Dynamic IoT Device Records for Data Drift Validation
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Solution Overview
Problem
Smart contracts with IoT devices face challenges due to data drift, where changes in data formats and semantics from software, firmware, or hardware updates can lead to failures in executing automated processes, causing ambiguities and mismatches that waste network bandwidth and processing resources.
Innovation Solution
A dynamic IoT device record system that automatically updates to account for data drift, using a machine learning algorithm to validate communications and identify unauthorized updates, ensuring continued execution of smart contracts and enhancing security by monitoring changes and locking out potentially compromised devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a smart contract uses fixed data format expectations, then validation security is improved, but adaptability to device updates deteriorates
Solution Approach 1:
The patent applies dynamics by making the device record mutable and updateable over time. The system extracts current parameters from the IoT device and updates the device record dynamically, allowing it to adapt to data format changes from firmware updates while maintaining validation security through structured parameter extraction and comparison.
Solution Approach 2:
The patent changes parameters by extracting device parameters dynamically and storing them in a device record. When data drift occurs due to firmware updates, the system extracts new parameter values and updates the record, allowing the validation criteria to adapt to changed data formats while maintaining security through systematic parameter management.
2Reliability
If the system validates every transmission against fixed records, then security is improved, but network bandwidth efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by extracting device parameters in advance and storing them in a device record before actual transmissions occur. This pre-extraction allows the system to have validation criteria ready, reducing the need for repeated full validation of every transmission and improving bandwidth efficiency while maintaining security.
Solution Approach 2:
The system performs self-service by automatically extracting parameters from the device and updating the device record without manual intervention. This automated parameter extraction and record updating reduces the overhead of validation processes and improves system efficiency while maintaining security through consistent parameter management.
3Measurement precision
If the system monitors all device parameter changes, then detection accuracy is improved, but processing resources are consumed
Solution Approach 1:
The patent applies extraction by selectively extracting specific device parameters that are relevant to validation and security, rather than monitoring all possible device changes. This targeted extraction of critical parameters maintains detection accuracy while reducing processing resource consumption by focusing only on the most important data points.
Data Source
AI summary
A system includes a memory, and a processor. The processor receives a request to register a device. In response, the processor extracts a set of parameters from the device, and stores a baseline record associated with the device, which includes the set of parameters, in the memory. The processor determines that an update to the device modified the set of parameters. In response, the processor extracts the modified set of parameters from the device, and stores a new record associated with the device, which includes the modified set of parameters, in the memory. The baseline record was used to validate an identity of the device prior to the processor determining that the update modified the set of parameters. The new record is used to validate the identity of the device subsequent to the processor determining that the update modified the set of parameters.