Reinforcement-Learning Data Sampling for Bandwidth and Energy Limits

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Solution Overview

Problem

Existing data transmission methods in distributed systems consume excessive communication bandwidth and energy, impacting the performance of computer-implemented services by requiring unnecessary data transfer.

Innovation Solution

A data aggregator uses inference models to predict future measurements and a pattern selection model based on reinforced learning to determine which data collectors to sample, reducing data transmission by treating accurate predictions as actual data and updating the sampling pattern to prioritize collectors with likely inaccuracies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data is transmitted from all data collectors to the data aggregator, then data availability and system reliability are improved, but communication bandwidth consumption and energy usage increase excessively

Engineering Contradiction:

Improvedata availabilityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by using inference models to predict future measurements before actually collecting the data. The pattern selection model predicts which data collectors will provide accurate measurements and pre-determines sampling patterns, allowing the system to avoid unnecessary data transmission while ensuring reliable data availability from key collectors.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a copy of the data collection process by using inference models to generate synthetic predictions of future measurements. These predictions serve as substitutes for actual measurements, allowing the system to proceed with data processing without transmitting all raw data from all collectors, thus reducing energy consumption while maintaining data availability.

Inventive Principle:

Principle #26Copying

2Loss of information

If data is transmitted from all data collectors to the data aggregator, then data completeness is improved, but communication bandwidth is consumed excessively

Engineering Contradiction:

Improvedata completenessVSAvoidcommunication bandwidth

Core Design Contradiction:

Loss of informationVSArea of stationary object

Solution Approach 1:

The system applies local quality by differentiating the treatment of different data collectors based on their predicted accuracy. The pattern selection model identifies which data collectors are likely to provide accurate measurements and focuses data transmission only on those specific collectors. This selective approach maintains data completeness for critical measurements while reducing overall communication bandwidth consumption.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system uses partial action by collecting and transmitting data only from a subset of data collectors rather than all collectors. The pattern selection model determines which partial set of collectors to sample based on predicted measurement accuracy, ensuring sufficient data completeness while significantly reducing communication bandwidth requirements.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of energy

If a sampling pattern is used to reduce data transmission, then communication bandwidth and energy consumption are reduced, but the complexity of the system increases

Engineering Contradiction:

Improveenergy consumptionVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The system achieves multi-functionality by having the pattern selection model serve multiple purposes: it predicts future measurements, determines sampling patterns, identifies reliable data collectors, and guides data transmission decisions. This universal approach reduces system complexity compared to separate specialized components, while still achieving significant energy consumption reductions through intelligent sampling.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Loss of energy

If predictions are treated as actual data, then data transmission is reduced, but prediction accuracy must be maintained to ensure data quality

Engineering Contradiction:

Improveenergy consumptionVSAvoidprediction accuracy

Core Design Contradiction:

Loss of energyVSMeasurement precision

Solution Approach 1:

The system uses feedback by continuously comparing predicted measurements with actual measurements from data collectors. This feedback loop allows the inference models to be refined and improved over time, ensuring that prediction accuracy is maintained at sufficient levels. The feedback mechanism enables the system to trust predictions enough to reduce data transmission while maintaining data quality requirements.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12468781B2System and method for reduction of data transmission by information control with reinforced learning

Publication Date: 2025.11.11 DELL PROD LP

AI summary

Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator and data collectors. The data aggregator may utilize an inference model to predict the future operation of data collectors, and a pattern selection model to sample data from data collectors at a specific frequency and sequence. The pattern may specify that some data collectors are not to be sampled at various points in time. By doing so, the system may be able to transmit less data, consume less network bandwidth, and consume less energy throughout a distributed system while still providing access to aggregated data.