WLAN Device Classification Using RSSI Mobility Patterns
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Solution Overview
Problem
Existing solutions for analyzing WLAN user presence cycles are inaccurate due to the inclusion of fixed devices, which skew visitor client counts and dwell times, and fail to account for visitors without location services enabled or those not using specific applications.
Innovation Solution
The method involves using a received signal strength indicator (RSSI) to distinguish between fixed and moving devices, setting a dwell time threshold to exclude fixed devices, and combining RSSI with baselining algorithms to accurately classify devices as fixed or moving, thereby improving the accuracy of presence analysis and health monitoring of fixed devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all devices accessing the WLAN are counted to determine user presence, then the total device count is high, but the accuracy of visitor presence analysis is reduced due to inclusion of fixed devices
Solution Approach 1:
The patent segments devices into two distinct categories: fixed devices and moving devices. This segmentation is achieved by analyzing mobility patterns through RSSI measurements over time. Fixed devices exhibit stable RSSI values within a narrow range, while moving devices show significant RSSI variations. By separating these categories, the system can accurately count only moving devices for visitor presence analysis, excluding fixed devices from the visitor count while maintaining their presence in the network.
Solution Approach 2:
The patent extracts fixed devices from the overall device population by identifying their characteristic mobility patterns. Through RSSI analysis and dwell time calculation, the system isolates fixed devices and removes them from the visitor presence calculation. This extraction process ensures that only devices exhibiting moving patterns contribute to visitor counts and dwell time metrics, significantly improving measurement precision.
2Reliability
If fixed devices are included in presence data, then the dataset is complete, but visitor client counts and dwell times are skewed
Solution Approach 1:
The patent implements a dynamic classification system that continuously monitors RSSI values and updates device categorization based on observed mobility patterns. The system calculates dwell time and analyzes RSSI variation thresholds to dynamically distinguish fixed from moving devices. This dynamic approach ensures reliable visitor metrics while preserving complete device dataset for separate analysis, allowing the system to adapt to changing network conditions and device behaviors.
3Measurement precision
If RSSI baselining is used to identify fixed devices, then device classification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent utilizes parameter changes in RSSI values over time to identify fixed devices. By establishing baseline RSSI ranges and monitoring deviations, the system classifies devices based on their mobility characteristics. This parameter-based approach provides high classification accuracy using standard WLAN measurement capabilities, avoiding the need for complex external sensors or hardware modifications.
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 enhances the accuracy of visitor presence analysis by correctly differentiating between fixed and moving devices, providing more reliable visitor client counts and dwell times, and enabling proactive monitoring of fixed device health.
Implementation Method 1
using a received signal strength indicator (RSSI) to distinguish between fixed and moving devices
Data Source
AI summary
A method for classifying devices may include determining that a device is located at a location for more than a threshold amount of time and identifying that the device is a moving candidate based on a percentage of times a plurality of signal strength values for the device fall inside a baseline interval. The method may also include determining a most frequent pattern set of signal strength values for the device, classifying the device as fixed if the most frequent pattern set of signal strength values for the device includes at least one overlapping interval, and classifying the device as moving if the most frequent pattern set of signal strength values for the device includes at least one non-overlapping interval.