IoT Device Configuration via Machine Learning Grouping
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Solution Overview
Problem
Current systems for configuring IoT devices face challenges due to inconsistent device information from different types and manufacturers, leading to inefficient resource consumption and data loss from incorrect configurations.
Innovation Solution
A configuration system that automatically configures IoT devices by receiving device data, identifying activation sequences, training models, calculating weights and loss functions, generating device representations, and grouping similar devices to provide tailored configuration data, thereby conserving resources and ensuring accurate data reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual configuration methods are used for each IoT device, then configuration accuracy may be maintained, but resource consumption increases and productivity decreases
Solution Approach 1:
The patent merges multiple similar IoT devices into groups based on their characteristics and behavior patterns. By training a machine learning model on device activation sequences and grouping devices with similar patterns, the system applies configuration settings to entire groups simultaneously rather than individually, thereby improving productivity while maintaining accuracy through the model's learned patterns.
Solution Approach 2:
The patent creates a universal configuration approach that works across different types of IoT devices by using a trained machine learning model to identify patterns and group devices. The model learns from device activation sequences and applies configurations universally to grouped devices, enabling one configuration process to handle multiple device types efficiently.
2Productivity
If automated configuration systems are implemented, then productivity improves, but device complexity increases due to inconsistent device information from different manufacturers
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the configuration system and diverse IoT devices. The model learns from device activation sequences and acts as a mediator that handles the complexity of inconsistent device information from different manufacturers, enabling automated configuration without requiring complex device-specific logic in the main system.
Solution Approach 2:
The patent changes the approach from direct device configuration to learning-based parameter identification. By training the model on device activation sequences and using it to determine configuration parameters for grouped devices, the system simplifies the configuration process despite device diversity, as the model adapts to different device parameters through learning rather than requiring explicit programming for each device type.
3Loss of energy
If device grouping is implemented, then resource consumption decreases, but measurement precision may be affected by generalizing across device types
Solution Approach 1:
The patent performs preliminary actions by training the machine learning model on device activation sequences before actual configuration. The model learns precise patterns from individual device behavior during the training phase, then applies this learned knowledge to grouped devices during configuration, thereby maintaining precision while reducing resource consumption during the actual configuration process.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors configuration results and uses this information to refine the machine learning model. By continuously learning from device responses and configuration outcomes, the system maintains high precision in grouped device configuration while keeping resource consumption low through efficient pattern recognition rather than repeated individual device analysis.
Data Source
AI summary
A device may receive device data identifying activation and usage of different types of IoT devices, and may identify a device activation sequence based on the device data. The device may train a model with the device activation sequence, and may generate output data based on training the model with the device activation sequence. The device may calculate weights and a loss function for the output data, and may retrain the model based on the weights and the loss function to generate a retrained model. The device may generate device representations of the different types of IoT devices using the retrained model, and may determine groups of similar IoT devices based on the device representations. The device may generate configuration data for each of the groups of the similar IoT devices, and may cause the different types of IoT devices to be configured based on the configuration data.