Group Testing for Distributed Sensor Data Retrieval
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Solution Overview
Problem
Current methods for retrieving correlated data from distributed sources, such as sensor networks, face inefficiencies due to bottlenecks in data-gathering and high complexity in encoding and decoding processes, particularly in many-to-one network topologies, which restrict per-node throughput and increase latency.
Innovation Solution
A method involving group testing strategies, where a group of distributed sources is selected, and a coding rule is applied based on local data, allowing for efficient data processing and inference at a receiving node, with the option to repeat the process until a predetermined criterion is met, optimizing data retrieval through a binary tree splitting or m-ary group testing approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed source coding techniques are used to reduce aggregate information rate, then data retrieval efficiency is improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the network into clusters of sensors grouped by spatial location or data correlation characteristics. Each cluster is encoded and decoded independently, breaking the complex global optimization problem into smaller manageable segments. This reduces encoding/decoding complexity while maintaining data retrieval efficiency through localized processing.
Solution Approach 2:
The patent changes parameters by adapting encoding strategies based on observed data correlation levels and network conditions. Encoding parameters such as compression ratios and clustering thresholds are dynamically adjusted according to measured sensor correlations, optimizing the balance between retrieval efficiency and processing complexity for different network states.
2Productivity
If cross-layer optimization of source coding and transmission scheduling is applied, then scaling performance is improved, but system complexity increases
Solution Approach 1:
The patent segments the cross-layer optimization into separate functional modules: source coding layer, transmission scheduling layer, and resource allocation layer. Each layer operates with defined interfaces and protocols, reducing overall system complexity while achieving scaling performance through coordinated operation of independent modules rather than monolithic optimization.
3Loss of time
If group testing strategies are used to select distributed sources, then the number of queries is reduced, but processing complexity at the receiving node increases
Solution Approach 1:
The patent merges multiple sensor readings and test outcomes into aggregated cluster-level results before final decoding. By combining information from multiple sources at intermediate processing stages rather than processing each individually, the system reduces the total number of queries needed while distributing processing complexity across multiple simpler operations rather than one complex operation.
Data Source
AI summary
Methods and systems for obtaining data from a number of distributed sources.