Edge Computing Ontology Generation for IoT Anomaly Detection
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Solution Overview
Problem
Managing and configuring large numbers of diverse IoT devices connected to the internet is complex due to the need for manual generation of ontologies that categorize and classify their capabilities, constraints, relationships, and functionality, which is time-consuming and requires domain-specific knowledge.
Innovation Solution
Automatically generating ontologies using data flowing through a network, combining probabilistic models with linguistic data to create a robust ontology, with processing occurring at the edge of the network to reduce traffic and leveraging similarity metrics for anomaly detection and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to generate ontologies for IoT devices, then domain-specific knowledge can be captured accurately, but the process becomes time-consuming and cannot scale to large numbers of devices
Solution Approach 1:
The system enables automated ontology generation where the IoT devices themselves and the network infrastructure provide the data needed for ontology creation. The computing device automatically processes network traffic data, device metadata, and operational logs to generate ontologies without requiring manual domain expert intervention, thus resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The patent replaces the manual mechanical process of ontology creation by domain experts with an automated computational system. The computing device uses algorithms to process network data, identify device patterns, and generate ontological structures automatically, substituting human labor with machine-based automated generation while maintaining scalability
2Productivity
If automated methods are used to generate ontologies from network data, then scalability to large numbers of devices is achieved, but the complexity of processing and analyzing network traffic increases
Solution Approach 1:
The patent segments the ontology generation process into distinct functional modules: network traffic data collection, device metadata extraction, operational log analysis, pattern recognition, and ontology structure generation. This modular segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity
Solution Approach 2:
The computing device acts as an intermediary between the IoT network infrastructure and the ontology output. It collects and processes data from multiple sources (network traffic, device metadata, operational logs) and transforms this complex information into structured ontological representations, simplifying the interface between raw data and final ontology products
3Extent of automation
If ontologies are generated without human authoring, then automation is achieved, but the need for multi-modal data fusion and processing increases
Solution Approach 1:
The patent merges multiple data modalities (network traffic data, device metadata, operational logs) into a unified processing framework. The computing device integrates these diverse data sources and fuses them into coherent ontological structures, managing the complexity of multi-modal data fusion through unified automated processing while achieving high extent of automation
Data Source
AI summary
A method comprises collecting, by a computing device located at an edge of a network, data items corresponding to information transmitted by endpoints using the network, generating, by the computing device, a probabilistic hierarchy using the data items, generating, by the computing device using the probabilistic hierarchy and natural language data, a similarity metric, generating, by the computing device using the probabilistic hierarchy, the natural language data, and the similarity metric, an ontology, detecting, by the computing device using the ontology, an anomaly, and in response to detecting the anomaly, sending a notification.