Sensor Power Management via Predictive Sampling Rate Adaptation
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Solution Overview
Problem
Current methods for sensor power management are insufficient in reducing sensor power consumption to a desired level, as they do not effectively adapt to the patterns of events being measured.
Innovation Solution
Implementing a predictive data measurement technique that switches between different sampling rates based on identified patterns in sensor data, reducing power usage by adjusting sampling rates during periods of predicted event occurrences and between events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors perform data measurements at a fixed high sampling rate, then measurement precision is maintained, but power consumption increases
Solution Approach 1:
The patent implements dynamic sampling rate adjustment where the sensor operates at different sampling rates based on detected event patterns. During periods with predicted events, the sampling rate increases to maintain measurement precision, while during inter-event periods, the sampling rate decreases to reduce power consumption. This dynamic adaptation resolves the contradiction between maintaining constant measurement quality and reducing overall power usage.
Solution Approach 2:
The system changes the sampling rate parameter based on the detected state of the measured process. By analyzing patterns in the data stream and predicting event occurrences, the system adjusts the sampling rate parameter to match the actual needs of measurement, thereby achieving high precision when necessary and low power consumption when events are not expected.
2Use of energy by moving object
If sensors reduce sampling rate to save power, then power consumption decreases, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary analysis of the data stream to identify patterns and predict future event occurrences. Based on these predictions, it proactively adjusts the sampling rate before events occur, ensuring high sampling rates are applied only during periods when events are expected, thereby maintaining measurement precision while minimizing overall power consumption.
Solution Approach 2:
The system continuously monitors the data stream, detects patterns, and uses this feedback to dynamically adjust the sampling rate. The feedback loop ensures that measurement precision is maintained during critical periods while allowing power consumption to be reduced during non-critical periods, resolving the contradiction between power savings and measurement quality.
3Reliability
If sensors operate continuously at high sampling rates, then data quality is maintained, but energy efficiency worsens
Solution Approach 1:
The system implements periodic evaluation of the data stream to detect patterns and predict event occurrences. Based on these periodic assessments, it schedules high-sampling-rate operation only during predicted event periods, while using lower sampling rates during inter-event periods. This periodic action approach maintains data quality reliability while significantly improving energy efficiency compared to continuous high-rate sampling.
Data Source
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AI summary
Embodiments of the present disclosure provide techniques and configurations for an apparatus to reduce sensor power consumption, in particular, through predictive data measurements by one or more sensors, in one instance, the apparatus may include one or more sensors and a sensor management module coupled with the sensors and configured to cause the sensors to initiate measurements of data indicative of a process in a first data measurement mode, determine a pattern of events comprising the process based on a portion of the measurements collected by the sensors in the first data measurement mode over a time period, and initiate measurements of the data by the one or more sensors in a second data measurement mode. The second data measurement mode may be based on the pattern of events comprising the process. The pattern may indicate a prediction of appearance of events in the process. Other embodiments may be described and/or claimed.