Wireless Sensor Data Prediction Model for Power Optimization
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Solution Overview
Problem
Wireless sensor networks face challenges in power consumption, as existing power-saving mechanisms are ineffective in managing energy usage, particularly in outdoor environments where battery recharging is difficult, due to the trade-off between data sensing frequency and power consumption.
Innovation Solution
A data sensing method that analyzes the trend of collected data to establish a prediction model, dynamically adjusting the sensing behavior by determining whether to collect data based on the periodicity of the data, using a data processing module to output sense commands and update them according to the prediction model, thereby reducing unnecessary data collection and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the sense interval is shorter (sensing frequency is higher), then more sensed data is obtained, but power consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of the sensing interval based on data periodicity detection. The system transitions from a fixed sensing schedule to a dynamic one where the interval adapts according to the detected periodic patterns in the sensed data, optimizing the balance between data collection and power consumption.
Solution Approach 2:
The patent leverages the periodic nature of the sensed data to optimize sensing operations. By detecting and utilizing the periodicity of the data patterns, the system schedules sensing operations to occur at optimal intervals, reducing redundant sensing while maintaining data quality.
2Use of energy by moving object
If the sense interval is longer (sensing frequency is lower), then power consumption is saved, but less sensed data is obtained
Solution Approach 1:
The system dynamically adjusts the sensing interval based on real-time detection of data periodicity. When periodic patterns are detected, the system can extend intervals during predictable phases while maintaining shorter intervals during critical detection phases, thus saving power without sacrificing essential data.
Solution Approach 2:
The system employs feedback mechanisms where sensed data is analyzed for periodicity patterns, and this information feeds back into adjusting future sensing intervals. This closed-loop approach ensures that power savings do not compromise data quality, as the system learns from past data to optimize future sensing schedules.
3Measurement precision
If data collection is performed frequently, then data accuracy is maintained, but battery life is reduced
Solution Approach 1:
By exploiting the periodic characteristics of the sensed data, the system schedules collections at optimal intervals that maintain measurement precision while minimizing overall collection frequency. This reduces battery consumption while preserving the ability to detect meaningful changes in the monitored parameters.
Data Source
AI summary
A wireless sensor network and data sensing method thereof are provided. A prediction model is established according to sensed data. When statistical value of the sensed data is within a user allowable range, the average value of the sensed data is returned to a user based on the prediction model. Alternatively, when the statistical value of the sensed data is beyond the user allowable range, actually sensed data is returned to the user based on the prediction model. In addition, the prediction model may further be dynamically updated according to subsequently received sensed data.