Edge Intelligence – Cisco Internet of Things (IoT)

Edge Intelligence

Edge Intelligence (EI) is edge-to-multicloud data orchestration software designed for connected assets. This software is deployed on Cisco industrial routers and compute gateways for simple out-of-the box deployment.

EI gives organizations full control over data—from its extraction to its transformation to its governance to its delivery. At each stage of data collection, EI streamlines the process so that it can be delivered easily at scale. For example, EI significantly speeds the labor-intensive process of developing and deploying applications that process data at the edge. It offers a plug-in for Microsoft Visual Studio Code. Organizations everywhere can easily create code and push applications out wherever they need to go without having to leave Microsoft Visual Studio.

EI provides the flexibility to integrate with multiple applications in multiple clouds. EI offers native integrations that simplify the entire process for Microsoft Azure IoT Hub and other MQ Telemetry Transport (MQTT) applications.

Edge to Multicloud Data Flow

EI helps you take control of your data throughout key aspects of its lifecycle, helping you simplify from start to finish. The following list and Figure 7-15 summarize this edge-to-multicloud lifecycle:


Figure 7-15 Lifecycle of Edge Intelligence

• Extract: You can automatically ingest data from any edge sensor using Cisco EI hosted on Cisco network equipment. EI has built-in industry-standard connectors, such as OPC Unified Architecture (OPC-UA), Modbus (TCP and Serial), and MQ Telemetry Transport (MQTT), that allow data to be extracted from disparate sources. The data is then converted to industry-standard formats to enable its full use.

• Transform: Once the data is extracted, EI enables real-time processing to filter, compress, or analyze data in a uniquely simple way. Via a plug-in, EI is fully integrated with one of the most popular tools, Microsoft Visual Studio Code. Developers can create, test, and deploy code without ever leaving the tool.

• Govern: EI provides a central point for the creation and deployment of polices that govern how edge data is processed and delivered.

• Deliver: Organizations have the data they need from multiple aggregated sources to gain actionable insights for the best decision making. You can then choose which data is sent to which destination and send it to multiple destinations/applications.

Overview of Configuration Lifecycle Management in EI

Creating an edge-to-multicloud data policy is a multistage process that can be completed in the EI UI. The key steps for EI management are shown in Figure 7-16.


Figure 7-16 Process of EI management

The progression begins with the extraction of the data from disparate sources and the transformation of the data using data policies. Finally, the deployed data policies deliver the data securely to the predetermined destinations.

Step 1. Enable EI agents. Deploy and configure the EI agents on the network device. They will then “call home” and show up in the EI cloud.

Step 2. Add and configure assets. Define the asset type, test it, and then configure the assets based on this asset type.

Step 3. Add data destinations such as Microsoft Azure IoT, MQTT Server, IBM Watson, Software AG Cumulocity IoT, or AWS IoT Core.

Step 4. Create and deploy the data policy, which send data from the assets to the destinations. There are two options:

• Data Rules: Sends data from assets to destinations without transformation.

• Data Logic: Uses JavaScript scripts developed in Microsoft Visual Studio (VS) code to transform data before it is sent to a destination (if local processing of data is required).

Figure 7-17 summarizes the creation and deployment of data policies using the EI.


Figure 7-17 Creation and deployment of data policies using the EI

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