Adaptive Sampling for Sensor Networks Using Signal Predictability
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Solution Overview
Problem
Existing sensor networks face high power consumption due to sampling and data transmission, particularly for non-periodic but predictable signals, where current predictive methods are ineffective in reducing energy usage.
Innovation Solution
A method and system that adjust sampling schemes based on signal predictability, using prediction algorithms to reduce sampling points in predictable periods and increase in unpredictable periods, and transmitting data only when measurements exceed a predefined threshold, thereby optimizing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If sampling is carried out at a fixed original sampling scheme, then measurement coverage is ensured, but power consumption is high due to unnecessary sampling in predictable periods
Solution Approach 1:
The sampling scheme is transformed from a fixed static configuration to a dynamic adaptive scheme that automatically adjusts sampling frequency based on real-time signal predictability assessment. The system continuously evaluates signal characteristics and modifies sampling points accordingly, reducing sampling density during predictable periods while maintaining adequate coverage during unpredictable periods, thereby resolving the contradiction between energy savings and measurement coverage.
Solution Approach 2:
The system changes the parameter of sampling frequency based on the predictability parameter of the signal. By calculating signal predictability metrics and using them to adjust sampling interval parameters dynamically, the system reduces the number of sampling operations during high-predictability periods (saving energy) while ensuring sufficient sampling during low-predictability periods (maintaining measurement coverage).
2Use of energy by moving object
If predictive methods are used to reduce sampling, then power consumption decreases for periodic signals, but the methods fail for non-periodic predictable signals
Solution Approach 1:
The prediction algorithm is designed to handle multiple signal types universally - both periodic and non-periodic predictable signals. The system uses a general-purpose predictability assessment mechanism that can evaluate any signal pattern, not just periodic ones, enabling the same sampling reduction strategy to work across diverse signal types and thus resolving the contradiction between energy savings and adaptability.
Solution Approach 2:
The system changes the approach from assuming periodic patterns to calculating actual predictability parameters for each signal segment. By using predictability metrics that adapt to different signal characteristics rather than relying on fixed periodic assumptions, the system can effectively reduce sampling for any predictable signal type while maintaining versatility across different infrastructure monitoring applications.
3Loss of energy
If sampling points are reduced in predictable periods, then energy efficiency improves, but sampling density becomes insufficient during unpredictable periods
Solution Approach 1:
The system implements a feedback mechanism where the measured signal predictability is continuously assessed and fed back to adjust the sampling scheme. When predictability is high, sampling is reduced (saving energy); when predictability drops indicating unpredictable behavior, sampling density automatically increases (maintaining measurement precision). This closed-loop feedback resolves the contradiction by dynamically balancing energy efficiency and measurement quality based on actual signal characteristics.
Solution Approach 2:
The sampling density is made dynamic rather than static, allowing the system to adapt sampling frequency in real-time based on signal behavior. During predictable periods, lower sampling density suffices for energy efficiency; during unpredictable periods, the system automatically increases sampling density to maintain measurement precision, thus resolving the contradiction between energy loss and measurement quality.
Data Source
AI summary
A method and a system for determining sampling schemes of a network-connected measurement unit based on time-varying predictability of the measured signals are provided herein. The method may include the following steps: sampling, via a sensor, a metric indicative of a physical property of an infrastructure system, wherein the sampling is carried out over a training period, at an original sampling scheme; determining, based on the training period, in which future time ranges, whether said metric is predictable within a predefined threshold; and adjusting the original sampling scheme, so that the more said metric is predictable in a future time range, the less said future time range is sampled, to yield an updated sampling scheme. In another embodiment, the prediction of the signal may be used to postpone the transmitting of measurements by the local sensor devices whenever the new measurements do not exceed a predefined threshold beyond the predicted values