Wi-Fi Position Tracking via Triggered Probes and Adaptive ML
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Solution Overview
Problem
Current Wi-Fi device tracking methods face challenges due to the chaotic nature of Wi-Fi signals and the limited capacity of 2.4 GHz bands, requiring high sampling rates and inefficient data transfer, necessitating an adaptive and cloud-integrated system for accurate position tracking.
Innovation Solution
A system utilizing Wi-Fi access points as Triggering Routers to broadcast hidden and popular SSID networks, triggering Wi-Fi devices to transmit probes, and employing adaptive machine learning models based on RF measurements from multiple Wi-Fi devices and access points for position estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rate is used to track Wi-Fi devices, then measurement precision is improved, but use of energy and computational load increase
Solution Approach 1:
The patent combines multiple Wi-Fi access points into a cooperative network that jointly tracks devices. By merging the monitoring capabilities of multiple access points and combining their signal measurements, the system achieves accurate position tracking without requiring each individual access point to operate at high sampling rates, thereby reducing overall energy consumption while maintaining measurement precision.
Solution Approach 2:
The patent introduces a central server or cloud platform as an intermediary that collects, processes, and analyzes signal strength data from multiple access points. This intermediary handles the computational burden of tracking algorithms, allowing access points to operate at lower sampling rates while the central system performs sophisticated analysis to maintain measurement accuracy without excessive energy consumption at the device level.
2Ease of operation
If 2.4 GHz band is used for Wi-Fi tracking, then ease of operation is improved, but loss of information increases due to channel occupation
Solution Approach 1:
The patent transitions from monitoring a single 2.4 GHz frequency band to utilizing multiple frequency bands including 5 GHz and other available bands. This dimensional expansion into multiple frequency dimensions allows the system to maintain ease of operation on familiar 2.4 GHz while accessing additional channels in other bands to capture signal information that would be lost in the congested 2.4 GHz spectrum alone.
3Measurement precision
If adaptive machine learning model is used for position estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent places adaptive machine learning algorithms in a central server or cloud platform rather than in individual access points or mobile devices. This intermediary architecture allows sophisticated position estimation with high measurement precision while keeping the complexity centralized rather than distributed, so individual devices remain simple while the system as a whole achieves advanced tracking capabilities.
Data Source
AI summary
The invention provides a method and system for tracking a position of one or more Wi-Fi devices of a plurality of Wi-Fi devices. For tracking the position of the one or more Wi-Fi devices, a Triggering Router triggers the one or more Wi-Fi devices to transmit one of a broadcast probe and a directed probe through broadcasting hidden SSID networks and common SSIDs that increase a probing tendency of the one or more Wi-Fi devices. The plurality of Wi-Fi sniffers, then, collect measurement data associated with the one or more Wi-Fi devices in response to detecting the broadcast probe transmission and the directed probe transmission. Thereafter, the position of the one or more Wi-Fi devices is estimated using an adaptive machine learning model based on an indoor model whose parameters are tuned based on radio frequency (RF) measurements from the plurality of Wi-Fi devices and the measurement data.