Distributed Sensor Network Resource Allocation
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Solution Overview
Problem
In sensor networks, efficiently allocating resources such as waveforms and energy across multiple sensor systems to track objects is challenging due to conflicting resource utilization, especially in distributed environments without a central resource manager.
Innovation Solution
A distributed sensor network architecture where each sensor system receives and compares track data from others, predicts the quality of tracks, and determines resource allocation tasks to minimize cost functions while satisfying resource constraints, using heuristic methods like Tabu search to find near-optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a first waveform is used to track objects better, then tracking quality is improved, but energy consumption increases
Solution Approach 1:
The patent applies partial action by having sensor systems transmit waveforms only when necessary for tracking objects within their field of view. Instead of continuous transmission, the system selectively activates waveforms based on object presence and tracking requirements, thereby reducing overall energy consumption while maintaining adequate tracking quality.
Solution Approach 2:
The system dynamically changes waveform parameters (such as frequency, power level, and transmission timing) based on tracking quality requirements and energy availability. By adjusting these parameters adaptively, the system optimizes the balance between tracking precision and energy expenditure.
2Device complexity
If multiple sensor systems operate independently without a central manager, then system complexity is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
Each sensor system autonomously determines its own resource allocation decisions based on information exchanged with other sensor systems. The distributed architecture allows each node to self-manage waveform selection and energy distribution without requiring centralized control, thereby reducing system complexity while maintaining allocation efficiency through peer-to-peer coordination.
Solution Approach 2:
The patent implements feedback mechanisms where sensor systems share tracking data and resource status information with each other. This distributed feedback loop enables each sensor system to adjust its resource allocation based on the current state of the network, achieving efficient resource distribution without centralized management.
3Quantity of substance
If more objects are observed, then coverage is improved, but processing time increases
Solution Approach 1:
The patent segments the processing workload by dividing the network into multiple autonomous sensor systems, each responsible for a subset of objects within its field of view. This segmentation allows parallel processing of multiple objects across different sensor nodes, thereby reducing the processing time for tracking a large number of objects while maintaining comprehensive coverage.
Data Source
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AI summary
A method of multiple sensor processing includes receiving, at a first sensor system, track data from a second sensor system, comparing track data from the first sensor system to the track data from the second sensor system to determine if a track will be within a field of view of the first sensor system during a time period, determining, at a first sensor system, predicted quality of tracks based on the track data and broadcasting the predicted quality of tracks. The method also includes receiving predicted quality of tracks from the second sensor system and determining a first set of tasks based on the predicted quality of tracks determined by the first sensor system and the predicted quality of tracks received from the second sensor system.