As students who live in Mountain House and attend Mountain House High School, we are heavily impacted by the noise pollution and extreme heat that the city faces. Oftentimes, we have to walk home from school in extremely hot temperatures and are completely burnt out afterwards. Our project involves using sound sensors, heat sensors, and light sensors to create a database that compiles all the data from the sensors and assesses stress levels in certain areas, and highlights urban hotspots with high noise pollution and heat levels. We built our project as a combination of hardware and software parts. The hardware aspect pf our project includes the actual Raspberry Pi and breadboard with all of the sensors attached and our software converts the data collected from the hardware into measurable units and data. We experienced numerous challenges throughout the project, including being unable to attach the LDR sensor to the breadboard and issues with setting up Sudo on our primary software device. In the end, we had to omit the LDR sensor from our model and include example data collected in the past for our prototype website. We managed to write the entire back-end code for our website and made it work well for analyzing data collected from sensors. In the future, we plan to incorporate more real-time data from the sensors.
Built With
- base44
- gpiozero
- ldr
- lm35
- matplotlib
- multimeter
- python
- raspberrypi
- rpi.gpio
- sci-learn
- soundmodulesensor
- sudodisk
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