As a college student, we spend a great deal of time in lecture halls, which are infamous for their extreme variance in temperatures. Often, we find ourselves freezing in one class, while sweating in the other. We realized that there was no clear solution for this issue, and thus we created Sweater to assist us.

Wouldn't it be amazing if any student could simply turn to their mobile devices, and instantly know how to prepare for the weather? As we thought further on this subject, we realized the true potential and scalability of this idea. We can apply this crowd-sourcing system towards monitoring temperatures inside of more than just lecture halls, with applications in determining restaurant ambiance, hotels, office buildings, and so much more. The uses are limitless!

How it works

Sweater utilizes crowd sourced data to determine the interior temperature of rooms, which users can add. This data is synced between all devices via a Firebase backend. In addition, the sweater ecosystem contains the ability to utilize the Wunderground API to provide outside temperatures as well. This way, anyone can know whether or not they will need a sweater.

Challenges we ran into

As three freshmen and one sophomore, we have relatively less experience in programming compared to many of the other competitors. As a team, we learned a variety of new skills, such as programming the backend servers.

Accomplishments that we are proud of

We were able to successfully create a full ecosystem within the allotted 24 hours, with both iOS and Pebble interfaces. Although no one on our team knew how to create a backend server going into this hackathon, we were able to create a functional database.

What we learned

We learned how to send requests and receive responses from backend servers through a variety of methods, including JSON and Firebase, which we then were able to implement into our iOS and Pebble interfaces.

What's next for Sweater

Sweater does not end with HackDFW. We plan on further developing Sweater to its true potential, with added logic and backend work to provide maximum scalability. Through enhancements in UI/UX, as well as logic to determine outliers within the crowd sourced data, we can provide a more accurate representation of the weather data for our users.

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