Inference-Guided Data Collection for Low-Bandwidth Dynamic Systems
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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 while striving for accurate data aggregation in dynamically changing environments.
Innovation Solution
A data aggregator system that utilizes inference models to predict data and select paths for data collectors based on uncertainty levels and sensitivity, transmitting reduced-size representations or predictions to minimize data transmission and optimize communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collectors transmit all collected data to the data aggregator, then data accuracy and completeness are improved, but communication bandwidth consumption and energy usage increase
Solution Approach 1:
The patent extracts only the essential information from collected data by using inference models to predict data values. Only when actual data deviates significantly from predictions (exceeding uncertainty thresholds) is the actual data transmitted, rather than transmitting all collected data. This extraction principle reduces communication bandwidth and energy consumption while maintaining data accuracy for critical parameters.
Solution Approach 2:
The patent creates predictive copies of data using inference models that simulate the behavior of data collectors. These predicted data copies are used to replace actual data transmissions in many cases, reducing the need to transmit original data while maintaining system functionality and data accuracy where the predictions are sufficient.
2Loss of time
If data collectors continuously transmit data to the data aggregator, then real-time data availability is improved, but communication bandwidth consumption increases
Solution Approach 1:
The system performs preliminary actions by having the data aggregator build and maintain inference models that predict future data values based on historical patterns. This preliminary prediction capability allows the system to anticipate data values and determine in advance when actual data transmissions are necessary, enabling proactive rather than reactive data collection and reducing unnecessary bandwidth consumption while maintaining real-time availability for critical updates.
Solution Approach 2:
The patent dynamically changes the transmission parameter (whether to transmit data) based on uncertainty thresholds and prediction accuracy. When predicted data falls within acceptable uncertainty bounds, transmission is suppressed; when uncertainty exceeds thresholds, transmission is triggered. This parameter-based decision-making optimizes the balance between data availability and bandwidth consumption.
3Loss of information
If the system characterizes environments in detail through comprehensive data collection, then environmental understanding is improved, but data transmission requirements increase
Solution Approach 1:
The patent applies partial action by selectively characterizing environments based on uncertainty thresholds rather than comprehensively collecting all possible data. The system performs enough data collection and transmission to achieve sufficient environmental understanding for the specific application, without the excessive action of transmitting all available data. This partial characterization approach maintains adequate environmental understanding while reducing transmission requirements.
Data Source
AI summary
Methods and systems for managing data collection in a distributed system are disclosed. To manage data collection, the system may include a data aggregator and a data collector. The data aggregator and may utilize an inference model to predict data based on future measurements performed by data collectors throughout a distributed system without having access to the measurements. The data collectors may be mobile, and the data aggregator may direct the data collectors to various locations. To select paths for the data collectors to follow, the aggregator may utilize the level of uncertainty in predictions, the sensitivities in ranges of data to downstream consumers of the data collected by the data collectors, and/or other types of information. The data aggregator may select the paths for varying goals over time.