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Keywords: Industrial IoT Edge, Industrial IoT Gateways, Industrial IoT Routers, IPCs, Industrial Edge Servers, Edge AI, ARC Advisory Group.
Overview
The industrial IoT edge is the current focal point for enabling long-standing operational objectives, such as maximizing uptime, optimizing asset utilization, and reducing production costs. These traditional quests are now accompanied by the pursuit of incremental functionality in areas such as mobility, autonomy, and sustainability.
Descent of IT and cloud-native architectures and applications is concurrent with emphasis on the industrial IoT edge as a primary enabler underpinning both legacy and transformative objectives. This descent must be rationalized, however, with the ongoing need to meet the functional requirements of operations that may be foreign to an IT-centric orientation.
Concurrent with the demands for edge-to-cloud integration and edge compute, edge hardware platforms are now adopting cloud-native architectural concepts such as containerization and virtualization. This functionality divorces the traditional co-dependent relationship between hardware and software, allowing each to operate independently and expanding the spectrum of potential platforms for edge functionality. While the deployment advantages are clear, this trend can also represent an architectural conundrum when determining the appropriate platform for both current and future edge installations.
The rise of AI-based edge applications is reversing this trend in some instances due to the need for tight coupling between graphics processors and bare metal operating systems in these installations. As stated by one respondent interviewed as part of ARC’s ongoing research at the edge: “AI is making hardware relevant again.”
The Industrial IoT Edge Blends OT and IT Requirements
Functional requirements at the industrial IoT edge have continually escalated since the industrial Internet concept was first introduced over a decade ago. Initially viewed by some customers as parallel to the data ingestion requirements of the traditional MES layer, descent of enterprise applications intent on mining operational data for process improvement has expanded edge functionality beyond basic edge-to-cloud integration into the realms of data preprocessing, edge compute, and, increasingly, analytics, video, and AI/ML and AR/VR. At the same time, customers are reliant on the edge to overcome cloud limitations in areas such as performance latency, availability, and on-prem operation.
Ongoing expansion of the edge footprint in enterprise architectures, along with the associated increase in edge solutions available from both automation and OT providers, is impacting the selection process for edge-centric applications. Coupled with continued technological developments in both hardware and software, as well as the extended lifecycle of most automation installations, developing good selection criteria for suitable edge execution platforms is increasingly important.
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