Trusted Sensor Fusion for Reliable Shared Environmental Data
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Solution Overview
Problem
Distributed sensor networks face challenges in reliably sharing and fusing data among platforms with different capabilities, owners, and varying environmental conditions, which can lead to inaccurate or unreliable sensor data, especially in critical applications.
Innovation Solution
A system of trust is established among sensor platforms using cryptographic techniques and immutable ledgers to assign and dynamically adjust trust levels based on platform identity, location, and environmental conditions, enabling real-time filtering of sensor data based on trust settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data from multiple platforms with different capabilities and environmental conditions is fused, then situational awareness is enhanced, but data reliability deteriorates
Solution Approach 1:
The patent implements trust levels that differentiate data quality based on source characteristics. Each sensor platform is assigned a trust level (e.g., 1-5 scale) based on its capabilities, environmental conditions, and data quality metrics. This allows the system to weight or exclude data from specific sources locally, rather than uniformly treating all data equally, thus maintaining high situational awareness while filtering out unreliable data sources.
Solution Approach 2:
The patent introduces trust level metrics as an intermediary mechanism between raw sensor data and the fused sensor view. This intermediary layer evaluates data quality based on multiple factors (sensor capabilities, environmental conditions, platform identity) and determines which data to include or exclude from the final fused view, resolving the contradiction between comprehensive data collection and reliability.
2Reliability
If trust levels are dynamically adjusted based on environmental conditions and platform identity, then data reliability is improved, but system complexity increases
Solution Approach 1:
The patent establishes trust levels in advance based on platform identity and inherent characteristics before data fusion occurs. Platforms are pre-assigned trust levels based on their sensor capabilities, ownership verification, and initial environmental assessments. This preliminary establishment of trust metrics simplifies real-time operations by avoiding complex dynamic recalculations for each data point, thus improving reliability without proportionally increasing system complexity.
Solution Approach 2:
The patent implements dynamic trust level adjustment based on changing environmental conditions and data quality metrics. Trust levels are not static but can be modified in response to real-time factors such as environmental sensor readings, platform behavior, and data consistency checks. This dynamic approach allows the system to adapt to changing conditions while maintaining manageable complexity through standardized adjustment protocols.
3Reliability
If sensor data is selectively included or excluded based on trust settings, then data quality is improved, but information loss increases
Solution Approach 1:
The patent implements a graduated trust level system (e.g., levels 1-5) that allows partial inclusion of data from sources with moderate trust levels. Rather than binary inclusion/exclusion, the system can weight data differently based on trust level, allowing some information from lower-trust sources to contribute to the fused view while reducing their influence. This partial action approach maintains data quality by prioritizing high-trust sources while preventing complete information loss from lower-trust sources.
Solution Approach 2:
The patent uses trust levels as a parameter to control data inclusion thresholds. By adjusting the trust level parameter, the system can flexibly control how much data to include or exclude. This parameter-based approach allows optimization between data quality and information loss depending on operational requirements, rather than using fixed inclusion/exclusion rules.
Data Source
AI summary
In an example, a sensor platform includes at least one sensor configured to acquire sensor data about an environment in which the sensor platform is located, a wireless communication interface, and a computing platform. The computing platform can be configured to control the sensor platform to acquire first sensor data using the at least one sensor, acquire second sensor data, via the wireless communication interface, from at least one other sensor platform, and display, via a user interface, a fused sensor view that includes the first sensor data and includes or excludes the second sensor data based on a level of trust assigned to the at least one other sensor platform and a trust setting of the sensor platform.