How it Works
Edge Insights for Fleet is a set of pre-integrated ingredients designed to accelerate the development and deployment of solutions for the industrial sector. The package is meant to be deployed on the device closest to the data generation, such as the tool or machine assembling a product. This enables the ingestion of video and time-series data, storage of data, performing analytics, closing the loop by transmitting a control message, and publishing the results.
This section provides an overview of the modules and services featured with Edge Insights for Fleet.
Modules and Services
Video Ingestion and Analytics
Classifier algorithms are executed in the Video Analytics container. The container uses the Edge Insights Software Data Bus to subscribe to the input stream published by the Video Ingestion container. The container also uses MQTT through Kuiper* service to send data to the Alert Notification service.
The algorithms included in the container are optimized for Intel hardware using the Intel® Distribution of OpenVINO™ toolkit (OpenVINO™ and the Intel® Math Kernel Library. You can create multiple instances of the Video Analytics container to classify multiple video input sources.
The Intel® Distribution of OpenVINO™ toolkit is a software development kit (SDK) for deploying deep learning computer vision inference applications. It optimizes inferencing on your edge IoT device for Intel® architecture.
The Intel® Math Kernel Library is a library of highly-optimized, threaded, and vectorized math routines designed to maximize performance on Intel® processors.
Edge Insights for Fleet supports ingestion from:
Edge Insights for Fleet uses GStreamer* and OpenCV to provide a preconfigured ingestion pipeline that you can modify for different camera types and preprocessing algorithms. The Video Ingestion container publishes video data, consisting of metadata and frames, to the Edge Insights Software Data Bus. You can create multiple instances of the Video Ingestion container to ingest video from multiple input sources. In addition, the Video Ingestion container itself can run UDFs performing analytics. This is recommended for faster processing of frames to avoid transmission delays.
Training and Learning Suite (TLS)
The Training and Learning Suite has a web-based user interface for training deep learning models. The key TLS features are:
Remote deployment of a TLS-trained model into an Edge Insights for Fleet system.
Visualization of Edge Insights for Fleet video data classification results.
The image store provides storage and retrieval of images as binary blobs. You can store the images persistently by using SQLite*, an open source, SQL database engine.
Security and Configuration
The Data Security Framework enables security through a two-stage process involving provisioning and execution on an Edge Compute Node. Its primary objective is to prevent unauthorized access of generated data from within the system or via external network interfaces.
Edge Insights for Fleet uses etcd* for configuration management. etcd* is a consistent, distributed key-value store that provides a reliable way to store data that needs to be accessed by a distributed system or cluster of machines.
Docker* is a container framework widely used in enterprise environments. It allows applications and their dependencies to be packaged together and run as a self-contained unit.
Edge Insights Software Data Bus
The Edge Insights Software Data Bus is an abstraction over ZeroMQ*, which is used for all inter-container communication.
ZeroMQ* is a brokerless message bus that transfers data from the source directly to the destination. ZeroMQ* is used in TCP and IPC mode with pub-sub and request-response patterns.
Kuiper* MQTT Communication
Kuiper* is an edge lightweight IoT data analytics/streaming software implemented by Golang*. It can be run on many types of resource-constrained edge devices.
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