom from manufacturer cloud services. Each device required careful GPIO mapping and MQTT topic configuration.
Implemented sophisticated humidity-based bathroom fan control using Node-RED. System monitors temperature and humidity sensors (Sonoff TH16), calculates running averages, and triggers exhaust fans when threshold exceeded. Prevents moisture buildup and mold growth automatically.
Deployed Zigbee mesh network using Sonoff USB dongle and Zigbee2MQTT for low-latency local control. Network includes Aqara motion sensors, door/window sensors, wireless switches, blinds, and air quality monitors connected without proprietary hubs.
Sensors → MQTT → Home Assistant → Node-RED → Actuators. All traffic stays local on dedicated IoT VLAN for security isolation.
Building Management Systems - Same architecture scales to commercial automation
Industrial IoT - MQTT patterns apply to factory sensor networks
Edge Computing - Local processing reduces latency and bandwidth requirements
Security Systems - Zigbee mesh provides reliable, tamper-resistant monitoring
This Home Assistant project exemplifies the same first-principles approach applied throughout my career. Rather than accepting black-box solutions, I reverse-engineered proprietary RF protocols, flashed custom firmware onto commercial hardware, and built integrations from scratch. These skills—understanding systems deeply before building on them—directly transfer to the AI systems I build today with the Personal AI Infrastructure and Risk-Agents platform.