Adaptive IIoT Edge Node Provisioning for Context-Aware Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial Internet of Things (IIoT) edge nodes lack adaptability and intelligence, leading to inefficiencies in manual software updates, high maintenance costs, and equipment downtime due to fixed configurations that do not account for changing input data patterns and environmental contexts.
Innovation Solution
An adaptive edge platform that utilizes context-based deployment of functionality, including analytics, allowing edge nodes to dynamically adapt to their environment by using a situation-aware architecture with a smart cube and cubelet system, which can autonomously update and modify logic behavior based on incoming data streams and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If edge nodes use fixed configurations, then device complexity is reduced and ease of manufacture is improved, but adaptability deteriorates and manual software updates are required leading to downtime
Solution Approach 1:
The patent implements dynamic configuration of edge nodes through a situation-aware architecture that automatically adapts to changing environmental contexts and data patterns. The system transitions from static fixed configurations to dynamic reconfigurable architectures where software modules can be loaded, unloaded, and modified based on real-time conditions, eliminating the need for manual software updates and equipment downtime.
Solution Approach 2:
The patent segments the edge node software into modular functional units that can be independently managed and deployed. This segmentation allows the system to load only the necessary software modules based on the current situation, reducing the complexity of managing entire fixed configurations while improving adaptability through selective module deployment.
2Adaptability or versatility
If manual software updates are performed, then adaptability is improved, but productivity deteriorates due to substantial manual effort and equipment downtime
Solution Approach 1:
The patent implements self-service automation where the edge node system automatically monitors its own operational context, detects when software updates or configuration changes are needed, and performs these updates autonomously without human intervention. This eliminates manual software update efforts and prevents equipment downtime, directly improving productivity while maintaining high adaptability.
Solution Approach 2:
The system incorporates continuous feedback mechanisms that monitor environmental conditions, data patterns, and system performance. This feedback loop enables the system to automatically trigger software updates and configuration changes when needed, eliminating the need for manual intervention and preventing productivity losses associated with scheduled maintenance and manual updates.
3Loss of information
If data is sent to cloud for analytics, then analytics capability is improved, but loss of energy deteriorates due to heavy bandwidth utilization
Solution Approach 1:
The patent implements local quality by enabling analytics capabilities to be executed directly at the edge node rather than requiring all data to be transmitted to the cloud. The situation-aware architecture selectively performs analytics locally when appropriate, reducing bandwidth utilization and energy consumption while maintaining comprehensive analytics capability through a hybrid approach that combines local and cloud-based processing.
Solution Approach 2:
The system performs preliminary data processing and filtering at the edge node before transmitting data to the cloud. By pre-processing data locally to extract only the most relevant information, the system reduces the volume of data requiring transmission, thereby decreasing bandwidth utilization and energy consumption while preserving essential analytics capability.
Data Source
Figure 1
Figure 2
Figure 3
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, comprising: 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.