geo-infer-iot

To ingest real-time IoT sensor data using MQTT, connect to your broker using paho-mqtt or asyncio-mqtt to stream data into spatial analysis pipelines. This enables sensor network management and real-time environmental monitoring.

Running Bayesian spatial inference on streaming sensor data is achieved by applying H3 indexing and geospatial libraries like geopandas. This approach identifies pollution hotspots and predicts environmental quality levels continuously.

Yes, you can process real-time IoT data streams from both CoAP and HTTP simultaneously. The system integrates multiple protocols alongside MQTT, enabling comprehensive sensor network management and simultaneous data quality control.

Monitoring air quality across a city using distributed IoT sensors involves ingesting real-time data streams via MQTT or CoAP, applying H3 indexing for spatial analysis, and using Bayesian inference to identify pollution hotspots.

You do not strictly need Kafka to handle real-time geospatial data streaming, as MQTT and CoAP protocols provide direct connectivity. However, confluent-kafka is supported as a dependency for high-throughput sensor data pipelines.

Using H3 indexing for IoT sensor network spatial analysis provides a uniform hexagonal grid to accurately map distributed sensor readings. This enables precise Bayesian geospatial inference and effective environmental monitoring.