Indoor Air Quality Monitor - Case Study
Project Context
The project was initiated as a Proof-of-Concept (PoC) to develop a portable air quality monitor using the ESP32-C6 platform. The primary goal was to validate a low-power architecture capable of continuous indoor air quality tracking and air quality monitoring within home and classroom air quality settings. To maintain operational uptime and track air quality indoors, the device utilizes integrated power-saving modes and Non-Volatile Storage (NVS) for local air quality data visualization logging.
Data transmission is optimized by batching real-time air quality readings to AWS IoT Core, reducing radio uptime and power consumption. The device integrates an E-Ink display for an offline interface to check the current air quality, with Wi-Fi provisioning handled via BLE and initial support for multi-network memory to prevent air quality issues.
Project Context
The project was initiated as a Proof-of-Concept (PoC) to develop a portable air quality monitor using the ESP32-C6 platform. The primary goal was to validate a low-power architecture capable of continuous indoor air quality tracking and air quality monitoring within home and classroom air quality settings. To maintain operational uptime and track air quality indoors, the device utilizes integrated power-saving modes and Non-Volatile Storage (NVS) for local air quality data visualization logging.
Data transmission is optimized by batching real-time air quality readings to AWS IoT Core, reducing radio uptime and power consumption. The device integrates an E-Ink display for an offline interface to check the current air quality, with Wi-Fi provisioning handled via BLE and initial support for multi-network memory to prevent air quality issues.
Hardware Stack
Firmware Stack
Solution Overview
The project focused on the end-to-end prototype development of a portable, ESP32-C6-based smart air quality monitor. The main challenge was establishing a stable data pipeline to the cloud while maintaining a modular firmware architecture that supports both offline interaction and modern smart home standards.
The PoC version involved the development of firmware that utilizes BLE for provisioning and Wi-Fi for AWS IoT Core integration. We implemented data batching logic to optimize radio uptime and utilized NVS for local data storage to ensure accurate air quality records. To meet requirements for OTA updates and air quality guidelines, we implemented dual-channel OTA via AWS HTTP and local BLE, allowing for recovery during connectivity issues. Additionally, we developed cloud infrastructure to handle device shadowing, secure certificate exchange, and complex data aggregation for daily, weekly, and monthly reporting.
What We Achieved
Through the prototype development process, we successfully validated the core system architecture for the complete air quality monitor. We developed a reliable data pipeline from the ESP32-C6 to the AWS IoT Core backend, ensuring stable sensor data ingestion, allowing users to visualize air quality via remote configuration through IoT Shadows. We ensured data integrity by utilizing NVS storage to buffer air quality readings during Wi-Fi outages, allowing the device to automatically sync missing data once the connection is restored.
We proved the system’s long-term serviceability by successfully testing a dual-channel OTA mechanism for both remote cloud updates and local recovery via BLE. Finally, the prototype was delivered within the planned technical constraints, providing a clean firmware interface for the E-Ink display and rotary encoder that is already architected to support air quality improvement and smart home integrations like Home Assistant, Matter, or ESPHome.
Project Duration Estimate
| Stage | Min h | Max h |
|---|---|---|
|
Initial project setup and hardware abstraction layer validation. |
30 |
60 |
|
Basic functionality: local storage, time sync, and reset logic. |
56 |
92 |
|
Sensor integration for air quality and battery monitoring. |
48 |
104 |
|
E-Ink display interface development and rotary encoder navigation. |
42 |
88 |
|
Provisioning server setup and custom BLE API. |
60 |
99 |
|
WiFi, reconnection logic, and Mesh/AP transition handling. |
42 |
72 |
|
Cloud communication, remote settings, and secure device onboarding. |
162 |
248 |
|
Implementation of dual-path firmware updates (AWS HTTP and BLE). |
40 |
64 |
|
System logic for data sampling, batching, and power management. |
86 |
114 |
|
Cloud infrastructure: user authentication and data processing. |
82 |
128 |
|
Customer and space management APIs with historical data aggregation |
130 |
225 |
|
Project management |
60 |
124 |
|
Total estimated time |
838 |
1418 |
Note: To the final total project estimated time might be added up to 15% of time for risk assessment.
