Machine connectivity and edge computing

05 June 2024

Dr. Christopher Anhalt discusses technological advances and market developments for industrial edge computing, focussing on the interplay of machine connectivity and edge, operating models and the scalability of industrial IoT solutions.

Edge computing describes a system of decentralised edge nodes that are located close to a physical data source. These edge nodes are connected both to devices and to a central platform (such as a cloud). Unlike components on the production asset level, edge nodes can be managed centrally, with the processing of the data collected either being handled within the edge node or by the central platform.  

The edge level can be viewed from a number of perspectives. While an application perspective describes software applications and their functions (for example, data pre-processing, data bus, etc.), an infrastructure perspective describes the IT infrastructure deployed, including hardware and operating systems, and an operating perspective describes tools with which edge levels can be managed and administrated  – monitoring tools or tools for handling multi-site software rollouts.  

Arguments for edge computing in Industrial Internet of Things (IIoT) applications are well-known. Some applications require very short latencies that are unlikely to be maintained by communicating with a centralised cloud platform. Data volumes are also very high in some cases, requiring extensive pre-processing at the edge level as a minimum. Last but not least, regulatory conditions apply to some applications – and these regulations can prevent data from ever leaving the company network.  

Architectural trends
Customers wanting to set up and operate an IIoT solution need to look at many issues, one of the most important of these is the question of a suitable system architecture. There is currently a trend towards the consolidation of IIoT architectures, which is characterised in part by the following edge level aspects and properties: 

• Users are deploying cloud platforms but want to minimise the edge level’s technological dependencies on the cloud while avoiding vendor lock-in.  

• Users are splitting the edge level into two – a factory floor level underneath plus a top level with central platform/cloud connectivity – and both managed centrally.  

• Users are deploying an MQTT broker at the edge level as a central hub for data traffic. Data are streamed with MQTT or Kafka toward the central platform while applications that are run locally can be accessed via the MQTT broker. 

So, how do these architectural trends and edge relate to machine connectivity?

Efficient and secure access to machine or device data is fundamental for IIoT applications. In terms of functionality these machine connectivity demands are virtually identical to those required by traditional shopfloor applications.  

The typical devices built into plants or to be handled by brownfield projects – controls, first and foremost – need to be integrated. The collected data must be provided using standard protocols with application-side support, which usually means OPC UA or MQTT. Also important are functions that permit the efficient handling of multiple data sources, such as the consolidation of data or data sources into a single interface. 

Looking at suitable operating models reveals more significant differences between traditional shopfloor applications and IIoT solutions.  

Deployed and operated locally at a production facility, traditional applications plus machine connectivity will feature an HMI or SCADA system, for example Manufacturing Executing System (MES) solutions, or a database link with the aim of ensuring data backups. Machine connectivity users are typically non-specialists and therefore require IT interfaces that are simple to use.   

In contrast, IIoT solutions characteristically involve applications or an IoT/cloud platform being deployed across multiple production sites. Not only does the platform run several applications rather than just the one, but these applications evolve over the solution lifetime, with one key driver of this change being the short innovation cycles in software and IT. Dedicated teams are deployed to run the solution: these operators are assigned multi-site responsibilities and have extensive IT know-how. Customers are looking to utilise IT-driven operating models and their associated benefits in terms of solution efficiency and scalability.

As with other solution components, machine connectivity needs to meet the same requirements for flexibility, operating efficiency and scalability. Increasingly, users are no longer viewing machine connectivity in the context of the IIoT solution as a production asset, but as an edge level component, with all of its associated advantages in terms of efficiency and scalability. 

Machine connectivity 
So, if machine connectivity is to be deployed as part of the edge level, what requirements need to be met? The following are some of the key aspects here: 

• Machine connectivity is provided with software modules that are deployed on standard hardware and managed by end customers in exactly the same way as for other edge-level software components. Docker containers are now a common choice here.

• Machine connectivity can be managed by standard IT tools. Often, this will involve popular Kubernetes-based platforms like RedHat OpenShift or Suse Rancher, but leaner alternatives such as Portainer may also be used.  

• Machine connectivity supplies relevant data to popular IT monitoring tools such as Prometheus and Grafana.  

• Machine connectivity provides documented, stable interfaces for configuration that make use of standard protocols – whether remote, automated, or both (for example http REST).  

Alongside these technical requirements, Softing has also identified an increasing interest in utilisng machine connectivity as a service, looking for flexible fee schedules that reflect actual need (and benefits) without requiring capital expenditure or investment in equipment. 

For the foreseeable future, we predict that software and IT innovation will remain the primary driver for manufacturing investment. Solution architectures are expected to show further consolidation around specific standards, with architectural blueprints and best practices offering simpler approaches to addressing end-user needs. Edge computing will also show accelerated deployment within manufacturing. 

We are seeing a growing interest in IT-driven operating models for machine connectivity, even if the solution context is still conventional and has no Industrial IoT aspects. Over the medium to long term, however, this trend will strengthen, with IT standards and tools then finding increased acceptance at the shopfloor level and within OT. In time, this will render obsolete the distinction made in this article between conventional and Industrial IoT solutions – at least in terms of machine connectivity. 

Dr. Christopher Anhalt is Vice President Product Marketing at Softing Industrial Automation. 

Contact Details and Archive...

Print this page | E-mail this page