and money . By providing railroad operators with repeatable and quantifiable data in real time , railroads are able to make timely decisions that improve bottom line revenue and ensure the safety of its passengers and cargo .
After spending more than a year running demos for clients , using a web portal locally hosted in its office , RailPod realized it needed a cloud based IoT platform that could demonstrate how its technology could ingest , process and visualize the data from the drones in real-time . RailPod wanted a platform that required minimal maintenance and leveraged managed services , yet would be cost-effective . RailPod also wanted a solution that could scale with its customers and was architected to enable new data sources to be ingested as they were identified . Furthermore , Rail- Pod needed to closely control the flow of data to its customers and ensure governance and entitlements were properly maintained .
RailPod engaged Cloud Technology Partners ( CTP ) early in its ideation process to provide expertise around cloud , big data and IoT . After helping RailPod create a list of requirements , CTP created a solution architecture , UI / UX prototype , sprint backlog and a resource plan for future execution . In less than two months , CTP built out a prototype solution with viability for scalable growth .
An Enterprise-Class IoT Solution
Phase One
The goal of the first phase of the project was to build a prototype cloud-based solution that would enable RailPod to accomplish three key goals :
1 . Store and archive data captured by the drone .
2 . Visualize the measurement data in a web-based portal .
3 . Secure data so that users only see the data they are authorized to view .
The portal was built with login and logout functionality , a map of rail lines and charts to exhibit sensor data . CTP began with a detailed experience design plan and then proceeded to create the infrastructure , develop the application code , implement security , performance and durability measures , and provide RailPod recommendations on IoT and data integrity .
The IoT platform was developed using Python , SQLAlchemy , React , Leaflet , and PostGIS and is currently hosted on an EC2 instance . The following AWS services are currently being used to support the solution : VPC , EC2 , S3 , RDS , SQS , SNS , and Snowball .
Future Phases
The next phases of the project will enable integration with the AWS IoT platform and leverage serverless computing . Once the RailPod team is able to integrate the IoT SDK into their drone , MQTT messaging will replace the need for parsing large binary files and AWS Lambda will eliminate the need for EC2 instances , reducing maintenance overhead and costs . Future phases will also include performance improvements , including the building of a tile server for rendering maps more efficiently .
Deploying on Amazon Web Services
RailPod selected AWS to host its solution because of the smooth integration between the AWS IoT platform and Railpod ’ s existing system . While the RailPod team was integrating the AWS IoT SDK into their drone , AWS S3 , RDS , and EC2 provided an inexpensive way to deliver the initial prototype . AWS Snowball also provides an easy way to quickly transfer hundreds of GigaBytes of existing data into S3 .
The Data Backbone
Due to the high variety of data measurements and their frequency , the data collected by the RailPod drone is stored in large data files that are transmitted to the cloud either directly or through a proxy device . As the drone performs an inspection of a rail line , new files are automatically uploaded to AWS and processed as per RailPod ’ s predetermined business rules . When the data has been successfully ingested and processed , or if it has failed , an email notification is then sent to the appropriate groups . In order to handle RailPod ’ s security requirements , CTP integrated CTP Central , a proprietary containerized multitenant and multi-user software , as the authentication mechanism for the platform .
48 | THE DOPPLER | WINTER 2017