Industrial IoT deployments have traditionally focused on asset tracking and machine data collection through sensors and connected devices. These systems rely on technologies such as GPS, Bluetooth Low Energy (BLE), RFID, and industrial sensors to collect data on location, vibration, temperature, and equipment utilisation hours.
Industrial IoT forms a core component of Industry 4.0 initiatives, with deployments in manufacturing, logistics, and industrial operations. Edge computing has been adopted across several industrial sectors to support data processing closer to machines and equipment.
Tracking systems generate machine data
This data is often transmitted from sensors to gateways or edge devices. It can then be processed locally or forwarded to centralised systems. In many industrial environments, machines generate large volumes of telemetry that require filtering and analysis before use in operational decisions. Only a portion of this data is typically transmitted to central systems for deeper analysis, while time-sensitive data is processed locally at the edge.
Edge computing platforms are used to process data closer to machines rather than relying solely on centralised cloud systems. Edge nodes, which can include industrial PCs, embedded systems, or on-site gateways, act as intermediaries between sensors and enterprise systems, reducing latency by processing data at or near the source.
Edge computing shifts processing closer to machines
Enterprise data generation is increasingly shifting toward distributed environments. Gartner estimates that by 2025, up to 75% of enterprise-generated data will be created or processed outside traditional data centres or centralised cloud environments.
In factory and industrial settings, reliance on cloud-only processing is often limited by network constraints, including bandwidth availability and intermittent connectivity. Edge systems are typically deployed as part of a hybrid architecture, where local processing handles time-sensitive data while centralised systems are used for deeper analysis and long-term trends. Local processing allows systems to continue operating without disruption, particularly in environments where uptime and response times are critical.
In industrial settings, edge systems are used to monitor equipment conditions and support predictive maintenance workflows. This involves analysing inputs such as vibration patterns, thermal readings, and power consumption to identify deviations from normal operating conditions. These systems can trigger alerts or initiate maintenance workflows before equipment failure occurs.
SUSE provides infrastructure for distributed machine management
SUSE provides infrastructure designed to run and manage these workloads at the edge. Its platform combines an operating system with Kubernetes-based orchestration and lifecycle management tools. These components support distributed deployments across industrial environments.
The company’s edge portfolio includes SUSE Edge and SUSE Linux Micro. These products are designed for containerised applications running on gateways, factory systems, and remote equipment. SUSE Linux Micro is built as an immutable operating system, so system components are not modified directly. This reduces configuration drift across large fleets of machines. Containerisation allows applications and their dependencies to run consistently across different environments, supporting deployment across diverse hardware and locations.
SUSE’s use of Kubernetes through its Rancher platform enables centralised control over clusters running in different environments. This allows organisations to deploy and update applications across distributed edge nodes. It also supports rollback when needed while maintaining consistency across different hardware environments.
The platform is also designed to support integration between IT systems and operational technology. For example, machine-level data processed at the edge can be transmitted to enterprise systems such as maintenance platforms or resource planning tools, where it can be utilised to schedule maintenance or adjust production workflows.
Edge-based processing enables machines to continue operating even when network connectivity is limited. Systems can process data locally and apply predefined rules without continuous communication with the central infrastructure. They can also execute actions on site when required.
Industrial use cases include monitoring production equipment and managing distributed assets. They also include deploying updates to software running on machines. These deployments often span multiple facilities or remote sites. Differences in hardware, connectivity, and operating conditions require consistent management across environments.
In manufacturing, edge computing is used alongside smart factory systems to support real-time data processing and machine-level monitoring across production lines.
SUSE, a silver sponsor at the IoT Tech Expo scheduled for May 18–19, 2026, is expected to highlight how edge infrastructure supports distributed machine management and industrial IoT deployments.
(Photo by Markus Stickling)
See also: How digital twins are changing industrial machine operations
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