Minuscule’s IoT Projects

  • Setup IoT devices in geographically distributed locations and started gathering data through cloud
  • Processed huge volume of data for data analysis and decision making recommendations
  • We have used Apache QuickSight to visualize batch data stored in AWS S3
  • Created dashboards using multiple technologies based on our NoSQL expertise
    • Elastic Search is used for storing near real-time data from distributed IoT devices
    • User Kibana for visualizing the data analysis
  • We have used Analytics Engines like Apache Spark to analyze huge volume of data exist in Elastic Map Reducer

IoT Expertise

Our primary goal is to collect IOT data from various sources and provide a customizable dashboard to visualize the progress in cloud.

  • IOT devices can send and fetch data via AWS IOT which enables
    • To connect devices to AWS Services and other devices
    • Secure data and interactions
    • Process and act upon device data
    • Enable applications to interact with devices even when they are offline

We are using AWS Device Shadowing to control IoT devices using Mobile application which helps us to set the required state even if the device is in offline state. Once IoT topic collects the data via MQTT protocol, it sends the data to AWS Lambda for processing inputs and communicate with other devices via different Topic.

IoT Data & Analytics – Elastic Search

  • Distributed analytics engine capable of parsing JSON data based on index mapping and retrieve data near real time.
  • Helps us to track any events occurring in any organization.
  • Index per date has been created to track all events
  • It helps us to remove past index in a easy way instead of relaying on TTL feature
  • Use Kibana dashboard to represent the data in visual format.

IoT Data & Analytics

Firehose Delivery Stream & Amazon QuickSight

Incoming data from the Firehose delivery stream is fed into an Analytics application that provides an easy way to process the data in real time using standard SQL queries.

Analytics allows writing standard SQL queries to extract specific components from the incoming data stream and perform real-time ETL on it which is loaded in Amazon QuickSight to create a monitoring dashboard and check if the devices are over-heating or cooling down during use

Redirecting all IoT related data to S3 and doing analytics using Apache Spark via AWS Elastic MapReduce. This approach gives us a channel to do analysis of millions of records in an effective way.

It creates an avenue to do ML.


IoT Flow Chart