Lightweight AI Layer for Selective Data Transmission
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Solution Overview
Problem
Current big data processing systems for machine learning and artificial intelligence often transmit excessive data over networks, leading to bandwidth congestion, especially in lower bandwidth environments, due to their focus on bulk data collection rather than reducing data size, which can weaken the training process.
Innovation Solution
A lightweight artificial intelligence (AI) layer is implemented at the data source to selectively transmit only necessary data by analyzing and processing data before transmission, using a data modeler that includes a datagram classifier, processor, and controller to filter and compress data based on predictive models, ensuring only relevant data is sent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If bulk data collection is used to improve machine learning model performance, then training data quantity increases, but network bandwidth is congested and data transmission efficiency decreases
Solution Approach 1:
The system performs preliminary data analysis and classification at the data source before transmission. A lightweight AI model processes data locally to predict which data points are most valuable for training, enabling selective transmission of only necessary data to reduce bandwidth consumption while maintaining training effectiveness
Solution Approach 2:
The system extracts and transmits only the most valuable data points for training purposes. By using predictive models to identify important data, the system separates essential training data from redundant information, reducing the overall data volume transmitted over networks while preserving training quality
2Reliability
If all data is transmitted to ensure sufficient training data, then model training quality is maintained, but network bandwidth is wasted on unnecessary data
Solution Approach 1:
The system changes the parameter of data selection from transmitting all data to transmitting only selected data points. By adjusting the selection criteria based on predictive model output, the system optimizes the balance between training data quality and network bandwidth efficiency
Solution Approach 2:
The system uses feedback from the predictive model to guide data selection. The lightweight AI model continuously evaluates data and provides feedback on which data points should be transmitted, enabling dynamic optimization of data transmission based on real-time analysis
3Productivity
If a lightweight AI layer is added to filter and select data before transmission, then data transmission efficiency is improved, but device complexity increases
Solution Approach 1:
The system segments the data processing function into two parts: a lightweight AI model for initial data filtering and selection, and a traditional server-side model for final training. This segmentation allows the complex AI functionality to be distributed, reducing the computational burden on any single device while maintaining overall system effectiveness
Data Source
AI summary
Methods, systems, and apparatuses related to reducing network congestion by analyzing data using a lightweight artificial intelligence (AI) layer prior to transmission are described. An AI model may predictively select data that need not be transmitted and, in some embodiments, further process data to be transmitted. As a result, the total size and amount of data transmitted over the network can be reduced, while the data needs of the receiving device can still be met. For example, data generated by a source application may be received and input into a predictive model, which may generate a prediction output for the data. The data may be pre-processed using a strategy selected based on the prediction output, and the pre-processed data may be transmitted over a network to a server.