IoT Device Data Feed Normalization for Secure Real-Time Access
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Solution Overview
Problem
Existing systems fail to efficiently manage, transform, and authorize access to data generated by network-connected devices, particularly IoT and M2M devices, and do not accommodate real-time data push and query requirements of end users.
Innovation Solution
A method and system that utilizes a data model to manage device data feeds, employing subscription identifiers, API keys, and rule-based processing to handle and store data, supporting complex rule programming, data normalization, anonymization, and queuing mechanisms to handle data bursts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple IoT devices is collected and stored in a centralized system, then data availability for multiple purposes is improved, but system complexity and data management difficulty increase
Solution Approach 1:
The patent segments the centralized data system into distributed edge computing nodes that process and filter data locally before transmission. Each edge node handles specific device types or functions, dividing the complex centralized system into manageable distributed units that reduce overall system complexity while maintaining data availability.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary layers between IoT devices and the centralized cloud system. These intermediaries perform initial data processing, filtering, and aggregation, reducing the burden on the centralized system while ensuring data is captured and prepared for multiple uses without overwhelming the central infrastructure.
2Speed
If real-time data processing is implemented for all device data, then response time is improved, but energy consumption and computational resources increase
Solution Approach 1:
The patent applies local quality by implementing real-time processing only at the edge computing nodes closest to data sources, while less time-sensitive data is processed asynchronously in the cloud. This selective real-time processing approach ensures critical responses are fast while reducing overall energy consumption by avoiding universal real-time processing of all data streams.
Solution Approach 2:
The patent implements periodic data synchronization between edge nodes and cloud infrastructure, rather than continuous real-time communication. Edge nodes buffer and process data locally, then synchronize with the cloud at scheduled intervals or when thresholds are met, reducing energy consumption from constant communication while maintaining acceptable response times for most operations.
3Loss of information
If comprehensive data collection from all devices is performed, then data completeness is improved, but data privacy risks and security vulnerabilities increase
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) and sensitive data elements at the edge computing nodes before data leaves local networks. This extraction of sensitive information from complete datasets allows the system to maintain data completeness for analytical purposes while eliminating privacy risks associated with storing and transmitting identifiable personal information.
Solution Approach 2:
The patent implements differential privacy levels at different system levels, with edge nodes handling local data processing with enhanced privacy protections, while the cloud receives aggregated, anonymized data suitable for broader analysis. This local quality approach ensures privacy requirements are met at each level of the data pipeline while maintaining overall data completeness for various use cases.
4Adaptability or versatility
If multiple data formats from different devices are normalized to a common format, then data interoperability is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent performs preliminary data format normalization at edge computing nodes before data is transmitted to the cloud. By converting device-specific formats to standardized schemas at the source, the system achieves full interoperability without requiring time-consuming format conversion at centralized processing points, thus reducing overall processing time while maintaining data compatibility across all devices.
Data Source
AI summary
The present invention is directed towards a computer-implemented method and system for managing device data feeds. The computer-implemented method and system comprise using a data model to describe type of data received from the devices, grouping the received type of data based on a data description, and forwarding the device data to a receiver endpoint as directed by the subscription information comprising a receiver endpoint and a rule uniquely identified by the subscription identifier using application programming interface key to manage access to the device data.