Crowdsourced WiFi Model Break Detection
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Solution Overview
Problem
Existing indoor locationing systems face inaccuracies and increased prediction errors due to changes in wireless access points, such as disappearance or movement, over time, which are not effectively detected by traditional on-site surveys.
Innovation Solution
A method that analyzes wireless data from neighboring access points using crowd-sourced data from multiple users to determine if access points have moved or become unavailable, calculating a ratio of missing access points to identify breaks in the wireless network model, thereby maintaining an accurate indoor locationing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional on-site surveys are used to create indoor locationing models, then initial model accuracy is achieved, but the model becomes inaccurate over time due to undetected access point changes
Solution Approach 1:
The system performs preliminary detection by continuously monitoring wireless data from mobile devices to identify access point changes before they significantly degrade model accuracy. This proactive approach detects breaks in the wireless network model early, allowing for timely updates without requiring frequent comprehensive on-site surveys.
Solution Approach 2:
The system implements a feedback mechanism where wireless data from mobile devices is continuously analyzed to detect changes in access point availability and signal characteristics. When breaks or changes are detected, the system triggers model updates, creating a closed-loop system that maintains accuracy without requiring continuous manual surveys.
2Reliability
If frequent on-site surveys are conducted to maintain model accuracy, then model reliability improves, but operational complexity and costs increase
Solution Approach 1:
The system enables self-service by automatically detecting access point changes using wireless data from mobile devices in the environment. The detection process requires no manual intervention or specialized survey equipment - mobile devices naturally collect the necessary wireless signal data, and the system automatically processes this information to identify model breaks and trigger updates.
Solution Approach 2:
The system uses mobile devices for multiple purposes: they serve as both communication devices for users and as sensing devices for collecting wireless network data. This multi-functionality eliminates the need for specialized survey equipment and personnel, reducing operational complexity while maintaining detection capability.
3Measurement precision
If comprehensive wireless data collection from multiple users is implemented, then detection accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential information needed for break detection from the comprehensive wireless data collected from mobile devices. Instead of processing all raw wireless data, the system focuses on extracting access point identification, signal strength, and availability information, filtering out redundant data while maintaining detection accuracy.
Data Source
AI summary
Aspects of the present disclosure provide techniques for detecting breaks in a wireless network data model. An exemplary method includes determining neighboring access points from scans of network access points in a space. Each neighboring access point occurs together in a scan of a particular level of the space. Wireless data is received from a plurality of mobile devices moving through a space. A set of all access points for the space is identified based on the wireless data. A ratio is derived based on a difference between the neighboring access points and the set of all access points. The ratio represents a percentage of missing access points for the particular level of the space.