Adaptive IIoT Edge Provisioning for Changing Analytics Models
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Solution Overview
Problem
Industrial Internet of Things (IIoT) edge nodes lack adaptability and intelligence, leading to inefficiencies and high maintenance costs due to the need for manual updates and static analytics models, which fail to handle changing data patterns and context changes effectively.
Innovation Solution
The implementation of an adaptive edge platform that performs situation analysis to determine required changes in analytical models and functional blocks, automatically provisioning new or updated edge containers with micro containers, including rule sets, complex domain expressions, and protocol decoders, allowing the edge node to adapt to its environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates are performed on edge nodes to handle changing data patterns, then analytics capabilities are maintained, but maintenance costs and downtime increase
Solution Approach 1:
The edge node software is transformed from a static state to a dynamic state through automated provisioning. The system continuously monitors data patterns and automatically provisions software updates without manual intervention, allowing the edge node to adapt to changing conditions while remaining operational. This dynamic approach eliminates downtime associated with manual updates.
Solution Approach 2:
The edge node system performs self-updating through automated provisioning mechanisms. The system autonomously detects when software updates are needed based on changing data patterns and automatically provisions the necessary software components, eliminating the need for manual maintenance operations and associated downtime.
2Device complexity
If static analytics models are used in edge nodes, then device complexity is reduced, but adaptability to changing conditions deteriorates
Solution Approach 1:
The analytics functionality is segmented into modular software components that can be independently provisioned and updated. This modular architecture allows the system to maintain simplicity while gaining adaptability, as only specific analytical modules need to be updated in response to changing conditions rather than the entire software system.
Solution Approach 2:
The edge node is designed with universal provisioning capabilities that enable it to execute multiple different analytics models and software configurations. This multi-functionality allows the same hardware platform to adapt to various analytical requirements without increasing physical complexity, as the versatility is achieved through software provisioning.
3Device complexity
If all data is sent to the cloud for analytics, then centralized processing is simplified, but bandwidth utilization increases
Solution Approach 1:
Analytics processing is distributed with different functions performed at different locations. Edge nodes perform local data filtering, preprocessing, and context-aware analytics, while only essential data is transmitted to the cloud. This local quality approach reduces bandwidth utilization by eliminating the need to transmit all raw data to centralized cloud processors.
Data Source
AI summary
Computer-implemented methods for configuring an Industrial Internet of Things (IIoT) edge node in an IIoT network to perform one or more functions, including: performing a situation analysis to determine a required change in one or more of an analytical model, a runtime component, and a functional block of the IIoT edge node based on a change in the one or more functions; and automatically provisioning a new or updated functional module to the IIoT edge node, based on the situation analysis, the new or updated functional module including one or more components, wherein each component includes at least one of a rules set, a complex domain expression with respect to a process industry, an analytical model, and a protocol decoder.