Distributed Sensor Fusion via Relevance-Based Message Filtering
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Solution Overview
Problem
Existing solutions for distributed sensor networks do not efficiently manage the quantity and frequency of message exchanges between local sensors and a central processor, leading to congestion in communication channels, particularly in radio-electric communications like Wi-Fi networks.
Innovation Solution
A method where local processors only send messages if their contribution is deemed relevant by a central processor, based on a list of selected identifiers, and can unsubscribe from irrelevant messages, reducing unnecessary data transmission and channel congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local processors send all their data messages to the central processor, then complete information fusion is achieved, but communication channel congestion increases
Solution Approach 1:
The patent applies local quality by making each local processor evaluate its own data contribution individually against a relevance criterion. Each processor determines whether its specific data adds value to the global prediction matrix, allowing selective transmission based on local assessment of data quality and relevance rather than uniform transmission from all processors.
Solution Approach 2:
The patent implements partial action by having local processors transmit only a subset of their data - specifically, only those data elements that meet the relevance criterion and contribute meaningfully to the global prediction. This avoids the excessive action of transmitting all data from every processor, reducing overall communication load while maintaining fusion effectiveness.
2Measurement precision
If local processors transmit frequent update messages, then real-time fusion accuracy is improved, but message exchange quantity increases
Solution Approach 1:
The patent changes the parameter of message transmission from frequent fixed-interval updates to event-driven updates based on relevance criteria. Local processors evaluate their data against the global prediction matrix and transmit only when the relevance criterion is met, dynamically adjusting transmission frequency based on actual data value rather than following a fixed schedule.
Solution Approach 2:
The patent implements feedback through the relevance evaluation mechanism where local processors continuously assess whether their data contributes to improving the global prediction matrix. This feedback loop allows processors to adjust their transmission behavior based on the current state of the fusion system, transmitting only when their data provides meaningful improvement.
3Reliability
If all local processors participate in fusion calculations, then fusion comprehensiveness is maximized, but processing complexity increases
Solution Approach 1:
The patent applies local quality by enabling each local processor to independently evaluate the quality and relevance of its own data contribution. Processors assess whether their specific data adds value to the global prediction matrix, allowing the system to maintain comprehensiveness by including relevant contributors while reducing complexity by excluding processors whose data does not meet the relevance criterion.
4Loss of information
If local processors send complete data vectors, then information completeness is maintained, but communication bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential and relevant components of local data vectors that contribute to the global prediction matrix. Rather than transmitting complete data vectors from all processors, the system identifies and transmits only those data elements that meet the relevance criterion, maintaining information completeness for fusion purposes while reducing overall communication bandwidth consumption.
Data Source
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AI summary
The invention concerns a system for processing local information, providing feedback of certain information from a central processor to local processors, thus leaving the local processors to decide on the relevance of the individual contribution of same before transmitting the local information thereof, thus allowing the central processor to obtain all the information it needs in order to perform a real-time merge, while greatly reducing the number of messages transmitted by the local processors.