Data Aggregator Inference Model Validation
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Solution Overview
Problem
Existing data collection systems face challenges in managing data transmission efficiently, leading to increased communication bandwidth consumption and energy usage, as they often transmit full datasets rather than reduced-size representations, which can be inaccurate and burdensome.
Innovation Solution
Implementing a data aggregator system that uses inference models to predict future measurements, allowing it to validate data statistics and transmit only accurate representations, thereby reducing data transmission by comparing data statistics from collectors to complementary statistics generated by the aggregator, and requesting actual data only when inaccuracies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full datasets are transmitted from data collectors to data aggregator, then data accuracy is maintained, but communication bandwidth consumption and energy usage increase
Solution Approach 1:
The patent extracts only the essential information (data statistics) from the full dataset at the data collector side, transmitting only this reduced representation to the data aggregator. This extraction principle reduces communication overhead and energy consumption while preserving the necessary information for accurate data representation and inference model validation.
Solution Approach 2:
The patent creates a complementary data statistic at the data aggregator side by running an inference model that replicates the data collector's processing. This copy is then validated against the actual transmitted statistic, allowing the system to verify accuracy without transmitting the full original dataset, thus reducing energy usage while maintaining data integrity.
2Loss of energy
If reduced-size data representations are transmitted, then communication bandwidth is conserved, but data accuracy may be compromised
Solution Approach 1:
The patent implements a feedback mechanism where the data aggregator validates the transmitted data statistic by comparing it against a complementary statistic generated by its own inference model. This feedback loop ensures that reduced-size representations maintain sufficient accuracy, as the validation process detects and flags any significant deviations, allowing the system to request full data when needed.
Solution Approach 2:
The patent changes the parameter representation from full datasets to compressed data statistics (such as averages, minima, maxima, or other aggregate measures). This parameter transformation reduces communication bandwidth consumption while the validation process ensures that the transformed parameters still represent the original data accurately enough for the intended application.
3Measurement precision
If data statistics are validated by comparing with complementary statistics, then data accuracy is ensured, but additional processing is required
Solution Approach 1:
The data aggregator performs self-validation by generating its own complementary data statistic using an inference model and comparing it against the transmitted statistic from the data collector. This self-service approach ensures data accuracy without requiring external verification systems, as each component uses its own processing capabilities to validate the information it receives.
Solution Approach 2:
The system performs preliminary validation by comparing data statistics before committing to using the data for further processing or decision-making. This preliminary check ensures that reduced-size representations meet accuracy requirements upfront, preventing downstream issues and reducing the need for complex post-processing verification mechanisms.
4Loss of energy
If inference models are used to predict future measurements, then data transmission is reduced, but model accuracy must be continuously maintained
Solution Approach 1:
The validation mechanism provides continuous feedback to the inference model by comparing its predictions (complementary statistics) against actual transmitted statistics. When discrepancies exceed acceptable thresholds, the system can trigger model retraining or parameter adjustments, ensuring that the inference model maintains its accuracy over time while continuing to reduce data transmission requirements.
Data Source
AI summary
Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator. The data aggregator may utilize inference models to predict the future operation of data collectors. To validate these inferences, the data aggregator may compare a data statistic (a reduced-size representation of a series of measurements) to a complementary data statistic based on a set of inferences. If the complementary data statistic is determined accurate, the data aggregator may store the inferences as validated data and operate as though it has access to the measurements from the data collector. By doing so, the system may be able to transmit less data, consume less network bandwidth, and consume less energy throughout a distributed system.