Mesh Network Traffic Analysis via Passive WiFi Signal Detection
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Solution Overview
Problem
Existing methods for tracking the motion and trajectory of objects with wireless communication devices within predefined areas are complex, costly, and fail to provide reliable, universal analysis of traffic intensity and distribution by transport categories while ensuring data protection.
Innovation Solution
A passive method utilizing WIFI clients integrated in devices to estimate distance from access points, forming a mesh network for local data processing and anonymizing identifiers to maintain anonymity, allowing peer-to-peer communication for traffic analysis without a central server, using machine learning models to predict device location and categorize movement types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central server is used to collect and process traffic data from wireless devices, then comprehensive traffic analysis can be performed, but system complexity and cost increase
Solution Approach 1:
The patent divides the traffic analysis system into distributed mesh network nodes that independently process and share data. Each node performs local traffic analysis and shares results with neighboring nodes, eliminating the need for a centralized server while maintaining comprehensive coverage through collaborative processing.
Solution Approach 2:
Each mesh node autonomously performs traffic data collection, processing, and analysis functions. The nodes self-organize into a functional network where each participant contributes to the overall traffic analysis capability without requiring external coordination or centralized control.
2Measurement precision
If device identifiers are collected for tracking purposes, then accurate motion tracking is achieved, but personal data protection is compromised
Solution Approach 1:
The patent creates anonymized copies of device identifiers that preserve the ability to track motion patterns and calculate traffic metrics while removing personally identifiable information. These anonymized identifiers enable continuous tracking without exposing actual device identities or user personal data.
Solution Approach 2:
The mesh network nodes act as intermediaries that process and anonymize device identifiers before analysis. The system uses temporary, anonymized reference codes that mediate between the need for precise tracking and the requirement for data protection, allowing traffic analysis without direct access to personal device information.
3Loss of information
If active analysis codes are deployed on traffic participant devices, then detailed traffic information can be collected, but device compatibility and user acceptance decrease
Solution Approach 1:
The patent replaces active software agents with passive wireless signal detection. Instead of deploying analysis codes that require device processing power and user permission, the system uses mesh nodes to passively detect and analyze wireless communication signals, achieving comprehensive traffic information collection through physical signal detection rather than software execution.
Solution Approach 2:
The mesh network nodes independently perform all traffic analysis functions without requiring any software installation, configuration, or user interaction on the monitored devices. The system leverages the existing wireless communication infrastructure and signals that devices naturally emit, eliminating compatibility issues while maintaining complete traffic information collection.
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
Enables reliable, cost-effective, and universal analysis of traffic patterns, protecting personal data by processing traffic data locally and anonymously, providing insights into traffic density, speed, and waiting times without central server dependency.
Implementation Method 1
the presence of a wireless communication client is utilized, e.g. WIFI client, integrated of necessity in the device, e.g. smart phone... Based on the signal strength of the WIFI client measured at access points, it is possible to estimate its distance from one or more measuring points
Data Source
AI summary
A method for tracking the movement and trajectory of objects associated with wireless devices, located in a predefined area, wherein transceiver nodes communicating with the monitored wireless communication devices are formed, the objects associated with wireless communication device entering or located in the range of the transceiver nodes are detected by said transceiver nodes and relevant collected data are processed by measuring the strength of the signal emitted by the object during communication by at least two access points, converting the measured signal strength data into a vector set, selecting and filtering out the objects that are communicating but make no movement by evaluating the vector set, and analyzing the movement of the communicating objects changing their place by applying a pre-defined mathematical model. Access points operating according to a small-range “peer-to-peer” (P2P) type wireless communication standard are used as transceiver nodes.