Analyze Smart Office Environmental Data in real-time with AWS IoT Analytics

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.

Each IoT Device is registered with AWS IoT Core. IoT Core allows users to quickly and securely connect devices to AWS.
Once an MQTT message is received from an IoT device (a thing), we use AWS IoT Rules to send message data to an AWS IoT Analytics Channel.

Amazon QuickSight provides business intelligence (BI) and visualization. Amazon QuickSight ML Insights adds anomaly detection and forecasting. Here we are using Amazon QuickSight to visualize the IoT message data, stored in the IoT Analytics Data set. 

AWS IoT Analytics is composed of five primary components those are – Channels, Pipelines, Data stores, Data sets, and Notebooks. These components enable you to collect, prepare, store, analyze, and visualize your IoT data.

• IoT Analytics Channel – AWS IoT Analytics Channel pulls messages or data into IoT Analytics from Amazon IoT Core. Channels store data for IoT Analytics Pipelines.
• IoT Analytics Pipeline – AWS IoT Analytics Pipeline consumes messages from one or more Channels. Pipelines transform, filter, and enrich the messages before storing them in IoT Analytics Datastores. Here transformations to the messages include dropping the device_id attribute and converting the temp attribute value to Fahrenheit. In addition, the Lambda Activity rounds down the temp, humidity, co, LPG, and smoke attribute values to between 2–4 decimal places of precision.
• IoT Analytics Datastore – AWS IoT Analytics Datastore stores prepared data from an AWS IoT Analytics Pipeline, in a fully managed database. Both Channels and Datastore support storing data in your own Amazon S3 bucket or in an IoT Analytics managed S3 bucket.
• IoT Analytics Data set – AWS IoT Analytics Data set automatically provides regular, up-to-date insights for data analysts by querying a Datastore using standard SQL.
• IoT Analytics Notebook – AWS IoT Analytics Notebook allows users to perform statistical analysis and machine learning on IoT Analytics Data sets using Jupyter Notebooks. Here The Notebook uses pandas, matplotlib, and plotly to manipulate and visualize the sample IoT messages we published earlier and stored in the IoT Analytics Data set.