𝗔𝗜 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗳𝗼𝗿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴
Smart cities need data to protect citizens. AI pipelines help monitor air quality, noise, and temperature in real time.
Here is how these systems work:
Core Components
- IoT sensors collect air and noise data.
- Edge devices like Raspberry Pi or ESP32 process data locally.
Data Ingestion
- Use Kafka or MQTT brokers to stream data.
- Use LoRaWAN or NB-IoT for low-power devices.
AI/ML Processing
- Deploy TensorFlow Lite or PyTorch Mobile models at the edge.
- Use predictive analytics to find pollution spikes.
Visualization and Alerts
- Build dashboards with Grafana or Plotly.
- Send alerts via SMS or push notifications.
Example: Air Quality Prediction You use Scikit-learn to predict pollution levels. A Random Forest model takes sensor inputs and outputs a prediction. This helps cities prepare for bad air days.
Why this matters:
- Cities respond faster to climate stress.
- Developers build tools that improve urban health.
Source: https://dev.to/chigozirim_favour_022bd45/ai-pipelines-for-environmental-testing-in-smart-cities-41pf
Optional learning community: https://t.me/GyaanSetuAi