Network Payload Compression with Zero-MSB Byte Selection
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Solution Overview
Problem
Existing networking technologies are limited in data transmission and reception due to battery-powered devices and limited radio frequency bandwidths, leading to constraints in network throughput and energy efficiency.
Innovation Solution
The implementation of data compression techniques such as 're-indexing' and 'value-mapping', along with removing most significant bits (MSBs) from packet payloads, to enhance network throughput and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression techniques are implemented, then network throughput is improved, but device complexity increases
Solution Approach 1:
The patent segments the data stream into fixed-size blocks (e.g., 64-byte blocks) and processes each block independently through the compression algorithm. This segmentation allows the complex compression logic to be applied in manageable, discrete units rather than continuously, reducing the instantaneous processing complexity while maintaining high throughput. The segmented approach enables parallel processing and simplifies memory management.
Solution Approach 2:
The patent performs preliminary actions by pre-computing compression parameters and statistics for each data block before actual compression. It pre-allocates memory buffers and pre-configures compression parameters based on data characteristics. This preliminary preparation reduces the real-time computational burden during the actual compression phase, effectively managing device complexity while maintaining high productivity.
2Loss of energy
If data compression techniques are implemented, then energy consumption is reduced, but manufacturing precision increases
Solution Approach 1:
The patent dynamically changes compression parameters based on data characteristics and network conditions. It adjusts compression intensity, block size, and algorithm selection according to the entropy of the input data and available bandwidth. This parameter adaptation allows the system to achieve precise compression ratios when needed while consuming less energy during low-traffic periods, effectively balancing energy consumption and compression precision.
Solution Approach 2:
The compression system is designed to be dynamic, adjusting its operation mode based on real-time conditions. It can switch between different compression algorithms (e.g., lossless vs. lossy), adjust compression intensity, and modify block processing parameters dynamically. This dynamic behavior enables the system to optimize the trade-off between energy consumption and compression precision according to actual network demands and data characteristics.
3Productivity
If bandwidth utilization is improved, then network throughput increases, but use of energy increases
Solution Approach 1:
The patent implements self-service mechanisms where the compression system automatically monitors its own energy consumption and adjusts compression intensity accordingly. It uses feedback from power management interfaces to dynamically scale compression operations, ensuring that bandwidth utilization is optimized without exceeding energy budget constraints. This self-regulating approach allows the system to maintain high bandwidth utilization while automatically adapting energy usage to available power resources.
Solution Approach 2:
The system incorporates feedback loops that monitor both network throughput and energy consumption in real-time. Based on this feedback, it dynamically adjusts compression parameters to maintain optimal bandwidth utilization while keeping energy usage within acceptable limits. The feedback mechanism enables the system to learn from past performance and continuously optimize the trade-off between bandwidth efficiency and power consumption.
Data Source
AI summary
Techniques for data compression for efficient network management are described herein. In one example, group(s) of bytes are formed from among input bytes to be compressed. The groups are formed by including bytes having at least a certain number (e.g., three) zero-valued most significant bits (MSBs). A byte of input data having several zero-valued MSBs may be in several groups. A group having the largest product (number of bytes in the group times number of zero-valued MSBs in all bytes in the group) may be selected. A compressed-bytes array may be formed with data of the selected group of bytes, wherein the number of zero-valued MSBs originally present in all of the bytes of the group of bytes has been removed (to compress the array). An uncompressed-bytes array may be formed with bytes of the input bytes of data not in the selected group of bytes. An address-bit array may be formed to indicate the array in which data associated with each of the input bytes of data is stored.