Adaptive Network Data Noise Reduction via ML
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Solution Overview
Problem
The surge in remote device usage has led to overwhelming volumes of network data, straining storage capacities and making it difficult to efficiently extract relevant insights, leading to slow response times and connectivity issues in critical locations, while also increasing the complexity of maintaining user privacy and data security.
Innovation Solution
A mechanism utilizing machine learning to adaptively configure and reconfigure network parameters based on perceived patterns in processed data, dynamically expanding or contracting the set of parameters to be processed, and notifying operators of potential fault events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to adaptively configure network parameters, then fault identification accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses machine learning models to automatically identify patterns in network data and configure parameters without requiring manual analysis. The ML models self-adjust and learn from historical data, enabling the system to serve itself in optimizing network performance and identifying faults.
Solution Approach 2:
The system continuously monitors network performance parameters, feeds this data back into machine learning models, and uses the model outputs to adjust parameter configurations. This closed-loop feedback mechanism enables progressive improvement in fault identification accuracy while adapting to changing network conditions.
2Productivity
If large volumes of network data are stored and processed, then network optimization capability is improved, but storage resources and computational cost increase
Solution Approach 1:
The system extracts only the most relevant patterns and features from large volumes of network data using machine learning techniques. By identifying and isolating significant patterns, the system can optimize network performance without needing to store and process all raw data, thereby reducing storage requirements.
Solution Approach 2:
The system performs preliminary processing of network data by pre-configuring parameters and pre-training machine learning models on historical data. This preparation enables faster processing and identification of faults in real-time without requiring extensive computational resources during actual fault detection events.
3Reliability
If comprehensive network parameters are monitored, then network reliability is improved, but response time decreases
Solution Approach 1:
The system dynamically adjusts which network parameters to monitor based on current network conditions and identified patterns. Using machine learning to predict which parameters are most likely to indicate faults, the system can focus monitoring efforts on critical parameters only when needed, maintaining high reliability while reducing overall response time.
Data Source
AI summary
Systems and methods for identifying correlations for adaptive noise reduction. The system obtains a set of network performance metrics measured during a period of time from a sensor module communicatively coupled to a set of sensors deployed at remote devices. The system may input the set of network performance metrics into a trained model to obtain an optimal set of network performance metrics to be measured during a next period of time, wherein the model is configured to determine a next set of metrics to be measured based on a previous set of metrics. The system identifies a second set of sensors for usage and may generate one or more commands configured to effectuate activation of one or more sensors of the second set of sensors, and deactivation of one or more sensors of the first set of sensors.