Centralized Data Aggregation Platform for 5G Sensor Analytics
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Solution Overview
Problem
Current performance management systems in computer networks face inefficiencies due to manual data processing and analysis of sensor data, leading to inaccuracies and high resource consumption costs, especially in large technology-service providers with distributed sensors.
Innovation Solution
A centralized data-aggregation platform that receives, standardizes, and processes sensor data from various sources, enabling real-time analysis and decision-making through machine learning and artificial intelligence, predicting impacts of changes on performance metrics and optimizing power consumption across server rooms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data processing and analysis methods are used for sensor data, then system complexity remains low, but measurement precision and productivity deteriorate due to inaccuracies and high resource consumption
Solution Approach 1:
The patent introduces a centralized data aggregation platform as an intermediary component between distributed sensors and analysis systems. This platform standardizes sensor data formats, aggregates data from multiple sources, and prepares standardized outputs for machine learning models, thereby improving measurement precision without requiring direct complex interactions between individual sensors and analysis algorithms
Solution Approach 2:
The system architecture is segmented into distinct functional modules: sensor data collection layer, centralized data aggregation platform, machine learning model layer, and application layer. This segmentation allows each component to be optimized independently, improving overall measurement precision while managing system complexity through modular design
2Productivity
If manual data processing methods are used, then device complexity remains low, but productivity deteriorates due to high resource consumption costs
Solution Approach 1:
The system implements self-service through automated machine learning models that continuously analyze sensor data without manual intervention. The platform automatically detects performance metric changes, predicts future states, and generates insights, eliminating the need for manual data processing and significantly improving productivity while reducing resource consumption
Solution Approach 2:
The system performs preliminary actions by pre-processing and standardizing sensor data in the centralized platform before analysis. Machine learning models are trained in advance on historical data to predict future performance metrics, enabling proactive decision-making and reducing the computational resources needed during real-time operations
3Productivity
If real-time predictive analytics are implemented, then productivity improves through optimized resource management, but device complexity increases due to centralized data aggregation and machine learning infrastructure
Solution Approach 1:
The centralized data aggregation platform is designed as a universal system that handles multiple sensor types, standardizes various data formats, and serves multiple analytical functions. This multi-functional platform improves productivity by providing real-time predictive analytics across different domains while consolidating complexity into a single reusable infrastructure rather than requiring separate systems for each function
Data Source
AI summary
A simulator extracts sensor data from multiple systems. The sensor data includes measurements taken by sensors disposed at the multiple systems. The simulator standardizes the sensor data into a common format and classifies the sensor data according to a performance metric. A model of a target system for the performance metric is generated based on the standardized sensor data. The simulator can simulate the impact on the performance metric for a target system based on a simulated change to the multiple systems. The simulator can generate a network interface including a tool that enables end users to interact with the simulation and to determine procedures for mitigating the impact.