Data Compression via Re-indexing and MSB Stripping for Network Management
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Solution Overview
Problem
Networks with battery-powered devices and limited radio frequency bandwidths face limitations in data transmission and reception due to inefficiencies in existing technologies, necessitating improvements in networking infrastructure to enhance throughput and reduce energy consumption.
Innovation Solution
The implementation of 're-indexing' and 'value-mapping' data compression techniques, which involve creating data structures like first-instance arrays, index arrays, and address-bit arrays to remove most significant bits from packet payloads, along with packet header configurations to indicate compression status, to optimize network throughput and energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression techniques are implemented, then network throughput is enhanced and energy consumption is reduced, but device complexity increases due to additional data structures and processing algorithms
Solution Approach 1:
The compression algorithm segments data into distinct categories (first-instance bytes, repeated bytes with indices, compressed bytes with MSB removal) and processes them through separate handling mechanisms. This segmentation enables efficient compression while maintaining manageable complexity through structured organization of different data types.
Solution Approach 2:
The system performs preliminary actions by pre-processing packet data to identify compression opportunities, creating data structures (firstinstancearray, indexarray, addressbitarray) before actual transmission. This preliminary organization enables faster compression operations and reduces real-time processing complexity.
2Loss of energy
If data compression is applied to reduce bandwidth usage, then energy consumption decreases, but processing time increases due to compression and decompression operations
Solution Approach 1:
The compression algorithm applies partial compression by selectively compressing only the packet payload data while leaving the header uncompressed. This partial action reduces processing time and energy consumption compared to full compression, while still achieving significant bandwidth reduction for the majority of the data.
Solution Approach 2:
The system changes parameters dynamically by adjusting the compression level based on data characteristics. The algorithm determines the optimal compression approach (re-indexing, value-mapping, or MSB removal) based on the specific packet content, enabling efficient processing time and energy consumption balance.
3Quantity of substance
If compression algorithms are implemented to reduce data size, then bandwidth efficiency improves, but reliability may be affected due to potential data loss or corruption during compression
Solution Approach 1:
The compression algorithm creates copies of data structures (firstinstancearray, indexarray, addressbitarray) that preserve the original data information in an encoded form. These copies maintain data integrity through systematic encoding that can be perfectly reversed during decompression, ensuring no information loss.
Solution Approach 2:
The system incorporates feedback mechanisms through packet headers that indicate compression status and type. This feedback enables the receiving end to properly decompress the data, ensuring reliable transmission and preventing data corruption through proper protocol adherence.
Data Source
AI summary
Techniques for data compression for efficient network management are described herein. In one example, for each byte of input data, either: (1) a value of that byte is added to a first-instance array if the value of that byte has not yet been seen in the input data; or (2) an index value is added to an index array, wherein the index value points to the appropriate location in the first-instance array. An “address-bit array” is created with one bit for each byte of the input data. Each bit in the address-bit array indicates whether information of a corresponding byte of the input data was put into the first-instance array or the index array. When the input data file is smaller, the index values in the index array tend to be mostly small valued bytes. Accordingly, the number of zero-valued most significant bits (MSBs) present in all bytes may be stripped from the index array, thereby compressing the input data. The number of zero-valued MSBs stripped from all bytes in the index array may be indicated in a packet header.