Tutorialedge aiwbgtesp32google gemini
ReLeaf Deploys Edge AI Cooling Network
7.0
The ReLeaf project combines real-time sensing, edge computing, and generative AI to provide instant targeted mist cooling in urban public spaces. It uses DHT11 sensors and ESP32 hardware feeding an edge Decision Tree model to compute WBGT, trigger relay-controlled misting, publish MQTT telemetry, and generate human-readable advisories via Google Gemini.
Key Points
- 1Combines DHT11 and Open-Meteo sensing to compute WBGT at edge using Decision Tree
- 2Triggers ESP32-controlled misting when WBGT exceeds danger thresholds, preventing rapid heat stress
- 3Enables cities to deploy low-power, actionable cooling with MQTT dashboard and Gemini-generated health alerts
Scoring Rationale
Provides practical, deployable edge-AI cooling design with step-by-step implementation but limited novelty and single-project validation.
Sources
Public references used for this report.
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