SAX Filtering for RLTC Time Series Compression on IoT Devices
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Solution Overview
Problem
Existing time series compression methods are inefficient and resource-intensive for IoT devices due to constrained computational and energy resources, and there is a need for faster data compression with minimal signal loss.
Innovation Solution
A method combining symbolic aggregation (SAX) with refined lightweight temporal compression (RLTC) to preprocess data at the source, determining compressible and incompressible patterns, and selectively applying RLTC to reduce computational overhead and improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional compression methods are used on IoT devices, then compression can be performed, but computational resources and energy consumption become excessive
Solution Approach 1:
The patent applies SAX (Symbolic Aggregate Approximation) transformation as a preliminary step before compression. This pre-processing converts raw time series data into symbolic representations, which significantly reduces the computational complexity of subsequent compression operations while maintaining data integrity and enabling efficient compression on resource-constrained IoT devices
Solution Approach 2:
The patent segments the time series data into fixed-length windows and applies SAX transformation to each window independently. This segmentation allows parallel processing and reduces the overall computational burden, making compression feasible on devices with limited processing power and energy resources
2Use of energy by moving object
If lossy compression schemes are used to reduce resource demands, then energy consumption decreases, but signal loss increases
Solution Approach 1:
The patent changes the representation parameters of the time series data by transforming it into SAX symbols with controlled precision. This parameter transformation allows the data to be compressed with minimal information loss while requiring significantly less energy for processing, as the symbolic representation captures the essential patterns without preserving all original data details
3Speed
If fast compression is performed to meet timeliness requirements, then processing speed increases, but compression ratio decreases
Solution Approach 1:
By performing SAX transformation as a preliminary step, the patent prepares the data in a format that enables both fast processing and high compression ratios. The symbolic representation maintains enough information for accurate reconstruction while being inherently more compressible, allowing the system to achieve both speed and efficiency simultaneously
Data Source
AI summary
One example method includes at a source, at a source, performing a symbolic aggregation process on a series of raw data generated and/or collected by the source, to create a series of symbols, inputting, by the source, the series of raw data and the series of symbols to a lossy compression algorithm operating at the source, running, at the source, the lossy compression algorithm to obtain a series of raw values, and a sparse series of raw values, and transmitting, by the source to a target, the series of raw values, and the sparse series of raw values.