Water damage is a leading cause of mold in buildings. According to the National Institute for Occupational Safety and Health, it is estimated that approximately 47% of residential buildings in the United States have visible mold or mold odor. With this alarming statistic in mind, our goal is to develop a solution for landlords and homeowners to address this issue before it happen.
What it does
The Moist Meter is a Internet-Of-Thing web application equipped with a specialized sensor that can be embedded in wooden studs or into drywall. Upon contact with moisture-laden wood, the moisture will create an electrical bridge between the sensor's conductors legs that will conduct a small amount of currents. By precisely measuring the quantity of current conducted, Moist Meter can provide an accurate moisture assessment within the wood or drywall, and notify users of potential moisture-related concerns with a user-friendly interface and what preventative steps should be taken.
How we built it
For our hardware component, we used Digikey’s soil moisture sensor, which affixed it into a piece of cardboard. To simulate moisture in drywall, we gradually apply water until it reaches the maximum conductivity point.
We then use the MQTT protocol to transmit the sensor's data. Our Raspberry Pi “publishes” the sensor data every second to a topic on HiveMQ.
After the data is published on HiveMQ, we have data services subscribers “subscribe” to the topic for access and display of that data in the clients.
Our first subscriber compiles the data and calculates the average value every minute. This information is then stored to MongoDB, allowing our Moist Meter’s dashboard to show the historical moisture data. The dashboard can then provide weekly, and monthly change in moisture levels, and the chances of mold based on the historical data.
Our second subscriber provides the real time data for our Moist Meter’s floor plan heat map and offers immediate value for the best assessment. This feature helps identify which walls require inspection for potential mold issues.
Challenges we ran into
- Limited amount of on board memory(256 KB) and memory leaks in the Raspberry Pi Pico W.
- Connecting the Raspberry Pi Pico W to UTA's WLAN was not possible and therefore we have to use our mobile hotspot to publish the data to HiveMQ.
- Google Cloud credit didn't arrive in time, so we have to use our own money for projects.
Accomplishments that we're proud of
This is our team first hackathon where we employed the use of hardware, even though it take us a bit too long to get it set up properly.
What we learned
Learning about MQTT Protocol Learning about Raspberry Pi Pico W libraries ...etc
What's next for Moist Meter
When we have more historical data and more customer, we can implement an AI model to actually predict the chance of mold.