Binary Decision Diagram Partitioning for Sensor Data Compression
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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 optimizing data storage and processing for accurate health state analysis.
Innovation Solution
The integration of sensor networks with binary decision diagrams (BDDs) for data compression, partitioning, and correlation analysis, utilizing reduced ordered binary decision diagrams (ROBDDs) to optimize data representation and processing, and employing hashing techniques for data integrity checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single large BDD is used to represent all sensor data, then data representation is unified, but the BDD size becomes unmanageably large and processing becomes inefficient
Solution Approach 1:
The patent divides the large BDD into multiple smaller sub-BDDs, each representing a specific sensor or data stream. This segmentation reduces the size of individual BDDs making them more manageable while maintaining the ability to detect errors through hashing techniques applied to each sub-BDD.
Solution Approach 2:
The patent introduces hash functions as intermediaries that operate on individual sub-BDDs. These hash values serve as compact representations that can be stored and verified separately, enabling error detection without requiring the full sub-BDD structure to be maintained in memory simultaneously.
2Reliability
If comprehensive error detection is implemented across all sensor data, then data reliability is improved, but processing overhead increases
Solution Approach 1:
The patent extracts only the essential error-detection information from each sub-BDD in the form of hash values. These compact hash representations enable comprehensive error detection across all sensor data while requiring minimal processing and storage resources compared to maintaining and verifying the full BDD structures.
Data Source
AI summary
In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing data streams from sensors, selecting portions from the first BDD, constructing a second BDD representing the selected portions and a third BDD representing the non-selected portions, determining sizes of the first, second, and third BDDs, and if the size of the first BDD is less than a sum of the sizes of the second and third BDDs, then storing the first BDD, else storing the second and third BDDs.