IoT Sensor Data Valuation for Targeted Enterprise Distribution
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Solution Overview
Problem
Existing systems inefficiently manage sensor data by under-distributing or over-distributing data based on its value, leading to resource misallocation, errors in data transformation, and failure to secure high-value data.
Innovation Solution
A high-value data integration system identifies high-value sensor data through metadata analysis and creates customized APIs using the EdgeX Foundry™ platform architecture to efficiently distribute and manage sensor data based on valuation scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is distributed to all clients, then no high-value data is missed, but resource allocation becomes inefficient and processing overhead increases
Solution Approach 1:
The patent assigns different valuation scores to different sensor data points based on their specific characteristics and client relevance. High-value data is identified and prioritized for distribution, while low-value data is filtered out. This local differentiation of data quality allows the system to allocate resources efficiently by focusing processing and distribution efforts only on data that has high value to specific clients.
2Productivity
If high-value data is prioritized for distribution, then resource allocation efficiency improves, but risk of missing valuable data increases
Solution Approach 1:
The patent performs preliminary valuation scoring of sensor data before distribution decisions are made. By pre-calculating the value of each data point and pre-identifying which clients would benefit from specific high-value data, the system ensures that no valuable data is missed while maintaining efficient resource allocation. The preliminary classification and client matching prevent last-minute discoveries of valuable data that would require rushed processing.
3Manufacturing precision
If customized APIs are created for each data type, then data management precision improves, but system complexity increases
Solution Approach 1:
The patent creates a universal API framework that can handle multiple data types and valuation scenarios through a common interface. Rather than building separate customized APIs for each data type, the system uses a single versatile API that automatically applies valuation scoring and distribution logic based on the specific data being processed. This multi-functional approach maintains data management precision while avoiding the complexity proliferation of multiple specialized APIs.
4Reliability
If all sensor data is processed and stored, then data availability is maximized, but processing overhead and storage costs increase
Solution Approach 1:
The patent extracts and processes only the high-value portions of sensor data that are relevant to specific clients. Rather than processing and storing all sensor data, the system filters out low-value data at the source and only processes, stores, and distributes data that has been assigned a high valuation score. This extraction approach maximizes data availability for useful information while dramatically reducing processing overhead and storage requirements by eliminating unnecessary data handling.
Data Source
AI summary
A method of integrating high-value sensor data may comprise receiving a sensor reading from an Internet of Things (IoT) sensor, via a gateway network interface device in an enterprise network, receiving a high-value data model including a model field value matching a metadata descriptor associated with the sensor reading, and a valuation score associated with a business use case, and receiving a data access catalog associating the business use case with a client of the enterprise network who is not subscribed to receive the sensor reading. The method may further include identifying the client as a recommended recipient, and transmitting the sensor reading to the recommended recipient if the valuation score associated with the business use case within the high-value data model meets a high-value data threshold value.