About This Architecture

Edge-cloud IoT roof control system combining ESP32-S3 local sensors (DHT22, BME280, rain sensor) with Firebase backend and BMKG weather API for intelligent rain prediction. Local sensor fusion triggers immediate safety responses (roof closure, buzzer alarm) when humidity exceeds 85% or rain is detected, while cloud-based Random Forest model processes regional weather data for proactive automation. This hybrid architecture minimizes latency-critical decisions at the edge while leveraging cloud ML for predictive accuracy, demonstrating best practices in IoT resilience and fail-safe design. Fork this diagram on Diagrams.so to customize sensor types, adjust prediction thresholds, or integrate alternative weather APIs and ML frameworks. The dual-layer decision logic—local rules for safety, cloud predictions for optimization—exemplifies production IoT patterns balancing responsiveness and intelligence.

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How do you design an IoT system that combines edge sensors with cloud ML for predictive automation while maintaining low-latency safety responses?

This diagram shows a dual-layer approach: ESP32-S3 edge device reads DHT22 and BME280 sensors, triggering immediate roof closure and alarms when local conditions exceed thresholds (humidity ≥85% or rain detected). Simultaneously, sensor data flows to Firebase, where a Random Forest model fuses local telemetry with BMKG regional weather data to predict rain probability ≥70%, sending proactive cloud