Edge Device Connectivity With Cloud-Based Machine Learning
The ability to define data at the edge device is an enterprise challenge. Traditional industrial networks are designed to store data on-premise or within a centralized hub. Retrieving and analyzing data in real-time, at the edge device, requires specialized expertise. Fortunately, having a network partner like Cirrus Link Solutions, allows an enterprise to implement IIoT edge gateway software in a way that data can be defined at the source and transmitted throughout the entire network.
The ability to define data at the edge, as a single source of truth, and transmitting the data throughout the enterprise is accomplished using IIoT Ignition Gateway or Ignition Edge Device with MQTT. Adding IIoT edge gateway software, like AWS IoT SiteWise, makes it easy to collect, store, organize and monitor data from industrial equipment, without the limits of location. Because the AWS IoT SiteWise is a secure, cloud-based, managed service, you can easily monitor equipment and identify defects or production inefficiencies.
Enterprise wide, this gateway configuration connects the edge device data source to the AWS Cloud, providing an interface through MQTT messages or APIs. AWS SiteWise has developed a tool for understanding MQTT Sparkplug, an open source software specification enabling edge device applications, sensors, or gateways to seamlessly integrate data within the MQTT infrastructure.
Once the edge device is connected and the IIoT software is configured to fit the enterprise data transfer and analytical needs, the MQTT protocols delivers the data and AWS SiteWise automatically creates a digital twin of the data, storing it on the cloud. The massive amount of data available in SiteWise puts Machine learning at your fingertips.
Machine learning, the study of computer algorithms that improve automatically through experience, is critical to an enterprise’s predictive analytics. Adding AWS machine learning and AI services solves the problem of developer expertise and network data analysis. Each edge device is seamlessly connected, data is collected and available in a secure location, machine learning and AI services allow for real-time responses to potential inefficiencies, and developers can quickly build or add devices and protocols.