Team "Living on the Edge".
Using some bleeding edge technology, we have demonstrated how a unique approach to consumer feedback can enhance the existing the data set for machine learning to spot future issues in the water network.
Our approach is to show how low cost, high value Edge devices can be used to collect consumer feedback as "Dashes" and extra telemetry from non-invasive sensors and/or semi-permanent sensors. This data is stored in an OSIsoft Edge historian on an edge device (Raspberry PI) running Windows IoT Core. That then communicates with the PI System via a custom application we have written that can read from the Edge historian and send the data to PI Web API. This is then used in AF Analyses to calculate scenarios where extra telemetry data needs to be fed back to the central PI System.
Pretty awesome, right?