In a conventional setup, the automation system interfaces with the IoT platform via a gateway. This gateway typically requires configuration and effort to stage the data. And it is typically at this point where contextualization work becomes critical, especially when disparate systems are involved. Because the IoT system is not “aware” of the automation structures, you may have to build a new model for every application, and further inefficiencies result as the IoT system bombards automation controllers for data. For instance, an IoT system would get information in pieces, such as: action X is about to happen, action X is happening, action X is complete. These three data points need to then be manipulated to connect as one related, continuous action.
In a system built with smart objects, data can be automatically organized, modeled and consumed by IoT systems and applications with little to no effort from a programmer. PLC tags can now have consistent definitions of rates, states, status, etc., and they’re delivered into information databases with context such as line number, machine name and location. These elements are all critical in enabling IoT solutions that ultimately drive more insights, better analytics and a deeper understanding of your process and potential risks. In the example above, the three data points for action X are automatically recognized as one continuous action for greater context.
