OPCUA Server Aggregation Using Hardware Threshold Metrics

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Solution Overview

Problem

Existing systems face inefficiencies and resource wastage due to improper handling of data aggregation by OPCUA-based servers, leading to decreased performance, latency, and security vulnerabilities in industrial networks.

Innovation Solution

A server device classifies itself as a small- or high-configuration device based on hardware metrics, allowing partial or complete data source aggregation, and monitors resource usage to prevent overloading or underutilization, triggering alerts for optimal resource management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If complete aggregation of data source is allowed on any server, then data completeness is improved, but server resource overload and system crashes occur

Engineering Contradiction:

Improvedata completenessVSAvoidserver stability

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The system dynamically changes the aggregation parameter from complete to partial based on server hardware metrics. When server resources are insufficient, the system automatically reduces the scope of data aggregation while maintaining critical data completeness, thus preventing server overload and crashes.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Instead of always performing complete data aggregation, the system applies partial aggregation when server capacity is limited. This principle allows the system to aggregate only essential data subsets based on hardware metrics, preventing resource exhaustion while maintaining system reliability.

Inventive Principle:

Principle #16Partial or excessive action

2Loss of energy

If partial aggregation is imposed on high-configuration servers, then resource wastage is reduced, but data completeness and system performance deteriorate

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoiddata aggregation performance

Core Design Contradiction:

Loss of energyVSProductivity

Solution Approach 1:

The system dynamically adjusts the aggregation parameter based on server configuration. High-configuration servers automatically receive complete aggregation permissions while low-configuration servers receive partial aggregation, ensuring optimal resource utilization without compromising performance on capable systems.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Different aggregation strategies are applied to different servers based on their local hardware characteristics. Each server receives a customized aggregation level matching its capacity, preventing resource wastage on underpowered systems while maximizing data completeness on high-performance systems.

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If hardware metrics threshold is set low, then more servers can aggregate data sources, but resource overload and security vulnerabilities increase

Engineering Contradiction:

Improveserver compatibilityVSAvoidsecurity vulnerabilities

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system uses hardware metrics thresholds as dynamic parameters to determine aggregation permissions. By setting appropriate threshold levels, the system balances server compatibility with security requirements, allowing capable servers full access while restricting weaker servers to partial aggregation, thus reducing security vulnerabilities.

Inventive Principle:

Principle #35Parameter changes

4Device complexity

If hardware resources are not monitored, then system complexity is reduced, but resource wastage and performance degradation occur

Engineering Contradiction:

Improvesystem management complexityVSAvoiddata transmission efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system implements continuous monitoring of hardware metrics as a feedback mechanism. This feedback loop provides real-time information about server resource status, enabling dynamic adjustment of aggregation levels and preventing resource wastage while maintaining manageable system complexity through automated decision-making.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12511131B2Server device to facilitate optimum aggregate data source process unit based on set of hardware threshold metrics

Publication Date: 2025.12.30 HONEYWELL INTERNATIONAL INC

AI summary

Techniques for optimized aggregation of a data source are described. A server device receives a request from a data source for aggregating thereof and compares each of a set of hardware metrics corresponding to hardware configuration of the server device with a corresponding threshold hardware metric. The server device is classified as a small-configuration device or a high-configuration device and complete aggregation of the data source or partial aggregation of the data source is allowed based on the classification. A set of utilized hardware metrics of the server device that corresponds to hardware resources of the server device that is being utilized is determined. One or more operations corresponding to the transmission of data of the data source that is to be performed by the server device is blocked if at least one of the set of utilized hardware metrics is higher than a corresponding threshold operational soft limit.