Wi-Fi Location Data Aggregation for User Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for classifying mobile device users based on movement patterns are inefficient, particularly in distinguishing between employees and visitors in large public or private settings, as they rely on manual data collection, face privacy issues, and are costly to maintain, and fail to differentiate between categories in overlapping physical locations.
Innovation Solution
A system that aggregates location data from mobile devices using Wi-Fi infrastructure to analyze movement patterns, distinguishing between permanent and temporary users by collecting and classifying data through a mobility services server and classification server, which generates aggregated location data with timestamps and location information, and uses training data to categorize users based on dwell time, location dispersion, and floor dispersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data collection methods are used to classify mobile device users, then classification can be performed, but the process becomes inefficient and costly to maintain
Solution Approach 1:
The patent replaces manual mechanical data collection processes with an automated electronic system that uses Wi-Fi infrastructure to passively collect location data from mobile devices. The system automatically aggregates location information, calculates movement metrics (dwell time, location dispersion, floor dispersion), and classifies users without human intervention, thereby eliminating manual effort and significantly improving classification efficiency.
2Measurement precision
If location data aggregation is performed to distinguish user categories, then accurate classification between employees and visitors is achieved, but system complexity increases
Solution Approach 1:
The patent leverages the existing Wi-Fi infrastructure to serve multiple functions: the same Wi-Fi access points used for network connectivity are also utilized for passive location data collection. This multi-functional approach allows the system to gather location information without adding dedicated tracking hardware, thereby maintaining classification accuracy while minimizing system complexity.
Solution Approach 2:
The patent introduces a server as an intermediary component that centralizes the aggregation and analysis of location data from multiple Wi-Fi access points. This intermediary server simplifies the overall system architecture by consolidating data processing functions in one location, making the system easier to manage and maintain despite the complexity of processing multiple data streams.
3Measurement precision
If movement pattern analysis is used to classify users, then differentiation between permanent and temporary users is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for classification from the raw location data, specifically calculating three key metrics: dwell time, location dispersion, and floor dispersion. By extracting and analyzing only these critical movement patterns rather than processing all raw location points, the system achieves accurate user differentiation while minimizing data processing requirements and energy consumption.
4Ease of operation
If automated classification system is implemented, then manual effort and costs are reduced, but initial system setup complexity increases
Solution Approach 1:
The patent implements a self-training mechanism where the classification system automatically learns and adapts to the specific movement patterns of users in the environment. The system collects training data, identifies characteristics of different user categories, and refines its classification algorithms automatically without requiring manual configuration or expert intervention. This self-service capability reduces operational complexity while maintaining high classification accuracy.
Data Source
AI summary
Location data is obtained from signals transmitted by a first plurality of mobile wireless devices in a wireless network, wherein the first plurality of mobile wireless devices are moving within a predefined space, and wherein the location data comprises a plurality of location data time points, each location data time point including a timestamp, a unique mobile wireless device identifier, and location information indicating where in the predefined space an associated mobile wireless device is located. For each mobile wireless device, location data time points are aggregated to generate a set of aggregated location data for each mobile wireless device, and the set of aggregated location data is analyzed to determine characteristics corresponding to time-dependent behavior and location-specific behavior of the corresponding mobile wireless device. A user of each corresponding mobile wireless device is classified into a category of a plurality of categories based on the determined characteristics.