AI-Enhanced Data Sampling for Network Traffic Reduction
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Solution Overview
Problem
Determining the optimal sampling rate for communication devices in communication networks is challenging, leading to high network traffic and resource overload due to typically high sampling rates.
Innovation Solution
A method involving fine-grain sampling followed by machine learning algorithm training to reduce sampling rates while maintaining accuracy, using coarse-grain sampling intervals and machine learning inference to produce accuracy-enhanced samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rates are used to ensure accurate samples, then measurement precision is improved, but network traffic and resource usage increase
Solution Approach 1:
The patent segments the sampling process into two distinct phases: fine-grain sampling at high rates for training purposes, and coarse-grain sampling at lower rates for operational use. This segmentation allows the system to capture detailed patterns during training while reducing resource consumption during normal operation, thereby resolving the contradiction between measurement precision and network traffic volume.
Solution Approach 2:
The patent performs preliminary fine-grain sampling and machine learning model training before transitioning to coarse-grain sampling. By pre-processing the data at high resolution to build an accurate predictive model, the system can subsequently operate at lower sampling rates while maintaining accuracy, thus reducing network traffic without sacrificing measurement precision.
2Measurement precision
If high sampling rates are used to ensure accurate samples, then measurement precision is improved, but communication and processing resources are overloaded
Solution Approach 1:
The patent divides the processing workload into two stages: intensive fine-grain sampling and model training performed preliminarily, followed by lightweight coarse-grain sampling using the trained model. This segmentation transfers the heavy processing burden to an initial training phase, allowing operational devices to function with reduced processing requirements while maintaining high measurement precision.
Solution Approach 2:
The system performs preliminary fine-grain sampling and model training before deployment. By pre-computing the complex processing tasks during the training phase, the operational phase requires minimal processing resources, thus resolving the contradiction between measurement precision and device complexity.
3Loss of energy
If coarse-grain sampling is used to reduce network traffic, then loss of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary fine-grain sampling to train a machine learning model that captures the underlying patterns and relationships in the data. This preliminary high-precision sampling enables subsequent coarse-grain sampling to achieve accurate measurements by leveraging the learned patterns, thus reducing energy consumption without sacrificing measurement precision.
Solution Approach 2:
The machine learning model acts as an intermediary between fine-grain and coarse-grain sampling. It translates detailed fine-grain data into patterns that can be accurately reconstructed from sparse coarse-grain samples, enabling energy-efficient sampling while maintaining measurement precision through the mediating intelligence of the trained model.
Data Source
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AI summary
Monitoring an operational characteristic of a data communication device within a network includes sampling an operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval to produce fine-grain samples of the operational characteristic of the data communication device, training a machine learning algorithm using the fine-grain samples of the operational characteristic of the data communication device, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate, sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval to produce coarse-grain samples of the operational characteristic of the data communication device, and using the machine learning algorithm to process the coarse-grain samples of the operational characteristic of the data communication device to produce accuracy-enhanced samples of the operational characteristic of the data communication device.