Wi-Fi Attribute Extraction for Unknown Connected Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to automatically identify and update Wi-Fi attributes of connected devices, especially for new or unknown device models, without relying on labor-intensive manual processes or external databases.
Innovation Solution
A method to automatically extract and generate a maximum set of Wi-Fi attributes from wireless data transmissions, supporting all device types, including unknown models, by monitoring and analyzing layer 2 handshakes and probe request frames, and using machine learning to infer chipset characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual processes or external databases are used to identify Wi-Fi attributes, then accuracy can be maintained for known devices, but the process becomes labor-intensive and cannot automatically support new or unknown device models
Solution Approach 1:
The system enables devices to self-identify their Wi-Fi attributes by automatically extracting information from wireless data transmissions and probe request frames. The machine learning model allows the system to autonomously learn and identify device characteristics without manual intervention or external databases, resolving the contradiction between automation and accuracy.
Solution Approach 2:
The system performs preliminary extraction of Wi-Fi attributes from wireless data transmissions and probe request frames during the connection establishment phase. By capturing and analyzing these frames in advance, the system builds a database of device characteristics that can be automatically matched to identify unknown devices, enabling both automation and precision.
2Adaptability or versatility
If external databases are used to store device information, then comprehensive data can be maintained for known devices, but the system cannot automatically update with new device models and requires manual maintenance
Solution Approach 1:
The system implements a feedback mechanism where Wi-Fi attributes extracted from wireless data transmissions are continuously analyzed and fed back to update the machine learning model. This automatic feedback loop enables the system to adapt to new device models without manual intervention, resolving the contradiction between adaptability and complexity by making the database self-updating through continuous learning from observed device behavior.
Solution Approach 2:
The patent replaces the mechanical system of manual database updates with an automated machine learning system. The machine learning model automatically processes wireless data transmissions, extracts device attributes, and updates the device information database without human intervention. This substitution eliminates manual maintenance complexity while maintaining comprehensive support for all device types including newly released models.
3Productivity
If device identification is performed without automated extraction, then manual processes can be used for verification, but the process becomes time-consuming and cannot keep pace with new device releases
Solution Approach 1:
The system performs continuous extraction and analysis of Wi-Fi attributes from wireless data transmissions without interruption. The machine learning model operates continuously to identify device characteristics and update the database in real-time as new devices connect. This continuous automated process eliminates idle time and manual verification delays, maintaining high productivity while preventing time loss through non-stop automatic operation.
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Wireless data transmissions from a plurality of connected devices of various device types to a plurality of access points are monitored (102). A set of Wi-Fi attributes and corresponding values are extracted (110) from the wireless data transmissions. A maximum set of Wi-Fi attributes and corresponding maximum values are generated (114) for a specific device type of the various device types based on the set of Wi-Fi attributes and corresponding values.