Blockchain Sensor Validation Using Confidence Scores
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Solution Overview
Problem
Current validation methods for sensor data in computer systems, particularly in agriculture and supply chains, rely on human-trusted third parties, which can lead to risks of falsification and inconsistencies, and lack geographical and geolocation-dependent validation, compromising data accuracy and trustworthiness.
Innovation Solution
A method utilizing a blockchain network with a cloud application that applies domain-specific validation methods, incorporating geographical and geolocation-dependent information, historical measurement records, and weather data to compute a confidence score for sensor data, ensuring accuracy and trustworthiness by validating data before submission to the blockchain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human-trusted third parties are used for validation, then ease of operation is improved, but reliability deteriorates due to risks of falsification and inconsistencies
Solution Approach 1:
The system enables self-service validation where the blockchain network automatically validates sensor data against stored historical records and domain-specific models without requiring human intervention. The validation process is autonomous, with the system checking confidence scores and geolocation data automatically, eliminating dependency on human-trusted third parties while maintaining high reliability through automated verification.
Solution Approach 2:
The patent replaces the mechanical human-validation system with an automated blockchain-based validation mechanism. Instead of relying on human operators to verify data, the system uses computational algorithms, statistical models, and blockchain's immutable ledger to automatically validate sensor readings, ensuring consistency and eliminating human error or intentional falsification.
2Device complexity
If traditional validation methods are used, then device complexity is reduced, but measurement precision deteriorates due to lack of geographical and geolocation-dependent validation
Solution Approach 1:
The system adds new dimensions of validation by incorporating geographical coordinates, geolocation data, and spatial relationships into the validation process. Instead of validating sensor data in isolation, the system contextualizes measurements within their spatial environment, comparing them against historical data from the same or neighboring locations, thereby significantly improving measurement precision through multi-dimensional verification.
Solution Approach 2:
The system performs preliminary validation actions by pre-storing historical measurement records, domain-specific statistical models, and geolocation data in the blockchain before new sensor data arrives. When validation is needed, the system retrieves these pre-prepared resources and automatically compares them against current measurements, enabling rapid and precise validation without complex real-time computations.
3Productivity
If sensor data is submitted directly to blockchain, then productivity is improved, but reliability deteriorates due to lack of confidence score validation
Solution Approach 1:
The system performs preliminary validation actions by pre-storing historical measurement records, domain-specific statistical models, and geolocation data in the blockchain before new sensor data arrives. When validation is needed, the system retrieves these pre-prepared resources and automatically compares them against current measurements, enabling rapid and precise validation without complex real-time computations.
Solution Approach 2:
The system implements feedback mechanisms where the validation process continuously monitors confidence scores and adjusts its verification intensity accordingly. If a measurement achieves a high confidence score through comparison with historical data and geolocation context, it is rapidly accepted and appended to the blockchain. If confidence is low, the system requests additional verification or rejects the data, ensuring reliability while maintaining high productivity through efficient feedback-based validation.
Data Source
AI summary
A validation method applied to sensor data prior to submitting to a blockchain, a computer program product, and a system for validating chemical data. One embodiment may comprise receiving a sensor captured result at an application, applying the sensor captured result to a domain-specific statistical model of expected range of variability of measured results to extract a distribution of expected sensor values, computing a confidence value in the sensor captured result using the domain-specific statistical model, validating the confidence value against a required threshold of confidence, and submitting the sensor captured result for appending to the blockchain if the confidence level is validated against the threshold of confidence.