Azure IoT Edge

Data needs to gather from devices and needs to be processed before pushing it to the Microsoft Azure IoT Edge hub. Also, Device servers are present in network but it doesn't have access of internet for the security purpose. So to solve this issue Microsoft Azure IoT Edge is used, Microsoft Azure IoT Edge communicates with the server using MQTT protocol, customize module is used to process the data, and then it pushes to the IoT hub.

Microsoft Azure IoT Edge is well suited where Edge computing and analytics are required. It is also useful when the device doesn't have the capability to directly push the data to IoT hub. Filtration of raw data to some meaningful data is the business requirement then Microsoft Azure IoT Edge can be used.

Microsoft Azure IoT Edge has enabled us to accelerate our educational service delivery and business at the edge with data from students, staff members, places and connected devices in unique ways that unleash innovation with IoT. Our mobile IoT deployments helps us drive efficiency and engage various stakeholder groups within our university environment, to develop new business with greater insights.

Deployment of mission critical business support solutions particularly those that support virtual learning and video conferencing environments. This is because they help organisations such as the university to effectively transition to a digitally compliant organisation.

We used it for POC purpose. We wanted to show our clients how Edge computing can help their business to save a lot of money. Our product is an IoT based ERP system which is being used by our clients at different locations. Microsoft Azure IoT Edge helped us to present the concept of Edge computing like how we can process the machine generated data at the client location (Edge) to make quick decisions and avoid latency issues and send only the data that is required back to Azure Cloud.

It is well suited if you have a production plant and you want to cut down the cost of cloud storage. It is best for medium to large companies if they want to process their machine generated data at their production site and only store the data that is required in cloud storage. It is less appropriate for small companies.