Inspiration

When you want to study, one thing that can ruin your day is noise. Of course, there's no real way to know how noisy somewhere is without actually going there. That's where Cashew comes in.

What it does

Cashew uses small, lightweight sensors to sense ambient noise and send it back a central server. That server displays that information on a map, allowing you to pick your best spots.

How we built it

We used Firebase Realtime Database for our database. The front-end is written in Typescript and D3, and our sensors are a mix of Arduino Nano's and NodeMCU's. Our embedded code was written in C.

Challenges we ran into

Our biggest challenge was wrangling the hardware and getting the communications we need, including short range radio and WiFi.

Accomplishments that we're proud of

Our proudest accomplishment was getting Firebase automatically updated using data from the sensors and using that to update the front-end map visualization. It was great seeing it all put together.

What we learned

We learned a lot about hardware, primarily serial communication. A large part of our hardware work was communicating over serial with the radios and wifi modules.

What's next for Cashew

What's next for Cashew is more types of sensing. Something we wanted to get to but didn't have the time for was sensing of nearby WiFi enabled devices, giving us a rough proxy for nearby crowds. Other examples include light levels and temperature. The possibilities in this framework we've built are endless.

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