Dynamic Sensor Data Generation Pattern for Edge Computing
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Solution Overview
Problem
In edge computing ecosystems, existing technologies face challenges in dynamically controlling sensor data generation patterns to maintain data quality and accuracy, particularly in varying environmental conditions, leading to inefficient data collection and processing.
Innovation Solution
A processor utilizes an AI model to classify sensor data and adjust sensor attributes such as data volume, frequency, and type, by comparing quality values to determine if differences exceed a threshold, thereby optimizing sensor data collection configurations automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors continuously gather data at high frequency, then data quality and accuracy are improved, but energy consumption increases and processing efficiency decreases
Solution Approach 1:
The patent implements dynamic adjustment of sensor data generation frequency based on real-time environmental conditions and data quality requirements. The system transitions from static continuous sampling to adaptive variable-frequency sampling, where the sampling rate is modified according to actual needs, thereby reducing unnecessary energy consumption while maintaining data quality when required
Solution Approach 2:
The system changes the parameter of data generation frequency dynamically. By adjusting this parameter based on environmental conditions and classification quality thresholds, the system optimizes the balance between data quality and energy consumption, avoiding continuous high-frequency sampling when it is not necessary
2Measurement precision
If sensors continuously gather data at high frequency, then data quality and accuracy are improved, but processing efficiency decreases
Solution Approach 1:
The system dynamically adjusts the data collection frequency based on real-time conditions and quality requirements. By making the sampling rate variable rather than fixed, the system processes only necessary data volumes, improving processing efficiency while maintaining data quality when environmental conditions require it
Solution Approach 2:
The patent modifies the data generation frequency parameter adaptively. When classification quality meets thresholds or environmental conditions are stable, the system reduces sampling frequency, thereby decreasing processing load and improving overall processing efficiency without compromising data quality when needed
3Reliability
If sensor data collection frequency is increased, then data quality is maintained in varying conditions, but energy consumption and processing load increase
Solution Approach 1:
The system implements feedback mechanisms where classification results and environmental condition monitoring inform subsequent sampling rate decisions. When data quality metrics and environmental stability indicate sufficient information capture, the system feedback-reduces sampling frequency, maintaining reliability while conserving energy
Solution Approach 2:
The patent dynamically changes the data collection frequency parameter based on feedback from classification quality assessment and environmental condition monitoring. This adaptive parameter adjustment ensures data quality reliability when conditions require it while reducing energy consumption during stable periods
Data Source
AI summary
A processor may receive a first set of sensor data from a plurality of sensors, the plurality of sensors having a sensor attribute associated with a first configuration. The processor may determine, utilizing an artificial intelligence model, a first classification, wherein the first classification is determined based on the first set of sensor data from the plurality of sensors. The processor may receive a second set of sensor data from the plurality of sensors, the second set of sensor data having a second configuration associated with the sensor attribute. The processor may determine a second classification. The processor may identify whether a difference between a first quality value associated with the first classification and a second quality value associated with the second classification exceeds a threshold.