Network Node Location Estimation Using Reference Clustering
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Solution Overview
Problem
Existing systems face challenges in accurately determining the location of network nodes within a region, especially when incomplete location data is present, as they often rely solely on centroids which do not account for the clustering patterns of other nodes in the region.
Innovation Solution
A location engine is used to access and filter network transaction records with specific location data, aggregating the locations of reference nodes to estimate the location of nodes with missing data, providing a more accurate representation by considering the clustering patterns of different types of network nodes within the region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centroid-based location assignment is used, then the system is simple to implement, but the location accuracy deteriorates because it does not account for node clustering patterns
Solution Approach 1:
The system performs preliminary filtering and aggregation of node locations before final assignment. By pre-processing the location data to identify and remove outliers through statistical filtering, and pre-aggregating locations by node type and region, the system prepares accurate reference data in advance that improves final location estimation without requiring complex real-time processing
Solution Approach 2:
The system applies different location estimation strategies based on local characteristics. Instead of using a uniform centroid approach for all nodes, it identifies clustering patterns specific to different node types and regions, and applies localized aggregation methods that consider the specific spatial distribution characteristics of each node category, thereby improving accuracy for diverse node types
2Measurement precision
If aggregation of multiple node locations is performed, then location accuracy improves, but processing time increases
Solution Approach 1:
The system segments the location aggregation process by node type and region, processing different node categories separately rather than aggregating all nodes uniformly. This segmentation allows for more efficient processing by reducing the complexity of each individual aggregation task and enabling parallel processing of different node types, thereby reducing overall processing time while maintaining accuracy
Solution Approach 2:
The system dynamically adjusts aggregation parameters such as the number of standard deviations for outlier filtering and the weighting factors in location aggregation based on the specific characteristics of each node type and region. By optimizing these parameters, the system achieves high location accuracy with reduced processing iterations, thereby minimizing processing time while maintaining precision
Data Source
AI summary
Disclosed are exemplary embodiments of systems and methods for use in assigning locations to network records. One exemplary system includes a processor coupled to a data structure including network records, each record including a network node identifier and a boundary identifier. The processor is configured to, for each boundary identifier, access each of the network records including the boundary identifier. The accessed records include reference and target network records. Each reference network record includes location data indicative of a location of the network node identified in the reference record within the boundary. The processor is configured to determine an aggregate location as a median of the location data for the network nodes identified in the reference network records and assign the aggregate location to each of the target network records as location data indicative of the location of each network node identified in the target records within the boundary.