Sensor Data Compression by Interest Level for Bandwidth Limits
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Solution Overview
Problem
The limited wireless transmission bandwidth in satellite systems leads to delays in transmitting large amounts of sensor data, as existing compression methods are inefficient in prioritizing and compressing data based on interest levels.
Innovation Solution
A method and system that analyze sensor data to identify high and low interest data, applying multi-stage lossless compression to high interest data and multi-stage lossy compression to low interest data, prioritizing the transmission of high interest data and optionally discarding or storing low interest data for later transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is transmitted without prioritization, then complete data is delivered, but transmission time increases and bandwidth is wasted
Solution Approach 1:
The patent segments sensor data into distinct categories based on interest levels (high, medium, low interest regions). This segmentation allows differential compression and prioritization strategies to be applied to different data portions, enabling critical data to be transmitted quickly while less critical data is compressed more aggressively or delayed, thus resolving the contradiction between complete data delivery and transmission speed.
Solution Approach 2:
The patent applies different compression qualities and prioritization levels to different regions of the sensor data based on their interest levels. High interest regions receive lossless or low-loss compression with high priority, while low interest regions receive aggressive lossy compression with lower priority. This local differentiation optimizes overall transmission efficiency while maintaining data completeness for critical regions.
2Measurement precision
If lossless compression is applied to all data, then data accuracy is maintained, but compression efficiency decreases
Solution Approach 1:
The patent applies different compression algorithms and quality levels to different regions based on their interest levels. High interest regions are processed with lossless or low-loss compression to maintain accuracy, while low interest regions use aggressive lossy compression to improve efficiency. This localized quality adjustment resolves the contradiction by maintaining precision where needed while maximizing compression efficiency elsewhere.
Solution Approach 2:
The patent divides the sensor data into segments based on interest levels and applies appropriate compression strategies to each segment. This segmentation enables the system to achieve high overall compression efficiency while preserving data accuracy in critical regions, as each segment can be optimized independently according to its importance.
3Ease of operation
If bandwidth is allocated equally to all data, then fair transmission is achieved, but critical data experiences delays
Solution Approach 1:
The patent assigns different priority levels and bandwidth allocation strategies to different data regions based on their interest levels. High interest regions receive preferential treatment with higher priority queues and guaranteed bandwidth allocation, ensuring fast transmission of critical data. Lower interest regions receive standard or best-effort service, maintaining operational simplicity while enabling speed optimization for critical data.
4Quantity of substance
If high compression ratios are applied, then bandwidth requirements decrease, but data quality deteriorates
Solution Approach 1:
The patent applies different compression ratios and quality levels to different regions based on their interest levels. High interest regions use low compression ratios with high quality preservation, while low interest regions use high compression ratios with acceptable quality degradation. This localized quality adjustment achieves high overall compression efficiency while maintaining data quality in critical regions, resolving the contradiction between data volume reduction and quality preservation.
Data Source
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AI summary
Methods and apparatus for compression of generalized sensor data are described. One example method for use in compression of generalized sensor data at a first location for transmission to a second location includes analyzing the sensor data to identify high interest data and low interest data, and compressing the high interest data with a lossless compression algorithm.