What Is Edge Computing?

There are many buzzwords and acronyms regarding the Industrial Internet of Things, commonly called IIoT.

In this article, we will discuss Edge Computing.

So, what exactly are we referring to? The famous musician The Edge is from the U2 musical group.

He’s not part of the Edge Computing community.

You may have heard of Edge Devices, IIoT Gateways, Edge Processing, and Local Computing. Each of these terms relates to the umbrella concept of edge computing, where data is processed closer to the source rather than relying on centralized cloud servers.

Edge computing vs. Cloud computing

OK…so what about Cloud Computing? Is it similar to Edge Computing?

Well, sort of, as both can process, store, and analyze data to enable decision-making.

The difference is that Edge Computing processes data closer to where it is generated compared to cloud computing, where data is sent to a remote center for processing.

They often work together as a team. Edge computing offloads some data processing closer to the source while other data is sent to cloud computing.

So, why is Edge Computing used? One reason is that Edge Computing reduces the time it takes for data to travel from its source to its destination.

This results in faster response times when quick decisions for industrial automation applications require real-time interaction. Edge computing provides Immediate action based on quick decisions.

A Real-life example is intersection traffic control. Why send vehicle sensing information to a distant cloud computing center when decisions and actions can be made by edge computing right at the intersection?

In the IIoT world, cloud computing also provides extensive data analysis, machine learning, and historical insights crucial for predictive maintenance and optimization.

Edge devices

Alright…now let's talk about Edge Devices.

You may have guessed it by now, but an Edge Device performs the local Edge Computing and is positioned close to the source.

Edge computing devices in the IIoT world include edge gateways, industrial PCs, and edge controllers.

Here are a few examples of commercially available edge computing devices.

The Siemens SIMATIC IoT2040 is an industrial IoT gateway that connects and processes data from industrial systems.

The Phoenix Contact EPC1522 is an industrial edge controller for real-time data processing in automation systems.

The Dell EMC PowerEdge XE2420 is an industrial PC designed for edge environments with data processing capabilities.

Let’s take a closer look at the Phoenix Contact EPC 1522.

Phoenix Contact EPC 1522

Phoenix Contact states, This PLCnext Industrial programmable edge device controller supports IEC 61131-3, MATLAB Simulink, and C/C++ and is perfect for protocol converting and data collection. It can be programmed with PLCnext Engineer.

Data can be collected from sensors and other PLCs through protocols such as PROFINET, OPC UA, Modbus TCP, and more.

Sensor data can be introduced into applications like the browser-based Node-RED flow editor. In addition to utilizing ever-expanding open-source nodes, PLCnext nodes facilitate data exchange between the IT world and PLCnext Engineer.

For example, data can be read from the PLCnext Engineer control program, saved, visualized in a database, or sent via MQTT.

Unlike a traditional PLC, data can be stored and analyzed directly on the EPC 1522 via PLCnext's available databases, such as InfluxDB or MySQL.

You can create an alarm management system by analyzing data locally using Node-RED.

Using InfluxDB, data can be visualized to track and optimize the process and its accuracy over months.

EPC 1522 in action

Let’s look at an application example using this edge controller.

We want to use this controller to illustrate local edge computing action and send data for cloud computing.

In our example, an edge controller collecting temperature data is part of an IIoT network. The temperature data is used to initiate immediate action and assists in developing a predictive maintenance strategy.

We will use the PLCnext nodes in Node-RED to read and collect the temperature data.

We’ll initiate an alarm when the temperature exceeds a set point.

We’ll send the collected data via MQTT to the cloud to assist in developing the predictive maintenance strategy.

We will use the PLCnext nodes to read the temperature values derived from the sensor connected to the edge controller.

We’re interested in the TEMP and the ALARM variables from the PLCnext Engineer program running on the edge controller.

Triggering the inject node reveals a temperature of 69 degrees F and an alarm state of true.

A sneak peek at the program shows us that the Alarm setpoint is 25 degrees F.

This ALARM signal can be used for any number of actions locally.

We’ve also created a simple Node-RED Dashboard to indicate temperature values and ALARM state.

In our example, we use InfluxDB to store temperature data via the Node-RED influxdb-out node.

We've also established an MQTT Mosquitto Broker connection to publish and subscribe to the temperature data, taking advantage of the MQTT app on the edge controller.

We use MQTT Explorer to view the Temperature data.

We could use MQTT to send the temperature data to the cloud, which can be stored in databases like InfluxDB for further analysis.

That should do it for this article.

Wrap-Up

Ok…Let's review…

• Edge computing processes data closer to the source rather than relying on centralized cloud servers.
• Edge computing provides immediate action based on quick decisions.
• Edge Devices perform the local Edge Computing and are positioned close to the source.
• Edge devices can collect data from sensors and other PLCs through protocols such as PROFINET, OPC UA, and Modbus TCP.

Want to train your team with practical industrial automation skills? Head over to realpars.com/business and fill out the form to get started.

‍