Wireless Access Point Location Stability Clustering
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Solution Overview
Problem
Existing wireless positioning systems face challenges in accurately determining the location of mobile devices when using moving wireless access points, as these points often provide unstable signals due to frequent repositioning or movement, leading to inaccurate location estimates.
Innovation Solution
A method is introduced to assess the locational stability of wireless access points by clustering data items based on location and time observations, determining if recent observations are associated with a common cluster and exceeding a threshold period, and merging clusters if they overlap in time and distance, to provide accurate location information to mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If moving wireless access points are used to provide network connectivity, then adaptability and coverage flexibility are improved, but signal stability and location accuracy deteriorate due to frequent repositioning
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical location data of wireless access points before determining current locations. This historical data is used to predict and compensate for potential location changes, allowing the system to maintain location accuracy even when access points move.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the actual locations of wireless access points and comparing them with recorded historical locations. This feedback loop enables the system to detect movements and update location information dynamically, ensuring accurate positioning despite access point mobility.
2Area of stationary object
If location data from moving access points is used for positioning, then coverage area is expanded, but location accuracy deteriorates due to unstable signals
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical location data of wireless access points before determining current locations. This historical data is used to predict and compensate for potential location changes, allowing the system to maintain location accuracy even when access points move.
Solution Approach 2:
The system introduces an intermediary approach by using historical location information as a mediator between the moving access point and the positioning system. This historical data acts as a reference that helps maintain accuracy despite the dynamic nature of access point locations.
3Reliability
If historical location data is collected and analyzed, then location stability is improved, but system complexity increases due to data processing requirements
Solution Approach 1:
The system applies segmentation by dividing the data processing into distinct phases: data collection phase, historical data storage phase, and location determination phase. This segmentation allows the system to handle complexity systematically, processing historical data separately from current positioning operations.
Solution Approach 2:
The system performs preliminary actions by collecting and storing historical location data of wireless access points before determining current locations. This historical data is used to predict and compensate for potential location changes, allowing the system to maintain location accuracy even when access points move.
Data Source
AI summary
An example method includes obtaining a plurality of data items. Each data item includes an indication of a particular location, an indication that a wireless signal from a first access point was observed at that location, and an indication of a time at which the wireless signal from the first access point was observed at that location. The method also includes determining a locational stability of the first access point based on the data items. Determining the locational stability of the first access point includes clustering the plurality of data items into one or more clusters based on the locations indicated in the plurality of data items, determining whether the N most recent data items are associated with a common cluster, and determining whether a time span between the N most recent data items exceeds a threshold period of time.