From Wildfire Detection to Community Health Preparedness: An Edge-Enabled IOT Monitoring Framework

Abstract

Wildfire ignition can threaten forest resources and create smoke hazards for nearby communities. However, rapid detection of fire-like conditions is not equivalent to measurement of inhaled pollutants or clinical risk. This methodological case study critically reinterprets an author-supplied ESP32 NodeMCU prototype and develops an explicit pathway from environmental event detection to accountable health preparedness. The previously described prototype combines DHT11 temperature/humidity, an MQ-2 smoke-responsive channel, infrared flame sensing, an edge-hosted dashboard, a local buzzer and ThingSpeak logging. Its two-out-of-three rule uses heat, dryness and a smoke indicator. The precursor manuscript reports approximately 96% estimated decision accuracy in controlled tests, 2–4 s end-to-end detection, local alert activation within 1 s after crossing a threshold, and over 95% cloud-delivery consistency. Raw trial records, calibrated particulate measurements and health outcomes were unavailable for reanalysis. We derive the rule’s behaviour under independent and dependent sensor responses and identify critical differences between ignition alerts and exposure advisories. A proposed two-tier verification design integrates reference-calibrated PM2.5 monitoring, meteorology, spatial context and authorized public-health communication. The work provides an evidence-audited technical framework and prospective evaluation protocol, without claiming real-world exposure reduction or clinical benefit.