RL Sensor Network Control for Adaptive Industrial Reporting

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Solution Overview

Problem

Existing sensor control systems in industrial processes produce excessive data, lack global optimization, require costly rule-based configurations, and struggle with dynamic updates, leading to inefficiencies and increased complexity.

Innovation Solution

A sensor control system utilizing reinforcement learning (RL) agents to optimize sensor reporting settings based on process graphs, enabling adaptive, efficient data management and configuration adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If simple periodic report settings and/or threshold/trigger-based reporting are used, then the control system is easy to operate, but excessive sensor data is produced that is not useful for optimal process operation

Engineering Contradiction:

Improveease of operationVSAvoidloss of substance

Core Design Contradiction:

Ease of operationVSLoss of substance

Solution Approach 1:

The control system performs self-optimization through autonomous agents that continuously learn from process data and automatically adjust sensor reporting configurations without human intervention, enabling the system to serve itself in optimizing data collection efficiency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements closed-loop feedback where agents monitor process performance metrics and use this information to dynamically adjust sensor reporting settings, ensuring that data collection is continuously optimized based on actual process needs

Inventive Principle:

Principle #23Feedback

2Measurement precision

If hyperscale IoT sensors are introduced to improve monitoring granularity, then measurement precision is improved, but the control system explodes in scale and complexity

Engineering Contradiction:

Improvemeasurement precisionVSAvoiddevice complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The control system is divided into multiple autonomous agents that each manage specific sensor domains or process areas, allowing the complex system to be broken down into manageable, independent units that can operate and optimize locally while contributing to global optimization

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs dynamic agents that can adapt their behavior and configuration based on real-time process conditions, allowing the control architecture to remain flexible and manageable even as the number of sensors scales to hyperscale dimensions

Inventive Principle:

Principle #15Dynamics

3Ease of operation

If the control system is configured to rely on fixed rules, then ease of operation is maintained, but fine tuning is costly and sub-optimal and requires expert supervision

Engineering Contradiction:

Improveease of operationVSAvoidproductivity

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes operational parameters such as sensor sampling rates and reporting thresholds based on learned patterns and current process conditions, allowing automatic fine-tuning that improves productivity without requiring expert intervention

Inventive Principle:

Principle #35Parameter changes

4Adaptability or versatility

If dynamic priority updates are implemented in complex logics and algorithms, then adaptability is improved, but it becomes difficult to cascade through the system

Engineering Contradiction:

ImproveadaptabilityVSAvoiddevice complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12436525B2Sensor control system for controlling a sensor network

Publication Date: 2025.10.07 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

AI summary

A sensor control system (202) for managing at least a first set of one or more sensors (101) for monitoring a first domain of an industrial process and a second set of one or more sensors (102) for monitoring a second domain of the industrial process, wherein the sensor control system (202) comprises at least a first reinforcement learning, RL, agent (A1) and a second RL agent (A2), wherein the first and second RL agents were trained using reinforcement learning and a process graph (196) representing the industrial process.