Sensor Data Querying With Compressive Sampling and Frequency Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional sensor networks face inefficiencies in data transmission, processing, and storage due to high network traffic, redundant data transmission, and resource-intensive compression methods, which hinder advanced data analysis and increase latency, especially in large-scale applications like smart cities and machine-to-machine communications.
Innovation Solution
A method and system for querying and processing sensor data using compressive sampling schemes, involving frequency decomposition and sparsifying transforms, which allows for efficient data storage and analysis by detecting critical signal conditions and reconstructing signals within specific time windows, while adapting sampling parameters and transforms based on signal sparsity and noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional compression methods are used to reduce data size, then network resource consumption is reduced, but processing time increases and data analysis capability is degraded
Solution Approach 1:
The patent performs frequency decomposition and identifies critical frequency components during the data acquisition phase, storing only these essential components in the database. This preliminary action eliminates the need for time-consuming compression and decompression operations during subsequent processing, as the data is already in a compact, analysis-ready format that preserves critical information while reducing overall data volume.
2Productivity
If conventional compression methods are applied to sensor data, then data transmission efficiency is improved, but advanced data analysis and querying capability are hindered
Solution Approach 1:
The patent transforms sensor data from the time domain to the frequency domain through frequency decomposition, changing the representation parameters of the data. By storing data in terms of frequency components rather than raw time-series values, the system achieves both compression and enhanced analytical capability, as frequency domain representation naturally highlights periodic patterns and critical signal characteristics that are essential for analysis.
3Quantity of substance
If compressive sampling is used to reduce data volume, then network traffic is reduced, but data reconstruction complexity increases
Solution Approach 1:
The patent extracts and stores only the critical frequency components that are essential for signal reconstruction, rather than attempting to reconstruct the entire signal from compressed measurements. By identifying and retaining only the most significant frequency components through frequency decomposition, the system simplifies the reconstruction process while maintaining data integrity and reducing the computational burden compared to traditional compressive sampling reconstruction methods.
4Loss of energy
If traditional compression methods are used, then smaller data sets achieve acceptable compression ratios, but larger data sets require more computational resources
Solution Approach 1:
The patent performs frequency decomposition and critical component identification during the initial data acquisition and storage phase, rather than during subsequent processing. This preliminary action ensures that the data is pre-processed into a compact frequency domain representation with only essential components retained, eliminating the need for computationally intensive compression operations on large data sets later, regardless of their size.
Data Source
AI summary
A computer-executable method and system for querying sensor data of a plurality of sensors is provided. Sensor data is received that comprising sensor data values sampled by a first sensor of said plurality of sensors according to a first compressive sampling scheme. The first compressive sampling scheme can be applied by the first sensor within a sampling time window and the received sensor data corresponds to samples of a signal within the sampling time window. The sensor data is stored in a first database. A frequency decomposition of the signal is computed based on a sparsifying transform associated with the first compressive sampling scheme and the received sensor data. The frequency decomposition comprises one or more frequency components. The one or more frequency components are stored in a second database. A query is received from a client. The query specifies an event that indicates a critical signal condition of a signal. It is detected whether the event exists using the received sensor data or the one or more frequency components.