Multi-Source Data Compression for Inter-Sensor Redundancy Removal
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Solution Overview
Problem
Existing data compression methods are inefficient in distributed-sensor networks, as they fail to leverage data redundancy across multiple data sources, leading to suboptimal compression ratios and increased transmission overheads.
Innovation Solution
A method that aggregates data from multiple sensors, partitions messages based on relatedness, normalizes signals, and applies two-dimensional compression techniques to identify and remove shared redundancies, resulting in improved compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional single-source compression techniques are applied to distributed-sensor networks, then the compression process is simple to implement, but the compression ratio is suboptimal and data transmission overhead is high
Solution Approach 1:
The patent combines multiple data streams from different sensors into a single aggregated data structure, enabling joint compression processing. The compression algorithm processes all sensor data together rather than independently, identifying and eliminating redundancies across the entire dataset, which significantly improves compression ratios while maintaining reasonable implementation complexity.
Solution Approach 2:
The patent segments the compressed data output into individual sensor contributions, allowing the receiving system to reconstruct original sensor readings. This segmentation approach enables the transmitter to achieve high compression ratios by processing data collectively while preserving the ability to retrieve individual sensor data at the receiver.
2Device complexity
If traditional single-source compression techniques are applied to distributed-sensor networks, then the compression algorithm is simple, but data transmission overhead increases
Solution Approach 1:
By merging multiple sensor data streams into a unified compression process, the patent eliminates redundant transmission of identical or correlated data across the network. The combined compression algorithm identifies patterns and redundancies that span multiple sensors, reducing the total volume of data transmitted while the segmentation mechanism ensures individual sensor data can be recovered when needed.
3Reliability
If data from multiple sensors is compressed independently, then each sensor's data integrity is maintained, but overall compression efficiency is reduced
Solution Approach 1:
The patent segments the compressed bitstream into sensor-specific portions with appropriate metadata and identifiers. This segmentation ensures that each sensor's data can be independently reconstructed and verified for integrity, while the unified compression process maintains high efficiency by exploiting correlations across all sensors during the compression phase.
Data Source
AI summary
One embodiment of the present invention is directed to a method for compressing data generated by multiple data sources. The method includes steps of partitioning data generated by the multiple data sources into data partitions, the data included in each data partition containing inter-data-source redundancies and, for each data partition, compressing the data in the data partition to remove the inter-data-source redundancies.