Binary Decision Diagrams for Sensor Data Correlation

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Solution Overview

Problem

Current sensor networks and binary decision diagrams face challenges in efficiently monitoring and analyzing health data, particularly in detecting sensor malfunctions and data corruption, and in optimizing data analysis processes for size and compression.

Innovation Solution

The integration of sensor networks with binary decision diagrams (BDDs) enables data compression, detection of sensor malfunctions, and optimization of data analysis by using reduced ordered binary decision diagrams (ROBDDs) and hashing techniques, allowing for efficient data processing and visualization of health state information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional sensor networks are used for health monitoring, then data collection capability is maintained, but data processing efficiency and storage optimization deteriorate

Engineering Contradiction:

Improvedata processing efficiencyVSAvoiddata analysis complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical data processing methods with binary decision diagrams (BDDs), a computational data structure that efficiently represents and manipulates Boolean functions. This substitution enables optimized data analysis by transforming health monitoring data into BDD representations, allowing for more efficient processing and correlation detection without requiring complex analytical systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If comprehensive health data is monitored and stored, then detection accuracy is improved, but data storage size and processing overhead increase

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoiddata storage volume

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent changes the representation parameters of health data by encoding it into binary decision diagrams. This parameter transformation allows the same information to be stored and processed in a compressed format, reducing storage volume while maintaining the capability to detect anomalies and correlations with high accuracy through efficient BDD operations.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates compressed representations (copies) of health data using BDD structures. Instead of storing raw data in its original form, the system generates equivalent BDD representations that occupy less storage space but preserve all necessary information for detection and analysis, effectively reducing storage requirements while maintaining detection capabilities.

Inventive Principle:

Principle #26Copying

3Loss of time

If real-time health monitoring is implemented, then response time to anomalies is reduced, but computational resource consumption increases

Engineering Contradiction:

Improveanomaly detection timeVSAvoidcomputational energy consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The patent replaces energy-intensive real-time computational methods with BDD-based processing, which leverages the inherent efficiency of binary decision structures. This substitution enables real-time anomaly detection by using BDD operations that require fewer computational resources, thereby reducing energy consumption while maintaining fast response times for detecting health anomalies.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Quantity of substance

If data compression is applied to optimize storage, then storage efficiency is improved, but data integrity and detection reliability may deteriorate

Engineering Contradiction:

Improvedata storage efficiencyVSAvoiddata integrity

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The patent creates faithful BDD representations as compressed copies of the original health data. These BDD copies maintain complete information equivalence with the source data, ensuring that no information is lost during compression. The structural properties of BDDs guarantee that the compressed representation preserves all necessary data integrity for reliable anomaly detection and correlation analysis.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces traditional compression algorithms with BDD-based compression, which inherently preserves data integrity through the mathematical properties of Boolean function representation. This substitution ensures that compressed data maintains full reliability for detection purposes, as BDDs provide lossless compression by exactly representing the underlying data relationships without approximation or information loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS8909592B2Combining medical binary decision diagrams to determine data correlations

Publication Date: 2014.12.09 FUJITSU LTD

AI summary

In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing a data stream from a first sensor and a second BDD representing a data stream from a second sensor, determining whether the first sensor data and the second sensor data correlate by: constructing a third BDD by performing an OR operation between the first and second BDDs, determining sizes of the first, second, and third BDDs, and comparing the third size to the sum of the first size and the second size; where the degree that the first sensor data and the second sensor data correlate is indicated by the amount that the third size is less than the sum of the first size and the second size.