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AeroWatch
AI Air Quality Station based on ESP32-S3
Distributed air quality sensor node that identifies pollution sources using on-device pattern recognition.
PM Detection
0.3-10µm
Placement Density
1 per 200m²
AeroWatch
Client
An environmental monitoring company deploying hyperlocal air quality sensor networks for urban areas, industrial parks, and schools.
Challenge
Building a low-cost air quality sensor node that can distinguish pollution sources (traffic vs. construction vs. cooking) using only consumer-grade gas sensors.
Product Concept & Architecture
The ESP32-S3 with its vector extensions runs both sensor management and on-device ML inference. Sensors include SPS30 (PM1/2.5/4/10), SGP40 (VOC index), SCD41 (CO2), and BME280 (T/H/P). Nodes communicate via ESP-WiFi-Mesh.
Engineering Solution
Sensor fusion on the ESP32-S3's dual cores: Core 0 reads all sensors at 1Hz and applies temperature/humidity compensation curves. Core 1 runs a TinyML random forest classifier (100KB) that identifies pollution type every 5 minutes. Nodes synchronize via Wi-Fi mesh and elect a gateway node for cloud uplink.
Result
87% pollution source classification accuracy. Per-node BOM under $60. 500-node mesh network deployed.
Deep Tech FAQ
What makes the AI Air Quality Station based on ESP32-S3 unique?+
The AI Air Quality Station based on ESP32-S3 leverages custom hardware and edge AI to deliver industry-leading performance and efficiency.
What are the core technical benefits?+
Key benefits include low power consumption, real-time processing, robust connectivity, and a compact form factor tailored for deployment.
Can this architecture be adapted for other use cases?+
Absolutely. The underlying methodology is modular and can be retargeted for various applications with minimal redesign.
Project Profile
Services Delivered
Hardware DesignEmbedded Software
Core Technologies
Wi-Fi/BluetoothTinyMLRTOS
Silicon & Vendors
EspressifSensirion
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