Adaptive Sensor Sampling Using Predicted Sparsifying Transforms
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Solution Overview
Problem
Conventional sensor networks face inefficiencies in data transmission and processing due to high resource consumption and latency, as well as the transmission of redundant data, which hinders advanced data analysis and query capabilities.
Innovation Solution
An adaptive compressive sampling scheme is implemented, where a predictive compressive principal component model determines an optimal sparsifying transform and subsampling parameter, placing computationally intensive tasks on a server to reduce bandwidth and resource usage, and allowing for asynchronous sparsifying transform updates between the gateway and server, enabling efficient data processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression methods are used to reduce data transmission, then bandwidth requirements are reduced, but resource consumption increases and processing delay is added
Solution Approach 1:
The system performs preliminary action by determining the sampling scheme at the gateway before data transmission occurs. The gateway analyzes sensor data characteristics and pre-determines optimal sampling parameters, transforming the data into a compressed form prior to transmission. This eliminates the need for post-reception compression processing, reducing both computational resource consumption and processing delay while maintaining reduced data transmission volume.
2Quantity of substance
If conventional compression methods are used to reduce data transmission, then bandwidth requirements are reduced, but processing delay increases
Solution Approach 1:
The gateway performs preliminary compression and sampling scheme determination before data transmission, so that when data arrives at the server it is already in a processed, compressed format. This eliminates post-reception compression processing time, significantly reducing processing delay while maintaining reduced data transmission volume through efficient sampling.
3Productivity
If conventional compression methods are used, then data transmission efficiency is improved, but data analysis capability deteriorates
Solution Approach 1:
The system dynamically changes sampling parameters based on data characteristics and analysis requirements. The gateway determines optimal sampling schemes by analyzing sensor data properties and adjusts sampling rates, patterns, and compression levels accordingly. This adaptive parameter adjustment maintains data transmission efficiency while preserving the information necessary for advanced data analysis and queries, as the sampling scheme is optimized for both compression and analytical utility.
4Use of energy by moving object
If compressive sampling framework is used to overcome compression drawbacks, then resource consumption is reduced, but data reconstruction difficulty increases
Solution Approach 1:
The gateway serves as an intermediary between the sensor and server, performing the compressive sampling and data transformation functions. By placing the computational complexity at the gateway rather than requiring complex reconstruction at the server, the system reduces overall resource consumption. The gateway handles the difficult transformation and compression operations, while the server receives pre-processed data that requires minimal reconstruction effort, thus reducing data reconstruction difficulty.
Data Source
AI summary
A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.