Dynamic Sensor Data Sampling via Surplus Energy Prediction
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Solution Overview
Problem
Existing electronic devices that wirelessly report sensor data often struggle to provide a diverse range of sensor data from various environments, conditions, and usage scenarios, due to their default sampling and transmission schedules, which may not align with the needs of machine-learning models or other evaluation models.
Innovation Solution
A method that predicts the surplus energy in the power source of an electronic device, allowing for a modified schedule that increases the sampling and transmission of sensor data beyond the default schedule, thereby enhancing the availability of relevant sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electronic devices use default sampling and transmission schedules, then power consumption is controlled and device operation is stable, but the availability of sensor data for diverse environments and conditions is insufficient
Solution Approach 1:
The patent implements dynamic scheduling by allowing the electronic device to switch between default schedules and modified schedules based on energy availability. The modified schedule is determined by predicting surplus energy and adjusting sampling rates and transmission frequencies accordingly, making the system adaptable rather than static.
Solution Approach 2:
The patent changes operational parameters (sampling rate, transmission frequency) based on the predicted magnitude of surplus energy. When surplus energy is available, the system increases sampling rates and transmission frequencies to capture more diverse sensor data, thereby resolving the contradiction between stability and data availability.
2Loss of information
If electronic devices increase sampling and transmission of sensor data, then availability of sensor data for model development improves, but power source energy is depleted faster
Solution Approach 1:
The patent performs preliminary prediction of surplus energy before determining the modified schedule. By forecasting the magnitude of surplus energy in advance, the system can plan increased sampling and transmission activities only when energy availability permits, avoiding premature energy depletion.
Solution Approach 2:
The system uses feedback from energy prediction results to dynamically adjust the sampling and transmission schedule. The predicted surplus energy magnitude feeds into the schedule determination process, creating a closed-loop control that balances data collection needs with energy conservation.
3Adaptability or versatility
If electronic devices operate according to modified schedule with increased sampling, then diversity of sensor data for machine-learning models improves, but device complexity increases
Solution Approach 1:
The electronic device autonomously determines its own modified schedule based on its predicted surplus energy without requiring external control. The device self-manages the complexity of schedule switching by using its own energy state information, reducing the need for complex external coordination.
Data Source
AI summary
An electronic device that includes a power source and a wireless transmitter is controlled by a method. The method predicts a magnitude of surplus energy in the power source under the assumption that the electronic device is operated in accordance with a default schedule over a time period. The default schedule defines sampling of sensor data from at least one sensor and transmission of the sensor data by use of the wireless transmitter. The method further determines, based on the magnitude of surplus energy, a modified schedule that results in sampling of an increased amount of sensor data per unit time compared to the default schedule, and configures the electronic device to operate in accordance with the modified schedule for at least part of the time period. The method increases availability of sensor data without compromising the mission of the electronic device.