IoT Data Normalization via Unified Device Models
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Solution Overview
Problem
IoT data from consumer devices is often fragmented and disconnected due to different formats, terminology, and measurement units, making it difficult for service providers to collect, store, and present effectively.
Innovation Solution
A dynamic data model is defined for IoT devices, allowing customers to select capabilities from a published listing, map incoming event data to these capabilities for normalization, and submit models for certification, enabling standardized data collection and presentation across various IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If IoT data is collected from multiple consumer devices with different formats and terminology, then the quantity and diversity of data increases, but the complexity of data management and integration increases
Solution Approach 1:
The patent applies homogeneity by mapping diverse IoT data from different devices to a unified data model with standardized schemas, common terminology, and consistent data structures. This allows heterogeneous data sources to be integrated into a homogeneous framework that simplifies management while preserving the quantity and diversity of the underlying data.
Solution Approach 2:
The patent introduces an intermediary layer (the unified data model and mapping service) that sits between diverse IoT data sources and the data management system. This intermediary translates and normalizes data from various formats and terminology into a standardized representation, reducing the complexity of direct data integration while maintaining access to all data sources.
2Adaptability or versatility
If a unified data model is implemented to normalize IoT data, then data integration and utilization improve, but the initial setup and standardization effort increase
Solution Approach 1:
The patent applies preliminary action by pre-defining unified data models, schemas, and mapping rules before actual data integration occurs. The framework establishes standardized templates and certification processes in advance, so that when new IoT data sources are added, they can be quickly mapped to existing models rather than requiring custom integration work for each source.
Solution Approach 2:
The patent creates a universal data model framework that can serve multiple IoT data sources and use cases simultaneously. The unified model is designed to be multi-functional, accommodating various device types and data formats through flexible schemas and mapping capabilities, reducing the need for separate integration efforts for different scenarios.
3Measurement precision
If custom data models are created for each IoT device type, then measurement precision and device-specific accuracy improve, but the overall system complexity and difficulty of management increase
Solution Approach 1:
The patent merges multiple device-specific data models into a unified framework by identifying common patterns and structures across different device types. Instead of maintaining separate models for each device, the system combines them into a hierarchical structure where device-specific characteristics are captured as parameters within a unified schema, reducing the total number of models while preserving measurement precision.
Data Source
AI summary
A network device stores capability designations associated with Internet-of-Things (IoT) devices and receives, from a customer device, one or more of the capability designations associated with a first type of IoT device. The network device receives event data generated by the first type of IoT device and maps the event data to the one or more of the capability designations. The mapping produces normalized IoT data for the first type of IoT device. The network device generates semantic information for the normalized IoT data and assembles a device model for the first type of IoT device. The device model includes the one or more of the capability designations and the semantic information.