Multisensor Encoding Models for Redundancy-Aware IoT Estimation
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Solution Overview
Problem
Existing methods for machine learning with IoT devices face challenges in efficiently reducing data dimensions and eliminating redundancy in sensor data from multiple instruments, leading to decreased communication efficiency due to redundant information.
Innovation Solution
A machine learning device that includes an acquisition unit, encoding units, an estimation unit, and adversarial estimation units, trained through machine learning to encode and eliminate redundancy in sensor data from multiple measuring devices, using encoding models and adversarial estimation models to match and mismatch estimated values with correct answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensor data from multiple measuring devices is processed independently through separate neural networks, then each device can perform local processing, but redundant information is generated in the low-dimensional observation data, decreasing communication efficiency
Solution Approach 1:
The patent merges the independent neural networks into a single shared neural network that processes sensor data from multiple measuring devices collectively. This unified approach enables the system to identify and eliminate redundant information across devices while maintaining local processing capability, thereby improving communication efficiency without sacrificing operational independence.
2Quantity of substance
If the number of nodes in the intermediate layer is reduced for dimensionality reduction, then communication data amount is decreased, but information redundancy from multiple devices cannot be effectively eliminated
Solution Approach 1:
The patent introduces a feedback mechanism where the neural network processes sensor data from multiple measuring devices and uses the combined information to identify and eliminate redundancies. The system continuously refines its processing based on the feedback from multiple data sources, enabling effective redundancy elimination while maintaining reduced dimensionality for efficient communication.
3Measurement precision
If sensor data is transmitted without dimensionality reduction, then communication accuracy is maintained, but communication overhead increases significantly
Solution Approach 1:
The patent applies preliminary dimensionality reduction by processing sensor data through a shared neural network before transmission. This preliminary action extracts and eliminates redundant information in advance, reducing the amount of data that needs to be transmitted while preserving essential information accuracy, thereby significantly lowering communication overhead.
Data Source
AI summary
A machine learning device that trains a first encoding model for encoding first sensor data into first code, a second encoding model for encoding second sensor data into second code, and an estimation model for making estimation using the first code and the second code such that an estimation result from the estimation model conforms to correct answer data, trains a first adversarial estimation model that outputs an estimated value of the second code in response to the input of the first code such that the estimated value of the second code estimated by the first adversarial estimation model conforms to the second code outputted from the second encoding model, and trains the first encoding model such that the estimated value of the second code estimated by the first adversarial estimation model does not conform to the second code outputted from the second encoding model.