Local Event Sensing for Relative Position and Data Trust
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Solution Overview
Problem
There is a growing demand for secure communication between devices, particularly in unmanned aerial vehicles and mobile devices, where it is crucial to confirm the authenticity and reliability of data exchanged, especially regarding local conditions, hazards, or events, but existing technologies lack effective methods to verify the legitimacy and trustworthiness of data in real-time.
Innovation Solution
The system employs digital signatures generated from data captured by sensors during events of interest, allowing devices to verify if they are within a common locality by comparing signatures, thereby confirming the authenticity of received data and estimating relative positions or distances based on consistent sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If devices exchange data regarding local conditions without verification, then communication efficiency is improved, but data authenticity and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by having devices capture and share sensor data about local events before formal communication occurs. This pre-shared contextual information serves as a basis for later verification of data authenticity, allowing devices to efficiently communicate while maintaining reliability through the pre-established local event verification mechanism
Solution Approach 2:
The system implements feedback by having receiving devices verify transmitted data against their own locally captured sensor data. This feedback loop confirms whether the transmitting device was indeed present at the local event, thereby verifying data authenticity while maintaining communication efficiency through automated verification processes
2Reliability
If devices verify data authenticity through local event verification, then data reliability is improved, but system complexity worsens
Solution Approach 1:
Each device performs verification using its own locally captured sensor data without requiring external verification authorities. The devices self-verify authenticity by comparing received data against their own independent observations of the same local events, eliminating the need for complex centralized verification infrastructure
Solution Approach 2:
The system uses universal sensor components already present in modern devices (cameras, microphones, accelerometers) for verification purposes. These existing multi-functional sensors serve both their primary functions and the additional function of providing verification data, avoiding the need for specialized verification hardware and reducing overall system complexity
3Measurement precision
If devices determine relative positions based on sensor data comparison, then position accuracy is improved, but measurement difficulty worsens
Solution Approach 1:
The system replaces complex mechanical positioning systems with sensor data comparison. Instead of using GPS, radar, or other sophisticated positioning hardware, devices determine relative positions by analyzing and comparing their respective sensor observations of shared local events, significantly reducing measurement difficulty while maintaining or improving accuracy
Solution Approach 2:
Local events serve as intermediaries that mediate the position determination process. Rather than devices directly measuring their relative positions through complex interactions, they both observe the same local event and compare their observations, using the event as a common reference point that simplifies the measurement process while improving accuracy
Data Source
AI summary
Devices or systems such as aerial vehicles may determine that they are located within a common locality based on data captured during an event, such as the emission of light, sound or other matter or energy. Where sensors associated with such devices or systems are each determined to have captured data associated with the event, the devices or systems may be determined to have been located within a common locality during the event. The locality may be defined with respect to the data or the devices or systems, e.g., a range associated with the data or the event, on any basis. A relative distance between the devices or systems may be determined based on the data captured during the event. Additionally, where two or more devices or systems are determined to have been located within a common locality, data exchanged therebetween may be trusted by each of such devices or systems.