Developing a Custom ESP8266-Based Environment Sensor

Conceptualizing the Sensor

The idea was simple: build a compact, reliable, and customizable sensor that could collect environmental data in real-time. The ESP8266 microcontroller was the obvious choice for this project due to its affordability, versatility, and built-in WiFi capabilities. However, the ESP8266 couldn’t connect to the university’s eduroam network, so I had to rely on local data storage using a micro SD card.

The sensor needed to measure three key parameters: sound levels, light intensity, and nearby devices (as a proxy for occupancy). Each of these measurements required careful hardware selection and software configuration.

Hardware Design

The hardware setup was built around the NodeMCU ESP8266 12.0, which served as the central processing unit. Here’s a breakdown of the components I used:

• Sound Sensor: MAX9814 - Measures sound pressure levels, which can be converted into decibels. Calibration was required to ensure accurate readings.
• Light Sensor: BH1750FVI - Measures ambient light intensity using I2C communication. A light ball diffuser was added for consistent readings.
• Device Counting: The ESP8266 was put into promiscuous mode to detect WiFi probe requests and estimate occupancy levels.
• Micro SD Card Adapter: Used for local data storage, with encrypted files for security.

Software Implementation

The software for the sensor was developed using PlatformIO in VSCode. Here’s a breakdown of the key features:

• Data Collection: The ESP8266 collected data from the sensors every minute and stored it on the SD card in encrypted form.
• Sound Level Calculation: Raw voltage readings from the MAX9814 were converted into decibels using a logarithmic formula.
• Light Intensity Measurement: The BH1750FVI provided readings in lux, which were directly written to the SD card.
• Device Counting: WiFi probe requests were detected and processed to estimate occupancy levels.
• Data Encryption: AES encryption was used to secure the data, with a Python script for decryption during analysis.

Data Processing and Analysis

Once the sensor had collected enough data, I transferred the encrypted files to my computer and decrypted them using the Python script. The data was then processed using statistical and machine learning techniques to uncover patterns and correlations.

• Sound Levels: Excessive noise negatively impacted comfort and focus, while moderate background noise was generally well-tolerated.
• Light Intensity: Brighter light levels were associated with more positive emotions, especially during group activities.
• Device Counts: Higher occupancy often created a lively atmosphere but could also lead to overcrowding and discomfort.

Challenges and Lessons Learned

This project wasn’t without its challenges. Here are a few key lessons I learned along the way:

• Hardware Compatibility: Not all SD card brands worked well with the ESP8266. Testing different options was essential for ensuring reliable data storage.
• Sensor Calibration: Calibrating the MAX9814 microphone was tricky but crucial for accurate sound level measurements.
• Privacy Concerns: Implementing anonymized device counting required careful consideration to ensure no personal data was collected.

Conclusion

Building this custom ESP8266-based environment sensor was a rewarding technical challenge. From hardware design to software implementation and data analysis, the project pushed me to explore new technologies and problem-solving techniques. While the sensor was designed for a specific research purpose, its modular design and customizable features make it adaptable to other applications.

If you’re interested in building your own environment sensor or have questions about the technical process, feel free to reach out. I’d be happy to share more details or help troubleshoot your setup!