IoT Device Type Assignment via Communication Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current IoT device tracking methods are prone to human error and fail to accurately identify and update devices, leading to potential security issues due to incomplete identification and lack of timely software/firmware updates.
Innovation Solution
A processor-based system that analyzes communication characteristics to automatically assign device types to IoT devices by comparing extracted characteristics with predefined thresholds and utilizing a knowledge graph to determine device types when initial matches are not found, reducing the need for additional information and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used to track IoT devices, then device tracking can be performed, but human error occurs and device identification accuracy deteriorates
Solution Approach 1:
The system enables IoT devices to self-identify by automatically transmitting device information and characteristics to the network. Devices provide their own identification data without requiring manual intervention, eliminating human error in the identification process while maintaining tracking reliability.
Solution Approach 2:
The patent replaces manual mechanical identification processes with automated electronic analysis. The system uses processors to automatically analyze communication characteristics and extract device features, substituting human operators with computational algorithms that provide consistent, error-free identification.
2Measurement precision
If comprehensive device information is collected for accurate identification, then device type assignment accuracy improves, but resource usage and processing overhead increase
Solution Approach 1:
The system extracts only the most relevant communication characteristics from device communications, such as protocol types, packet structures, and transmission patterns. By selectively extracting key features rather than processing all possible device data, the system achieves accurate device type assignment while minimizing processing resource consumption.
Solution Approach 2:
The patent applies different analysis depths to different device characteristics based on their diagnostic value. High-importance features receive detailed analysis while less critical attributes receive minimal processing, optimizing the balance between identification accuracy and resource efficiency through differentiated processing quality.
Data Source
AI summary
According to examples, a processor may receive data pertaining to a communication sent over a network by a device, extract a set of characteristics associated with the communication from the received data, and determine whether the extracted set of characteristics meets a predefined similarity threshold with respect to a previously identified set of characteristics, in which the previously identified set of characteristics is assigned with a certain device type. The processor may also, based on a determination that the extracted set of characteristics meets the predefined similarity threshold with respect to the previously identified set of characteristics, assign the device with the certain device type.