Unsupervised AP Floor Classification via Air Pressure
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Solution Overview
Problem
Existing systems face challenges in accurately determining the locations of access points (APs) in a geographic area, particularly due to inaccuracies in initial recordings, changes in AP locations over time, and the addition of new APs, which can make it difficult and time-consuming to perform service or maintenance.
Innovation Solution
The implementation of a computer-implemented method that utilizes air pressure sensor data from APs to perform automated localization. This method involves receiving air pressure sensor data, evaluating it using an unsupervised clustering model, and determining the number of floors and the location of each AP within the environment, thereby transmitting this information for accurate AP localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording of access point locations is used at installation time, then initial location data can be captured, but measurement precision deteriorates due to inaccuracies in recording and subsequent changes in AP locations
Solution Approach 1:
The access points automatically determine their own locations using onboard sensors (accelerometers, gyroscopes, magnetometers) and communicate this information to the network controller without requiring manual intervention. This self-localization process continuously updates AP positions, eliminating the need for manual recording and ensuring accurate, up-to-date location data without consuming administrator time.
Solution Approach 2:
The patent replaces manual mechanical recording methods with automated electronic sensing and computation. Sensors embedded in the APs collect environmental data, which is then processed algorithmically to determine precise locations. This substitution of manual processes with automated sensor-based systems continuously maintains accurate location records despite AP movements or installations.
2Measurement precision
If automated localization using air pressure sensor data is implemented, then AP location accuracy is improved, but device complexity increases due to the need for sensors and processing systems
Solution Approach 1:
The air pressure sensors serve multiple functions: they detect floor level changes, enable automated AP localization, and provide data for network optimization decisions. By making the sensors multi-functional, the system achieves accurate localization without adding dedicated complex hardware, as the same sensors used for other purposes now also enable precise floor-based AP tracking.
Solution Approach 2:
The network controller acts as an intermediary that collects air pressure data from APs, processes this information through clustering algorithms, and determines floor locations. This intermediary approach distributes the complexity: simple sensors in APs gather raw data, while the centralized controller handles the complex computational tasks, keeping individual AP hardware simple while achieving sophisticated localization functionality.
3Adaptability or versatility
If unsupervised clustering models are used to evaluate sensor data, then adaptability to dynamic AP locations is improved, but manufacturing precision requirements increase for the clustering algorithm
Solution Approach 1:
The unsupervised clustering model continuously processes new sensor data from APs and automatically adjusts floor location assignments based on observed patterns. This feedback loop enables the system to adapt to dynamic changes in AP positions, new AP installations, or environmental variations without requiring manual reconfiguration. The model learns from ongoing data streams, maintaining high adaptability while achieving sufficient precision through iterative refinement rather than requiring extremely high initial precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate and scalable localization of APs, minimizing user error and allowing for dynamic updates of AP locations, thus improving the efficiency of service and maintenance operations.
Implementation Method 1
receiving air pressure sensor data from a set of access points (APs) deployed in an environment
Data Source
AI summary
Systems and techniques for performing automated access point (AP) localization are described. An example technique includes receiving air pressure sensor data from a set of APs deployed in an environment. The air pressure sensor data is evaluated using an unsupervised clustering model. At least one of (i) a number of floors in the environment or (ii) for each AP, which floor in the environment the AP is located is determined. An indication of at least one of (i) the number of floors or (ii) each AP's floor location is transmitted.