Adaptive Sensor Sampling for Energy-Efficient Data Quality Control
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Solution Overview
Problem
Existing sensor systems face challenges in achieving energy efficiency while ensuring the quality of data collected, particularly in environments where energy supply is difficult, such as mountainous regions for fire detection.
Innovation Solution
An electronic apparatus and method for controlling a sensor that selectively collects required information based on data variations, optimizing energy usage while maintaining data quality. This involves acquiring sensing data, identifying data variations, determining target data, and adjusting the sampling period accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection frequency is increased to guarantee quality of service, then data quality is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of data collection frequency based on real-time analysis of data variation. The system transitions from static to dynamic sampling by calculating data change rates and adapting the sampling period accordingly, allowing the sensor to collect data more frequently when variations are high and less frequently when variations are low, thus resolving the contradiction between data quality and energy consumption
Solution Approach 2:
The system changes the sampling period parameter dynamically based on data variation characteristics. By computing data variation metrics and adjusting the sampling period parameter in response to these variations, the system optimizes the balance between capturing meaningful data changes and conserving energy in remote sensor deployments
2Loss of energy
If data collection frequency is reduced to save energy, then energy efficiency is improved, but data quality deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors data variation and uses this information to adjust the sampling frequency. The calculated data variation feeds back into the sampling period determination, creating a closed-loop control system that prevents information loss by increasing sampling rate when data changes significantly while maintaining energy efficiency when data remains stable
Solution Approach 2:
The system employs dynamic sampling where the collection frequency adapts to the actual data characteristics rather than operating at a fixed rate. This dynamic approach ensures that energy is not wasted on excessive sampling during stable periods while still capturing critical data changes when they occur, thus maintaining data quality while improving energy efficiency
Data Source
AI summary
The disclosure generally relates to devices and techniques for operating an electronic apparatus that controls a sensor, the devices and techniques including acquiring first sensing data through the sensor, identifying a data variation based on the first sensing data and sensing data for a first time point and a second time point acquired in advance based on a sampling period of the sensor, identifying target data among a plurality of predetermined meaningful data based on the identified data variation and determining a time point at which the target data is to be acquired, and controlling the sampling period based on the determined time point.