Why Azure IoT Edge Is a Favourite Platform for Edge Computing

Azore IoT Edge service is built on top of IoT hub. The service is useful because you don’t need to send the data to cloud for analyzing it, instead you can analyze it on the device itself, that’s why it’s called “at the edge”. The advantages of this approach is to save time sending and receiving messages to the cloud and react on the events much quicker on the real time data.

Open Source:

Azure IoT Edge is open source and available in the below GitHub repositories.

Version 1: https://github.com/azure/iot-edge-v1 https://github.com/azure/azure-iotedge

Version 2: https://github.com/azure/azure-iotedge

As the complete code is available, it gives more control to developers to modify and fix the issues.

Security

Azure IoT Edge services using the Azure IOT capabilities to provide security. One of the service is called Device Provisioning Service, where you don’t need any human intervention in providing the provisioning to devices and it can be done on real time basis. Through this way you can provide millions of device provisioning without any security bottlenecks.

The Azure IoT Edge security manager is a well-bounded security core for protecting the IoT Edge device and all its components by abstracting the secure silicon hardware. It is the focal point for security hardening and provides technology integration point to original device manufacturers (OEM).

Machine Learning on the Edge

This is really an amazing feature where you apply machine learning not on the cloud but on the device itself. You will prepare the model and train it on the cloud and then dockerize and deploy it to device. The devices are able to predict the maintenance requirements or let say if it is a pipeline then it predicts that pipeline is going to break soon, need replacement etc.

I will prepare a separate post to do a walk through with some real examples.

There are only 2 major platforms available for IoT Edge, the AWS Greengrass and Azure IoT Edge. As the Azure IoT Edge release almost after one year of the launch of AWS greengrass, they got the time to analyze the dynamics and readiness of the industry and understand the customer requirements.