Wi-Fi Access Point Location via Crowdsourced Signal Correlation
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Solution Overview
Problem
Current Wi-Fi positioning systems are unreliable in dense urban and indoor environments due to their reliance on GNSS for geo-tagging, leading to inaccurate or missing position fixes, and manual database generation is not cost-effective or scalable.
Innovation Solution
A method and system that use electronic devices to collect and store radio signal characteristics from access points, assign correlation values to quantify similarity, and map these values to correlation ranges based on known locations to determine access point locations, enabling accurate positioning even without GNSS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS is used for geo-tagging Wi-Fi scan captures, then position fixes can be obtained in open sky conditions, but positioning fails in dense urban areas or indoors where GNSS signals are blocked
Solution Approach 1:
The system segments the positioning problem into two parts: using GNSS when available (outdoor/open sky) and using Wi-Fi fingerprinting when GNSS is unavailable (indoor/dense urban). This segmentation allows the system to adapt to different environments by switching between positioning methods, resolving the contradiction between reliability in open sky and adaptability to blocked signal environments.
Solution Approach 2:
The patent introduces Wi-Fi access points as intermediary objects for positioning when GNSS is unavailable. By using Wi-Fi scan captures and creating fingerprint databases, the system establishes an alternative positioning mechanism that mediates between the blocked GNSS signals and the need for location determination in indoor and dense urban environments.
2Measurement precision
If manual recording of access point positions is used to generate reference databases, then accurate positioning data can be obtained, but the process is neither cost effective nor easily scalable
Solution Approach 1:
The system implements self-service by enabling mobile devices to automatically collect Wi-Fi scan captures and automatically generate the fingerprint database without manual intervention. The crowd-sourced approach allows numerous devices to contribute data, automatically building and updating the reference database at scale, thus achieving both accuracy and scalability simultaneously.
Solution Approach 2:
The patent changes the parameter of database generation from manual recording to automated crowd-sourced collection. By transforming the data collection process into an automated system that leverages existing mobile device sensors and Wi-Fi capabilities, the system achieves high scalability while maintaining positioning accuracy through the accumulation of diverse real-world data.
3Extent of automation
If Wi-Fi based positioning systems rely on mobile devices to crowd-source and geo-tag lists of access points, then database generation is automated, but positions become skewed toward venue perimeter or outdoors where GNSS is available
Solution Approach 1:
The patent extracts the dependency on GNSS for positioning by using Wi-Fi signal characteristics as the primary basis for location determination. The system separates Wi-Fi fingerprinting from GNSS reliance, allowing indoor and dense urban positioning to be determined purely through Wi-Fi scan captures and pattern matching, thus eliminating the skew toward outdoor locations.
Solution Approach 2:
Instead of using GNSS coordinates to tag Wi-Fi scans (traditional approach), the patent inverts the approach by using Wi-Fi signal patterns to determine location without requiring GNSS tags. This inversion allows the system to accurately position access points and users in environments where GNSS is unavailable, preventing the perimeter-skew problem.
Data Source
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AI summary
A method of determining locations of access points in a venue, one or more electronic devices configured to receive radio signals from the access points, the method comprises: generating and storing, at a server in communication with the one or more electronic devices, a database comprising information received from the one or more electronic devices, the information comprising: values associated with a radio signal characteristic of the radio signals at locations of RF scan capture by the one or more electronic devices and access point identifiers representing access points corresponding to the values, the locations of RF scan capture being unknown at a time of RF scan capture; assigning correlation values to quantify similarity between the values for pairs of the access point identifiers; mapping the correlation values to correlation ranges based on known locations of at least two of the access points; determining the locations of the access points based on the correlation ranges and the known locations.