Cached Recycle Bins for Adaptive IoT Sensor Sampling
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Solution Overview
Problem
IoT analytics systems face challenges in achieving consistent accuracy due to varying sampling frequencies, leading to increased power and network bandwidth consumption as they continuously adjust sensor transmit frequencies to find an optimal solution.
Innovation Solution
Implementing an IoT analytics engine with cached recycle bins that automatically downsample and upsample sensor data using a finite impulse response (FIR) interpolator, allowing for dynamic adjustments of sampling frequencies to stabilize model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system continuously adjusts sensor transmit frequencies to find optimal sampling frequency, then model accuracy is improved, but power consumption and network bandwidth consumption increase
Solution Approach 1:
The system dynamically adjusts the sampling frequency based on model performance feedback. The analytics engine monitors model accuracy and automatically modifies the sampling frequency to find the optimal balance between data quality and energy consumption, rather than using a fixed frequency throughout
Solution Approach 2:
The system implements a feedback loop where the analytics engine monitors model performance and uses this information to adjust the sampling frequency. This closed-loop control enables the system to learn from past performance and optimize future sampling decisions, reducing unnecessary adjustments and associated energy consumption
2Measurement precision
If the system continuously adjusts sensor transmit frequencies to find optimal sampling frequency, then model accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The sampling frequency is dynamically adjusted based on real-time model performance monitoring. The system transitions from static to adaptive sampling, reducing the number of frequency adjustments needed and consequently lowering network communication overhead and bandwidth consumption
Solution Approach 2:
The feedback mechanism enables the system to learn from previous sampling decisions and model performance, making more informed sampling frequency choices that reduce unnecessary network communications. The analytics engine uses performance data to optimize future sampling strategies, minimizing redundant data transmissions
3Extent of automation
If the system uses cached recycle bins for auto sampling adjustments, then sampling frequency adjustments are automated, but device complexity increases
Solution Approach 1:
The system pre-configures the analytics engine with the capability to automatically adjust sampling frequencies based on stored performance data. By preparing the automated adjustment mechanism in advance, the system reduces the need for manual intervention and complex real-time decision-making processes
Solution Approach 2:
The analytics engine performs self-adjustment of sampling frequencies by automatically monitoring model performance and modifying sampling parameters without external intervention. This self-service capability automates the optimization process while containing complexity within the analytics engine itself
Data Source
AI summary
An approach is provided in which the approach stores, in a cached recycle bin, a set of sensor data that is sent from a sensor at a transmit frequency. The approach samples at least a portion of the set of sensor data from the cached recycle bin based on a sampling frequency and training a machine learning model using the sampled data. In response to detecting that a performance of the machine learning model falls below a threshold during the training, the approach adjusts the sampling frequency and re-sampling at least a portion of the sensor data based on the adjusted sampling frequency. The approach instructs the sensor to adjust the transmit frequency in response to determining that the performance of the machine learning model reaches the threshold using the re-sampled data.