IoT Data Normalization and Anonymization for Cross-Platform Monetization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumer IoT data is fragmented and disconnected, limiting cross-platform monetization and analysis opportunities, as different manufacturers' devices operate on separate platforms without a unified framework for data aggregation and sharing.
Innovation Solution
A network device within a service provider's network collects, normalizes, and aggregates consumer IoT data from registered devices, creating data portfolios that exclude unique identifiers, providing a uniform format and semantic linkage to generate valuable data products for third-party buyers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If IoT data is collected from multiple manufacturers' devices, then data volume and analysis opportunities increase, but data fragmentation and platform incompatibility worsen
Solution Approach 1:
The patent introduces a data service platform as an intermediary that sits between IoT devices from different manufacturers and data consumers. This platform collects data from diverse devices, normalizes them into a unified format, and makes them available to third parties. The intermediary resolves the platform incompatibility issue by providing a universal interface that abstracts away the underlying device diversity.
Solution Approach 2:
The patent implements a universal data collection framework that can handle multiple IoT device types and manufacturers through standardized protocols. The system provides multi-functional capabilities including data collection from various sources, normalization to common formats, aggregation into portfolios, and distribution to consumers. This universality enables the system to manage data diversity without requiring separate handling for each device type.
2Adaptability or versatility
If consumer IoT data is aggregated across platforms, then cross-platform monetization improves, but data privacy and anonymity concerns worsen
Solution Approach 1:
The patent extracts and removes unique device identifiers from the aggregated data before making it available to third parties. By taking out personally identifiable information and device-specific markers, the system retains the analytical value of the data while eliminating privacy concerns. The extracted identifiers are separated from the data portfolio, allowing cross-platform monetization without compromising consumer privacy.
Solution Approach 2:
The patent transforms data from its original platform-specific format into a normalized, anonymized parameter set that retains analytical meaning but loses identifying characteristics. The system changes the parameters of the data through normalization and aggregation processes, converting raw device-specific measurements into generalized patterns that can be monetized across platforms while maintaining anonymity.
3Measurement precision
If IoT data is normalized and aggregated into portfolios, then data quality and anonymity improve, but processing complexity and computational resources worsen
Solution Approach 1:
The patent segments the complex data processing task into distinct operational phases: data collection from individual devices, normalization to standard formats, aggregation into portfolios, and quality validation. By dividing the processing complexity into manageable segments handled by different system components, the system achieves high data quality without requiring all processing to occur simultaneously in a single complex operation.
Data Source
AI summary
A network device receives a definition for a data product of consumer Internet-of-Things (IoT) data and registers multiple machine-type communications (MTC)-devices for collection of consumer IoT data. The MTC devices provide the consumer IoT data with heterogeneous formats. The registering identifies a profile for each MTC device and particular data types authorized for collection. The network device receives consumer IoT data generated by the multiple MTC devices and extracts the particular data types from the IoT data. The network device normalizes the extracted data to include a uniform data format, and aggregates the normalized IOT data into clusters that exclude device identifiers. The network device constructs the clusters into a data portfolio that meets the definition for the data product.