A Knowledge Graph Driven Approach for Edge Analytics
the downstream control message schema to modify the operation of the sensor or actuator , relevant protocol information , and any run-time information and parameters ( e . g ., container references , etc .).
To enable the edge devices and analytics applications to communicate with one another , we deploy a messaging backbone within the edge appliance gateway . Since the messaging protocol employed by the sensor may not be the same as chosen for our messaging backbone , the device expert would need to create a translation container specific to a device type that would consume messages from the edge device ' s protocol and push them to the messaging backbone ' s protocol ( and vice versa ). While a container only handles a single edge device instance , a containerized approach allows for additional instances of the same container to be deployed whenever a new sensor is onboarded .
Application Expert
The application expert can query all necessary information for development from the knowledge graph . The analytics container developed by the application expert pulls the data and control message schema from the knowledge graph allowing the application to consume data from the edge device . This information , along with any known hardware dependencies , is documented in the knowledge graph when metadata for model sensor types is onboarded into the RDF ontology . The application container communicates with the edge device ( or another application container ) through the centralized messaging backbone to seamlessly produce and consume data .
Our framework ' s containerized approach allows the application expert to reuse or to develop an application in whatever environment they choose so long as containerization is supported for that operating system ( currently supported by all major OS ’ s ). For clients that seek to reuse applications from their existing application portfolio , the only required addition is communication with the messaging backbone . Additionally , because the production and consumption of data is abstracted through the message bus , this application container may be deployed on any edge appliance that has access or subscribes to the edge device ' s message topics . Messages are labeled as belonging to a topic category for subscription models and filtering of data messages , allowing for simplified vertical mobility of analytics containers as hardware resource constraints or uplink backhaul quality requires .
Field Engineer
The field engineer needs only to concentrate on the physical deployment of the sensor itself . Once the sensor is installed , it will begin to generate identifying heartbeat messages , which in turn will be consumed by an auto-discovery agent resulting in the registration of the new unique sensor instance into the knowledge graph . The registration of a new instance of an edge device will trigger the deployment of the container instances required to communicate with the edge device ( e . g ., message protocol translator ) by our orchestrator . Every new sensor that is
- 50 - September 2017