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Industrial Ethernet Book Issue 105 / 10
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IloT Intelligence starts at the edge for industrial automation

Edge computing is becoming the sensor on-ramp to the IIoT. Once communication, security, and Internet computing technologies find their way into computing at the edge, the IIoT will begin to reach its potential and make an ever greater impact in the world of industrial factory automation.

ON THE TRAIN TO WORK, Lee opened an email on her smartphone sent from a controller operating a surface-mount tool at her factory. Attached to the email was a quality control report that suggested changing the tool's solder temperature. To generate that email suggestion, the controller had securely sent yesterday's production data to a cloud-based analytics system to compare current and historical data for the machine.

Next, it accessed the machine manufacturer's website and obtained the latest recommended settings. Finally, it built a production efficiency report with a suggested solder temperature for today's production run that would increase yield by 7 percent over yesterday's run. Lee clicked a link in the email and connected to the controller's mobile operator interface over a secure, encrypted channel. Lee logged in and navigated to the machine's solder temperature setpoint, where she entered the recommended value. All this took place before she got to the office.

The challenge of edge computing is to provide access to data that brings IT and OT solutions together.

At the network edge

The controller operating the surface-mount tool at Lee's factory operates at the edge of the factory's network. Systems like these are increasingly able to leverage cloud-based resources to perform edge computing-if computing resources exist as needed along the path from a sensor to the cloud-and if these computing resources reduce the total amount of data to be sent to the cloud for storage, processing, and analysis.

As a result, businesses can more quickly identify real opportunities for operational efficiency improvement and meaningful revenue generation. To foster such business benefits, data from the physical world of machines and equipment must be available to the digital world of the internet and information technology systems, quickly, easily, and continuously.

Successful industrial internet of things (IIoT) applications require operational technology (OT) professionals to make data from their systems, which monitor and control the physical world, accessible to the data processing systems of information technology (IT) professionals.

Once the data is there, cognitive prognostics algorithms running on IT systems can analyze it, refining raw physical data into actionable information that can predict outcomes in real time. The results can be used to improve inventory management and predictive maintenance and reduce asset downtime.

But before such benefits can be realized, three problems need to be solved: connectivity, big data, and IIoT architecture. Lee's factory has a new kind of programmable industrial controller (EPIC), which goes a long way toward solving these three problems.

The connectivity problem

The internet of things runs on vast amounts of data, generated by the physical world and then transported and analyzed by the digital world. It's an attempt to achieve perpetual connectivity and communication between people and things and even between things and other things.

But in the industrial world, most of these things were never designed to serve this new purpose. They were designed and installed long before the internet was developed.

At the edge, things like sensors, circuits, relays, and meters are attached to industrial control systems used to operate equipment and machines. These sensors translate what's physically happening in the world (temperature, light, vibration, sound, motion, flow rate, and so on) into an electrical signal like voltage or current.

The voltage or current is then interpreted by a controller, which monitors and controls physical equipment and machines. Sensors typically have little or no intelligence. They are designed to merely observe and report. They can't speak in the digital bits and bytes, the ones and zeros that information technology and internet devices understand and use to communicate. They also lack the physical connections and logical interfaces to communicate on the industrial internet of things (IIoT). They do not have a built-in Ethernet jack or a wireless interface.

They don't understand the languages the internet uses, like JSON, RESTful APIs, and JavaScript. They don't run an operating system or have a built-in TCP lIP stack or web server. And sensors have little or no built-in computing power, so providing edge computing at the sensor level to filter volumes of data before forwarding to the cloud is impossible. So right now the internet and the industrial things we want to connect to it aren't communicating.

What can we do?

One option is to simply wait for highly intelligent, connected sensors to become available to the marketplace. But those sensors are years away from being cost effective. Moreover, sensors installed today or even decades ago are still performing their tasks. They're just not connected to the IIoT, so the data they generate is siloed and inaccessible to IT systems for further analysis.

In Lee's factory, EPIC controller technology takes on the task of communicating with IT systems and the internet. Located at the edge and connected to sensors and devices through its IIO, the EPIC can speak the languages computer networks understand.

The Big Data problem

Across the globe a massive installed base of things exists today, generating useful data that the IIoT wants to access and consume. In oil and gas applications, a typical oilfield has up to 30,000 sensors installed. Factories and plants across the world have billions of sensors. Each sensor is capable of generating huge amounts of data from the physical world. Some IIoT applications could potentially generate terabytes of data per second. These are volumes of data the digital world has never seen before. This is the Big Data problem.

Moving that much data onto existing network and internet infrastructures for cloud based analytics and centralized management will clog networks, vastly increasing network and internet latency. For many industrial IoT applications, that is not acceptable, because real-time control and monitoring are mandatory. For the industrial internet of things to reach critical mass, intelligence must be pushed to the network edge, where the physical world meets the digital world. Computing systems at the network edge must have the capability to collect, filter, and process data generated at the source, before it's transmitted up to the IIoT.

And at the same time these edge computing systems must be able to complete the local real-time process control and automation tasks of traditional industrial applications. The Big Data problem can be at least partly solved by edge systems which have the computing power to sift and process data before sending it into network and internet infrastructure.

The IIoT architecture problem

A third problem revolves around how today's IIoT architecture works. Let's take a look at its complexity and explore a possible path forward. For a cloud-based server to capture data from an analog sensor today, the sensor's data must be translated using a series of software and hardware tools.

First, the sensor is physically wired to a device such as a PLC (programmable logic controller). While modern PLCs do provide basic analog-to-digital conversion of sensor signals, PLCs were not designed to interface with the IIoT. PLC hardware, software, and programming languages were designed for repetitive, application-specific tasks like process control and discrete automation. They typically use proprietary protocols and languages for communication and programming, and do not include information security standards like encryption and authentication.

PLCs were originally designed as standalone systems. The protocols they use are seldom internet compliant and are designed for point-to-point communication instead of the point-to-multipoint communication architecture found in the IIoT ecosystem.

If systems that communicate using internet compliant protocols such as PCs, web servers, and databases want to communicate with a PLC, a vendor-specific and often proprietary software driver or hardware-based protocol gateway is required.

OPC (Open Platform Communication) software is one solution to this communication disconnect. But OPC was originally designed around PC architecture using the Microsoft Windows-only process exchange, COMI DCOM. Most systems and devices connecting to the IoT are not Windows-based devices. For example, take your smartphone. It's likely an Apple or Android device, both of which run modified versions of the Linux operating system, where COMIDCOM process exchange does not exist. OPC UA has been released, but it's merely a wrapper for existing OPC drivers built on Windows architecture. It requires design engineers to build an OPC UA client adapter into their products. And even then, modern network and internet assets such as web servers, databases, smartphones, and tablets do not speak OPC UA.

PLCs, OPC servers, proprietary drivers, and protocol gateways quickly become a convoluted IIoT architecture. These layers of complexity not only require time, money, and specific domain expertise to install and maintain, but also the data being sent from the physical world has been converted by so many different pieces of hardware and software that data integrity can be jeopardized.

Imagine the difficulty in provisioning and troubleshooting these IIoT systems. And then consider that today's automation architectures often do not address information security. Sending data generated at the edge through so many layers of conversion opens up complex information security concerns as the data is transported to the cloud. Multiply these architectural issues across the billions of devices we expect to connect, and you see the communication challenge the IIoT faces.

Flattening the IIoT architecture

One way to simplify this complex IIoT architecture is to consider a different communication model. Most devices on a computer network today communicate using the request-response model. A client requests data or services from a server, and the server responds to the request by providing the data or service. This is the model company computer networks use, and it's also the typical model for that biggest of all networks, the internet.

Each client on the network opens a direct connection to each server and makes a direct request. A client doesn't know when the data changes, so it sends requests at intervals (in automation, as fast as once per millisecond), and the server responds each time.

This communication model works very well on a network where the number of clients and servers is not large. But the IIoT poses problems of scale. If each client must be connected to each server it uses and must constantly request and receive data, network traffic can quickly become an issue.

Another model may be more effective in IIoT applications: publish-subscribe, or pub-sub. In the pub-sub model, a central broker becomes the clearinghouse for all data. Because each client makes a single lightweight connection to the broker, multiple connections are not necessary. Clients can publish data to the broker, subscribe to data going to the broker from other clients, or both.

In addition, clients publish data only when it changes (sometimes called report by exception), and the broker sends data to subscribers only when it changes. The broker does not hold data but immediately forwards it to subscribers when it arrives, so multiple data requests are not necessary.

These two factors, just one connection per client and data traveling only when it changes, shrink network traffic substantially, making pub-sub an effective communication model if you have many clients and many servers.

Pub-sub can also be effective when it's difficult to set up a direct connection between a client and a server, or when the network is low-bandwidth or unreliable for example, when monitoring equipment in remote locations. The pub-sub transport protocol MQTT is frequently mentioned for IIoT applications, especially when used with Sparkplug messaging, which was designed for industrial applications. Secure data communications using MQTT/Sparkplug can help flatten IIoT architecture and more easily produce the results we need.

Architectural changes

As we've seen, for the IIoT to reach critical mass, internet protocols and technologies need to be driven into systems at the edge, where the physical and digital world connect.

Layers of complexity must be removed from the communication process between digital systems and physical assets. Modern IIoT system architectures must be flattened, streamlined, optimized, and secured.

Edge computing systems must easily and securely access the cloud through the open, standards-based communication technologies the internet is based on.

That means:

  • Internet technologies like MQTT/Sparkplug, TCP/IP, HTTP/S, and RESTful APIs -the dialect of the internet-must be built directly into devices at the edge.
  • Internet security technologies like SSL/TLS encryption and authentication must be built in directly to edge systems.
  • Cloud-based systems must be able to make RESTful API calls to access data, or use a publish-subscribe communication model like MQTT/Sparkplug to get data from remote edge devices, without the layers of complexity and conversions that exist in industrial applications today.

The power of interoperability

The standard technologies used by the internet for transmitting information have created a cohesive system for communication. But we have not always had this cohesive system. Before the internet and the worldwide web, many different internet-like protocols and architectures existed. Computer systems all ran different operating systems, requiring different programming languages.

Small pockets of interconnectivity existed, but for the most part systems were disconnected from each other. It was very similar to the way industrial systems communicate today, with the need for converters, adapters, and gateways. The internet was designed to allow input/output and information systems to share data through a common interface, removing layers of complexity and allowing for greater interoperability between systems designed and manufactured by different vendors.

That's why an Apple computer or Android phone today can send an email to a Windows computer: despite their incompatibilities, they speak the same internet languages. Today's internet uses a common set of protocols, tools, and routines designed to make the transportation, acquisition, and analysis of digital information a seamless process, no matter what device you're using.

Although sensors and other physical assets installed at the edge may not have been designed with internet interoperability in mind, there's still a massive opportunity to collect meaningful data from the huge installed base of existing things. But it will require a solution that understands both sides of the OT and IT convergence and that can:

  • Locally translate the physical world of currents and voltages (OT) into the secure communication protocols and languages the digital world (IT) understands.
  • Process and filter mountains of data, sending only the necessary data to the cloud for analysis.
  • Provide communications interfaces and processing power to maintain the closed loop, real-time control requirements of industrial applications.
  • Deliver all of the above in a package suitable for challenging industrial environments.


We've seen that edge computing is the sensor on-ramp to the IIoT. Once the communication, security, and computing technologies of the internet find their way into computing at the edge, the IIoT will begin to reach its potential.

Internet technologies are available in some industrial systems today. And some vendors have already started bridging the gap between OT and IT by adding IIoT technology like MQTT and RESTful APIs directly into their controllers. The shortest path to a successful IIoT is to leverage the existing interoperability technologies of the internet in industrial automation products and applications.

Technology report by Opto 22.

Source: Industrial Ethernet Book Issue 105 / 10
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