Collaborative Sensor Networks for Uncertain Source Localization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing decentralized sensor networks struggle to accurately determine the location of a physical event or object due to high uncertainty caused by factors like wind patterns and the need for improved uncertainty quantification in decision-making processes, particularly in critical applications such as search and rescue and disaster management.

Innovation Solution

A characteristic identification (CID) system that employs a network of nodes with sensors, computing components, and communication interfaces, utilizing algorithms like Black Box Variational Inference and Evidence Lower Bound to iteratively refine probability distributions of event characteristics, such as source location, based on measurements from multiple nodes, while censoring non-informative data and managing communication overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If a single sensor is used to determine source location, then device complexity is reduced, but measurement precision deteriorates due to high uncertainty from wind patterns and inability to determine direction

Engineering Contradiction:

Improvesensor network complexityVSAvoidsource location precision

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

Multiple sensor nodes are merged into a collaborative network where each node contributes measurements and probability distribution information. The nodes collectively perform source localization by combining their individual probability distributions through iterative information exchange, achieving higher measurement precision than any single node could achieve alone.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The source localization problem is segmented into individual probability distribution calculations at each sensor node, with each node independently computing its own probability distribution based on local measurements. These segmented calculations are then combined through iterative collaboration to form the overall source location estimate.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If more sensor nodes are added to improve source location accuracy, then measurement precision improves, but communication overhead and device complexity increase

Engineering Contradiction:

Improvesource location precisionVSAvoidsensor network complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Instead of requiring all sensor nodes to communicate with all other nodes, the system implements partial communication where nodes exchange information with a subset of other nodes. This partial action reduces communication overhead while still achieving convergence of probability distributions and accurate source localization across the network.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes the parameter of information representation from raw measurements to probability distribution parameters (mean and covariance). This parameter transformation compresses the information exchanged between nodes, reducing communication overhead while preserving the essential uncertainty quantification needed for accurate source localization.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If complete probability distribution information is exchanged between all nodes, then measurement precision improves, but loss of time increases due to extensive communication iterations

Engineering Contradiction:

Improvesource location precisionVSAvoidconvergence time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system transforms complete probability distribution information into compact parameter representations (mean vector and covariance matrix). This parameter change enables efficient exchange of uncertainty quantification information between nodes, reducing the time required for information exchange while maintaining measurement precision.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The probability distribution information exchange occurs in periodic iterative cycles rather than continuously. Each iteration refines the source location estimate by incorporating new information from neighboring nodes, with the process repeating until convergence criteria are met, balancing precision improvement with time efficiency.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12530595B2Identification of a characteristic of a physical system based on collaborative sensor networks

Publication Date: 2026.01.20 LAWRENCE LIVERMORE NAT SECURITY LLC

AI summary

A node having a sensor and a computing device is provided for identifying a location of a source of a physical process. The node collects via the sensor a measurement of the physical process. The node repeatedly recalculates parameters until termination criterion is satisfied. The node receives parameters of a probability distribution and a gradient from other nodes. The node generates parameters based on the parameters and the gradients received from the other nodes. The node samples from a distribution of source locations based on the generated parameters. The node calculates a gradient derived from the sampled source locations, the generated parameters, and a joint probability of the sampled source locations and the measurement. The node sends to a subset of other nodes the generated parameters and the calculated gradient. When the termination criterion is satisfied, the generated parameters represent the probability distribution of the source location.