Device Operational Data Confidence Validation Against GNSS Spoofing
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Solution Overview
Problem
Existing systems fail to establish and maintain confidence in the internal time and location data of computerized devices, which can be compromised by unauthorized actors spoofing global navigation satellite system signals, leading to unreliable operations and safety issues.
Innovation Solution
A system that calculates a confidence level for local operational data by comparing it with external data from ecosystem members, using messages like BSM, CAM, and DENM, and takes remedial actions when the confidence level falls below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a computerized device relies on its internal time and location data, then device operation is simplified and autonomous, but the data can be compromised by spoofing signals leading to unreliability
Solution Approach 1:
The system continuously receives external time and location data from multiple ecosystem members and compares it with the device's internal data. This feedback mechanism allows the device to detect discrepancies caused by spoofing signals and adjust its operational confidence level accordingly, maintaining reliability while preserving autonomous operation.
Solution Approach 2:
The system introduces an intermediary verification process where external data from multiple ecosystem members acts as a mediator between the internal data source and the device's operational decisions. This intermediary layer validates the accuracy of time and location data before it is used for critical operations.
2Reliability
If the device continuously validates operational data with external sources, then data reliability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The system performs partial validation by sampling external data from a subset of ecosystem members rather than continuously validating with all possible sources. This partial action approach maintains adequate reliability while significantly reducing computational overhead and system complexity.
Solution Approach 2:
The system dynamically adjusts the validation process parameters based on the confidence level and operational context. When high reliability is required, the system increases validation intensity; when operational simplicity is prioritized, it reduces validation overhead, optimizing the balance between reliability and complexity.
3Device complexity
If the device uses data from a single source, then device complexity is minimized, but vulnerability to spoofing and harmful factors increases
Solution Approach 1:
The system merges data from multiple ecosystem members into a unified validation process. By combining external data sources, the system achieves redundancy that protects against spoofing while maintaining manageable complexity through integrated processing of multiple data streams.
Solution Approach 2:
The system performs preliminary validation of external data sources before incorporating their data into operational decisions. This preliminary action filters out potentially harmful spoofing signals before they can compromise the device, adding a layer of protection without requiring complex real-time analysis of all incoming data.
Data Source
AI summary
Systems, methods, and devices for establishing a confidence level for local operational data for a computerized device that is a member of a technological ecosystem, such as the V2X ecosystem. The systems, methods, and devices may perform operations that include: storing the local operational data; obtaining, e.g., using the communication interface, messages from external devices that are members of the ecosystem, wherein each of the messages comprises external operational data from each external device; determining deviations between the stored local operational data and the external operational data from each message; storing the deviations determined for each message; calculating, based on the stored deviations, a confidence level for the local operational data (e.g., 85% confidence that the local data is accurate); and executing a remedial action when the confidence level is below a threshold for the confidence level.