Abstract
In many industries, digital twinning has become an indispensable element of advanced technologies. However, digital twins are heavily reliant on extensive Internet of Things (IoT) sensor measurement data to function effectively. Consequently, data mining has become a lucrative endeavor, akin to gold rushes in the XIX century. However, the substantial volume of collected data often stresses the storage capacities for smaller to medium-sized enterprises, necessitating efficient compression techniques. Error-bound lossy compression offers substantial data reduction advantages, but introduces distortion that, when uncontrolled, can adversely affect analysis. This paper proposes an information optimization scheme that employs information entropy as a comprehensive measure of distortion across lossily reconstructed sensor data. Moreover, we present a general model-based compression framework capable of extracting the uniquely observed data features prior to compression. The results of our analysis show that the proposed framework further improves compression performance over direct compression of raw data.
Recommended Citation
S. Thompson and M. Zawodniok, "An Entropy-Bounded, General, Model-Based Framework for Lossy Compression of Sensor Data," Conference Record IEEE Instrumentation and Measurement Technology Conference, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/I2MTC62753.2025.11079003
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
data compression; data management; digital twin
International Standard Serial Number (ISSN)
1091-5281
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Comments
Boeing, Grant None