Industrial IoT Edge Architecture for Machine and Deep Learning
significantly less than 1 % of total video footage .
How do you distinguish between normal footage 99 +% of the time , vs . interesting events worthy of archival ? It is based on image recognition and inference neural nets . Learning ( generation and enhancement of the inference model / framework ) happens at the Platform tier based on as many camera feeds as possible . Big Data storage and processing in action . Perhaps with human / expert assistance to train on identifying events of interest . So , gradually the system becomes better at detecting more and more important events . Periodically , upon non-trivial improvements to the inference framework , an update is pushed to all the edge devices for better inference ( i . e ., judgement / harvesting ) of “ interesting events .” In this truck use case , this manifests as an OTA software update .
What we describe above is the application / data plane , which layers on top of and is enabled by the control / infrastructure plane , provided by Pulse IoT Center . IoT Center insures the secure , reliable and orderly functioning of the different components of the system , and the data flow from end device to platform through the edge device . IoT Center also manages the OTA software updates . Following are more details of the control plane .
EdgeX on-boards the cameras as end devices ( the initial version of EdgeX does not support video ; however , this could be added in the future – our emphasis in this example is on the architecture ), and populates the device metadata . An EdgeX microservice processes the footage from each camera and applies inference model / incidence detection .
Another EdgeX microservice stores important event footage . Then an export microservice transmits this footage to a Platform tier service . Liota discovers on-boarded cameras through the EdgeX device metadata microservice and proxies these devices for management by IoT Center . It obtains infrastructure metrics ( e . g ., heart beat signal ) to monitor and ensure proper function . Liota also obtains metrics from the edge device itself ( e . g ., storage availability and CPU load ), for monitoring and managing . In addition , IoT Center is used for OTA software updates of the EdgeX inferencing microservice , and for the latest security patches .
CONCLUSIONS
We presented architectures for the edge and platform for real-time and batch data collection , model training , model management and model inference at the edge and Platform tiers . This is a comprehensive solution for Deep Learning in Industrial IoT which can be used in many use cases . One use case involving sensor-based trucking with open source and commercial solutions is also shown using our architecture .
IIC Journal of Innovation - 69 -