CloudRail Use Case
Several Options for Edge Computing
Edge computing is a particularly promising approach to smart industrial solutions. Using edge functions brings the functionality of IoT-services from the cloud directly on the local device. By processing data before sending it to the cloud, latency is optimized and the data stream is reduced. Furthermore, functions that analyse data and trigger events (e.g. a flashing LED-light) can be implemented locally and independent from the cloud-service. Striking a good balance between computing locally and using the power of cloud connectivity, a lot more can be achieved using the same set of devices.
Using the CloudRail.Box in combination with our Device Management Cloud gives you several options to use Edge Computing:
AWS IoT Greengrass extends the AWS functions with local processing options of the generated data before uploading it to either AWS IoT Core or AWS IoT Sitewise. Using AWS IoT Greengrass, Lambda functions or Docker containers can be used locally on edge devices without a connection to the internet. Only the required set and amout of data gets published to the cloud, saving costs for infrastructure and cloud-services. AWS IoT Greengrass can be remotely deployed to any CloudRail.Box with just one click.
In this tutorial, we connected an IO-Link laser distance sensor to AWS IoT Greengrass which runs on the CloudRail.Box to provide a simple object counter: Building an Object Counter with AWS IoT Greengrass and CloudRail
Microsoft Azure Edge is the native edge computing environment of Microsoft. Azure Edge can be remotely deployed to any CloudRail.Box with just one click.
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