IoT Analytics can manage the complexities of petabytes of IoT data. IoT data frequently has significant gaps, corrupted messages, and false readings that must be cleaned up before any analysis can occur. Additionally, IoT data must often be enriched and transformed to make it more meaningful. IoT Analytics can filter, transform, and enrich data before storing it in a time-series data store for further analysis.
PROLIM has implemented AWS IoT Analytics in the Smart Office solution to analyze environmental sensor data, in near real-time, from a series of IoT devices. In this solution, seven parameters are monitored from four sensors at a regular interval. Sensor readings include temperature, humidity, carbon monoxide (CO), liquid petroleum gas (LPG), smoke, light, and motion along with a device ID and timestamp, as a single message, and publishing to AWS IoT Core using the ISO standard Message Queuing Telemetry Transport (MQTT) network protocol.