Edge Computing Middleware: How It Can Tame IoT Complexity
One of the biggest challenges when working with APIs based on open source is not just reading data from devices, sensors and actuators in a factory, “but being able to have data that’s interoperable,” Steele said.
EdgeX Foundry is basically a collection of more than a dozen micro-services written in Java, Go, C and other languages that are deployed via Docker or Docker Compose. In general, data flows from a sensor and is collected by a data service, where it is then passed to a core service and then passed to an export service for transformation and filtering. At that point, the data is available for edge analysis which can trigger device actuation or shutdown.
Analyst firm MachNation described in a report the benefits of open source edge platforms, but the ultimate choice depends on an enterprise’s expectations for the project. “An open source platform is like a ball of clay that requires some serious work before it’s ready for enterprise users,” said MachNation President Steve Hilton.
Part of the consideration for IT leaders is whether a prospective IoT platform on the edge or in the cloud can support several types of users, including IT admins, backend developers, operators, hardware developers and user interface developers, Hilton said.
Case Studies Emerging with Edge Analytics
The benefits of using edge computing middleware are starting to emerge in case studies. A key area has been applying analytics and other artificial intelligence at the edge to better monitor processes for greater productivity or efficiency, perhaps by lowering network latency in critical processes.
“Edge platforms allow certain process such as machine learning to be decentralized and occur in a more optimal physical location,” Hilton said. “By running these processes at the edge rather than in the cloud, an enterprise can create more secure, reliable and scalable IoT deployments.”
Hilton gave the example of a piece of robotics equipment on a factory floor that is connected to an edge platform. The IoT application running on the edge hardware monitors the robot to make sure it doesn’t start assembling components incorrectly. If the connection linking the robot to the cloud fails and the monitoring application is only based in a cloud location, it wouldn’t be able to monitor the robot if something did go wrong. However, if the IoT app runs on the edge, it wouldn’t need the connection to the cloud and monitoring could theoretically continue. As a result, any problem with the robot could be noticed and an action taken.
FogHorn Systems is one of a number of vendors providing machine learning to analyze data from high resolution cameras and other sources for detecting irregularities. In one example, an oil refinery’s flare used to burn off certain gases is being constantly monitored via video to check for underlying problems in the refining process. The company’s Lightning ML platform serves as the edge middleware; it ingests data from the video and processes it in a convolutional neural network, then pushes out insights to a user interface, according to Ramya Ravichandar, vice president of product management.