Automated Routes for Network Performance Testing via Spatial Clustering
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Solution Overview
Problem
Current methods for creating network performance testing routes are inefficient, prone to errors, and consume excessive computing and networking resources due to manual route creation and erroneous data handling.
Innovation Solution
An optimization system that utilizes user criteria, network data, and cartographic data to calculate spatial data, extract network statistics, and generate optimized routes for network performance testing, ensuring waypoints are accessible and relevant network characteristics are captured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual route creation is used, then flexibility in route design is maintained, but efficiency and accuracy of route generation deteriorate
Solution Approach 1:
The system pre-calculates and stores optimal routes based on historical network performance data and testing requirements before actual field testing occurs. This allows the routing engine to quickly retrieve and adjust pre-planned routes rather than creating them manually each time, significantly improving efficiency while maintaining adaptability through parameter adjustments.
Solution Approach 2:
The routing system automatically generates and optimizes routes using algorithms that analyze network data, testing objectives, and geographic constraints without requiring manual intervention. The system serves itself by continuously learning from past testing routes and performance data, improving route quality over time while eliminating manual route creation labor.
2Reliability
If manual route creation is used, then human judgment can be applied, but error-proneness and resource consumption increase
Solution Approach 1:
The system implements feedback loops where actual testing results, route performance data, and network condition observations are continuously fed back into the routing algorithm. This allows the system to learn from mistakes and successes, automatically correcting errors and optimizing routes based on real-world performance rather than relying on fallible manual planning.
Solution Approach 2:
The patent replaces manual mechanical route planning with automated computational algorithms that process network data, geographic information, and testing requirements. This substitution eliminates human errors in route creation while the systematic algorithmic approach actually reduces computing resource waste by optimizing paths mathematically rather than through trial-and-error manual adjustments.
3Measurement precision
If comprehensive network data analysis is performed, then route optimization accuracy improves, but computing resource requirements increase
Solution Approach 1:
The system extracts and focuses only on the most critical network performance parameters and geographic features relevant to route optimization, rather than processing all available network data. This selective extraction maintains high route accuracy by concentrating computational resources on the most impactful variables while filtering out redundant information that would waste computing resources.
Solution Approach 2:
The routing system dynamically adjusts the level of data analysis depth based on testing requirements, network conditions, and available computing resources. It changes parameters such as data sampling frequency, analysis granularity, and optimization complexity to balance accuracy needs with resource constraints, achieving high precision when necessary while conserving resources during routine operations.
Data Source
AI summary
A device may receive user criteria associated with a route for network performance testing of a network, network data associated with the network, and cartographic data associated with a location of the network. The device may calculate network spatial data based on the user criteria and the network data, and may perform feature extraction of the network spatial data to calculate network statistics and to extract network event locations. The device may generate waypoint criteria based on the network statistics and the network event locations, and may map the network spatial data to valid roadways identified in the cartographic data to generate mapped data. The device may identify a list of viable waypoints based on comparing the mapped data and the waypoint criteria, and may process the list of viable waypoints, the network data, and the cartographic data, with a clustering model, to generate the route.