Sensor Data Fusion With Threshold Linking for Real-Time Inference
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Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by correlating and fusing sensor data before storage, leading to excessive computational and storage requirements, and lack real-time data processing capabilities.
Innovation Solution
A system and method for sensor data fusion that includes capturing, curating, linking, inferring, and validating sensor data in real-time or near real-time, creating a unique dataset by mathematically linking sensor outputs exceeding a predefined threshold, thereby reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is fused after storage in existing systems, then data availability is maintained, but computational requirements and storage needs become excessive
Solution Approach 1:
The system performs data fusion operations before final storage by creating curated datasets that link sensor outputs exceeding predefined thresholds. This preliminary fusion action reduces the volume of data requiring full storage while maintaining reliability through threshold-based selection and mathematical linking of relevant sensor data points across multiple modalities.
2Loss of information
If all sensor data is stored and processed later, then complete data analysis is possible, but power consumption and storage demands increase
Solution Approach 1:
The system extracts only the most relevant sensor data points by applying predefined thresholds to sensor outputs. Data fusion is performed on this extracted subset rather than all raw sensor data, enabling complete analysis of relevant information while significantly reducing power consumption and storage demands through selective data processing.
3Productivity
If sensor data fusion is performed in real-time, then actionable data is provided immediately, but processing complexity increases
Solution Approach 1:
The system performs preliminary data curation and threshold-based filtering before fusion operations. By pre-processing sensor data to identify and link only those outputs exceeding predefined thresholds, the system enables real-time actionable data generation while managing processing complexity through structured preliminary actions.
4Quantity of substance
If heterogeneous sensor data is fused without correlation, then data volume increases, but data quality and accuracy decrease
Solution Approach 1:
The system performs preliminary correlation analysis by mathematically linking sensor outputs that exceed predefined thresholds before fusion. This ensures that only correlated, relevant heterogeneous sensor data is combined, maintaining data quality and accuracy while managing data volume through selective fusion of provenance-tracked sensor points.
Data Source
AI summary
Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.