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Client belongs to one of the notable Environmental Monitoring companies.

Business Need

Client wanted to have a centralized environment monitoring system using IoT to access various graphical representations of the global environmental data like temperature, humidity, wind and directions etc. Also wanted to use AI ML capabilities for better climate prediction and analysis.

Client Situation

The business of our client was to study climatic conditions of specific geographic locations assigned to various units. But the main challenge was every time to study climatic conditions, client’s team had to remain at the site for longer duration to study everything. This effected their decision-making process as it often required local presence for longer duration. Client wanted to eliminate this constraint once the initial setup is completed with the help of IoT devices and implementing required environmental sensors. Rest of the time, our client wanted to monitor and action further from remote location based on current data and analyzing historical data trends.


Artificial Intelligence and Machine Learning (AI ML): TensorFlow, Python

Back-end Technologies: Java 1.7, Spring 3.2, JPA2, Entity Mapping Framework Dozer 5.5, ActiveMQ (MQTT), Spark

Front-end Technologies: Core JavaScript, Jquery 1.10.2, Rikshow.js (Graph data)

IDE: Eclipse Kepler

Database: MySQL 5.6 (Relational database), Cassandra (Time Series database), Hadoop

Server: Jboss 7.1 (Application Server)

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Recommended Solution

IoT based environmental sensors and hardware implementation was taken care by our client. Our role was to implement a robust and scalable IoT solution to collect data from the sensors & hardware and convert them to graphical presentation. Environment monitor sensors were collecting readings continuously for various parameters like temperature, humidity, wind speed, etc. Environmental monitoring system which our software development team developed for our client collected data from these environmental sensors and stored it in Cloud servers using API. Based on type, volume and velocity, classified time-series data are stored into the back-end system.

Key Highlights of our Environmental Monitoring System:

  • Implemented Cassandra to store time series data.
  • Secured layers at the time of data collection, transport and storage.
  • Detailed Dashboards accessible to various users based on Roles & Permissions.
  • Zoom in/zoom out feature to see various charts by Week/Day/Hour/Min

We also implemented Artificial Intelligence and Machine Learning (AI ML) capabilities to study the data patterns to track climate change & evolution of environment.


  • No need to stay and operate from hazardous locations to get the job done.
  • Easy access at finger tips, from anywhere without risking the health of operators.
  • Dashboard based graphical patterns to study climatic changes.
  • Central collection of data in sequence enables accurate predictions to forecast future environmental conditions.
  • Use of AI ML technology to predict trends in climatic changes.

Client Speaks

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