Beacon Position Clustering for Moving Data
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Solution Overview
Problem
Existing location services that rely on crowd-sourced data struggle to accurately determine the position of beacons that may be moving or stationary, as they are not designed to handle conflicting data from mobile beacons.
Innovation Solution
The use of clustering analysis, specifically the k-means clustering algorithm, to group position observations by spatial distance and timestamp, allowing for the identification of moved or moving beacons and eliminating outdated data for more accurate position calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing location services use all crowd-sourced position observations to determine beacon positions, then the quantity of data used is maximized, but the position accuracy deteriorates when beacons are moving
Solution Approach 1:
The patent segments the crowd-sourced position observations into multiple clusters based on spatial distance using k-means clustering. This segmentation separates position data into distinct groups, allowing the system to identify and handle moved beacons (those with positions in multiple clusters) differently from stationary beacons, thereby improving position accuracy without overwhelming complexity
Solution Approach 2:
The patent performs preliminary clustering analysis on all position observations before final position determination. By pre-grouping observations into clusters and identifying moved beacons in advance, the system eliminates outdated data beforehand, ensuring that only relevant position data is used for calculating final beacon positions
2Reliability
If existing location services assume all beacons are stationary, then the system complexity is minimized, but the reliability deteriorates when beacons are actually moving
Solution Approach 1:
The patent transitions from a static assumption (all beacons are stationary) to a dynamic model that can detect and handle moving beacons. By using k-means clustering to identify beacons with positions in multiple clusters, the system adapts to the actual dynamic behavior of beacons, improving reliability while introducing manageable complexity through automated algorithms
Solution Approach 2:
The system performs self-diagnosis by automatically identifying which beacons are moving through the clustering analysis. The algorithm autonomously detects moved beacons by examining whether position observations fall into multiple clusters, eliminating the need for manual classification and enabling the system to self-adjust to moving beacon scenarios
3Measurement precision
If existing location services include all position observations in calculations, then the data utilization is maximized, but the position determination accuracy worsens due to outdated data
Solution Approach 1:
The patent extracts and removes outdated position observations from the calculation set by identifying moved beacons through clustering. By taking out position data from beacons that have moved to different clusters, the system eliminates contradictory and outdated information, improving position determination accuracy while minimizing information loss by retaining data from stationary beacons
Data Source
AI summary
Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data for the beacon is grouped into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the beacon.