Heat Aware

Heat Aware is a Raspberry Pi–powered IoT system that monitors a car’s interior temperature and automatically calls a designated phone number if it becomes dangerously high, helping protect pets and children from heat-related emergencies.


Inspiration

Many pets and children are left in cars accidentally, leading to dangerous overheating. We wanted to build a simple, reliable system that alerts caregivers in real time to prevent heat-related accidents.


What it does

  • Monitors car interior temperature with a digital sensor connected to a Raspberry Pi
  • Automatically places a phone call (and optional SMS) to the registered caregiver if temperature exceeds a threshold
  • Logs all temperature readings and alerts in MongoDB for analysis and historical tracking

How we built it

  • Hardware: Raspberry Pi 4 Model B, DS18B20 or DHT22 temperature sensor, breadboard, jumper wires, internet connection
  • Software: Node.js + Express backend, MongoDB database, Twilio Voice API for calls, onoff or rpi-dht-sensor Node packages for sensor integration
  • Deployment: Raspberry Pi runs continuously in the vehicle, sending data to the cloud for monitoring and alerting

Challenges we ran into

  • Integrating real-time sensor readings with cloud logging
  • Ensuring phone calls trigger reliably without false positives
  • Managing secrets securely in Git and Python projects

Accomplishments that we're proud of

  • Built a fully functional IoT prototype capable of alerting caregivers automatically
  • Integrated hardware, cloud database, and Twilio alerts seamlessly
  • Implemented secure secret management using environment variables

What we learned

  • Best practices for handling secrets in code and version control
  • Combining Raspberry Pi GPIO programming with cloud services
  • Importance of iterative testing for real-time IoT systems

What's next for Heat Aware

  • Develop a mobile companion app for push notifications and remote configuration
  • Extend sensor monitoring to include humidity or air quality
  • Implement predictive alerts using weather data and historical trends
  • Explore OBD-II vehicle integration for enhanced monitoring of cabin conditions
Share this project:

Updates