IoT Sensor Voltage Adaptation for Temperature-Dependent Performance
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Solution Overview
Problem
IoT device sensors face performance degradation at high temperatures under nominal voltages, but experience performance enhancement at lower voltages, requiring a mechanism to determine the current temperature dependence for optimal functioning and power consumption.
Innovation Solution
A machine learning algorithm is employed to analyze sensed operating conditions, using a look-up table created from temperature charts to determine the acceptable voltage range for IoT devices, allowing for adaptive operation to ensure desired performance levels and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If IoT device operates at nominal voltage under high temperature, then device can maintain higher power output, but device performance degrades due to temperature dependence reversal
Solution Approach 1:
The system dynamically adjusts the operating voltage of the IoT device based on real-time temperature conditions and historical performance data. The hardware processor continuously monitors temperature sensors and uses machine learning algorithms to determine the optimal voltage level that maintains device performance while allowing operation in previously unacceptable temperature conditions.
Solution Approach 2:
The system changes the operating voltage parameter in response to temperature changes. By storing temperature charts that map temperature conditions to acceptable voltage ranges, the system can adjust voltage to compensate for temperature-induced performance degradation, effectively expanding the operational envelope of the device.
2Reliability
If IoT device operates at lower voltage to maintain performance at high temperature, then device performance is maintained, but power consumption increases
Solution Approach 1:
Instead of continuously operating at reduced voltage to maintain performance, the system uses partial action by only adjusting voltage when temperature conditions require it. The machine learning model predicts when performance degradation will occur and preemptively adjusts voltage, avoiding unnecessary power consumption while maintaining performance only when needed.
Solution Approach 2:
The system uses on-device temperature sensors and machine learning algorithms to autonomously determine optimal operating parameters without external intervention. The hardware processor self-adjusts voltage based on local temperature measurements and stored temperature charts, enabling the device to serve itself in maintaining optimal performance while minimizing power consumption.
3Reliability
If system frequently monitors device operation to ensure performance, then device performance is maintained, but battery life is reduced
Solution Approach 1:
The system performs preliminary action by pre-storing temperature charts that contain pre-computed acceptable voltage ranges for various temperature conditions. During operation, the system only needs to query these pre-computed values based on current temperature readings, rather than performing complex real-time analysis, significantly reducing monitoring overhead and extending battery life.
Solution Approach 2:
The system uses simple, low-power temperature sensors and basic threshold-based monitoring compared to continuous complex performance measurement. By relying on inexpensive temperature sensing and pre-computed lookup tables rather than continuous sophisticated performance monitoring, the system maintains reliability while minimizing power consumption from monitoring activities.
Data Source
AI summary
A system, method and computer program product for operating a low-voltage Internet-of-Things sensor device. The method includes sensing of the temperature dependence at each voltage condition in addition to the actual temperature and voltage. A programmed machine learning model uses the information to decide when it is appropriate to test the device functionality and use the results of different tests to determine when the system should run synchronously or asynchronously through a machine learning predictive algorithm. Based on said one or more sensed operating conditions, the system uses the model to detect a mode of operation of said IoT device indicating IoT device meets an expected level of performance, or a mode indicating said IoT device is not operating according to the expected level of performance. Based on the detected operating condition, the IoT device automatically adapts its operation to ensure a desired level of IoT sensor device performance.