Micro-climates exist within Philadelphia that can lead to life-threatening emergencies, and even death, due to extreme indoor temperatures. Many citizens within these communities where heat/cold islands exist do not have the resources or knowledge to afford proper management of cooling or heating systems. While federal and city programs exist to address this problem, a lack of household environmental data forces city and utility responses to be reactive instead of proactive.

An example use case: Maria is an 86 year-old woman who lives alone in North Philadelphia. She has limited income and is unable to always pay for proper heating and cooling. She occasionally is able to qualify for LIHEAP funding, but still has difficulty properly managing indoor temperature. She does not have internet access and has limited computer skills. PECO provides Maria with a Smart sensor, and registers contact information for her caretakers. This simple, self-contained sensor uses the LoraWAN network to provide real-time data the local neighborhood organization, which uses this information to track the heat and cooling island effects in the city. Further, the network sends alerting messages to Maria's family members when the temperature reaches extreme ranges. This allows her caretakers to come and help mitigate the situation (by bringing a fan, or an AC unit, or a space heater).

What it does

Our system uses the LoRaWAN network to provide real-time environmental monitoring data for low-income communities that are typically targeted for federal heating/cooling energy programs (such as LIHEAP). We monitor temperature, humidity, and other air quality parameters of households with at-risk populations (such as the young, sick, and elderly) and send this information to family members and community organizations. This information can then be used for immediate responses. Spec

How we built it

We used microshare APIs to send data to a front-end javascript framework written in ext-JS.

Challenges we ran into

The data pipeline seemed to run into issues, and the robots for the microshare network are a bit. We weren't able to get the full data pipeline working so we used static data instead.

Accomplishments that we're proud of

Getting the microshare facts framework working.

What we learned

How to use LoraWAN and get data into a visualization.

What's next for Philly Smart Urban Safe Monitoring

Finishing the pipeline and removing hard-coded data.

Built With

  • ext-js
  • sencha-ext-js
  • twilio
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